Reducing the number of accidents and accident severity is the highest importance according to the EU objectives, but this in not only a European, but a global problem affecting the whole society. Worldwide every year almost 50 million people are injured and 1.2 million people die in road accidents; more than half of them being young adults aged 15 to 44. Forecasts indicate that, without substantial improvements in road safety, road accidents will be the second largest cause of healthy life years lost by 2030. (Source:[1])

In Europe there is no reality to further improve the currently existing road network, so the solution has to face the limited infrastructure and the even further growing demand of transport. That is why highly automated road vehicles (integrated into an intelligent environment) will have a key role in improving road safety. The EU road safety strategy defined in 2001 has achieved significant results, but there is still a lot to do. The following figures [2] below show the overall and country based progress achieved in saving lives on European roads.

Figure 1.1. Road fatalities in the EU since 2001 (Source: CARE)

Figure 1.2. Road fatalities by population since 2001 (Source: CARE)

Hungary has been recognised with the “2012 Road Safety PIN Award” at the 6th ETSC Road Safety PIN Conference in 2012 for the outstanding progress made in reducing road deaths. Road deaths in Hungary have been cut by 49% since 2001, helped by a 14% decrease between 2010 and 2011. Since 2004 and its accession to the EU, Hungary quickly adapted to the rigours of membership and to the challenge of the EU 2010 target. (Source: [3])

The other global challenge is to make transportation more efficient and environment friendly. Europe accelerates its progress towards a low-carbon society in order to reach the target of an 80% reduction in emissions below the 1990 levels by 2050. To be able to achieve that, the emissions should be reduced 40% by 2030 and 60% by 2040. Each sector has to contribute to achieve this, and transportation has an essential part in that. (Source: [4])

Figure 1.3. European roadmap for moving to a low-carbon economy (Source: EU)

Although more economical engines (see also downsizing technology) also results in less emission, on the other hand there has been a great improvement recently in exhaust gas handling technology thanks to the EU regulations. Since the introduction of the Euro1 standard (1993) the harmful exhaust emission level was reduced the limit for NOX by 95% and for particulates by 97% to the Euro 6 levels. Figure 4 shows European harmful exhaust emission reduction requirements. (Source: [5])

Figure 1.4. European Euro 6 emissions legislation (Source: DAF)

Highly automated driving has also a value add to efficiency and lower harmful emissions by functions like Active Green Driving (AGD), Automated Queue Assistance (AQUA), forward thinking driving with eHorizon and velocity profile planning.

The main goal of the Active Green Driving is to reduce the fuel consumption and potentially emissions in vehicles by using e-Horizon system enhanced with environment sensors and the continuous prediction of vehicle speed profile. This includes a fuel optimal powertrain strategy and a driver support interface to give driving recommendations, in certain defined situations, using both graphical and textual information and furthermore haptic feedback in the accelerator pedal. (Source:[6])

One should understand that there are some objective factors that electronics (mechatronics) has no alternative in the future of driving. We should not forget that most of the drivers on public road are not professional pilots, but even the most talented driver cannot compete with intelligent mechatronic systems in the following fields:

The electronically networked driver assistance systems will have key factor in the increase of safety on roads. According to statistics only 3% of traffic accidents can be traced back to technical reasons (vehicle), while 97% of accidents are caused by human factors (driver). Beside the inadequate technical status of a certain vehicle, the major sources of vehicle accidents are wrong driver decision, inappropriate evaluation of the circumstances, or lack of consideration. The probability of wrong decisions radically increases if the driver is in bad physical or mental condition, for example tired or from some other kind of reason indisposed.

According to European accident statistics 2/3 of the accidents with serious outcome could have been avoidable by the usage of driver assistance systems. A considerable part of these accidents are pile-up accidents, from which more than half can be traced back to lack of attention. Beyond the pile-up, accidents where commercial vehicles are involved are typically caused by drifting out, jack-knifing or rolling over.

Figure 1.5. The effect of collision speed on fatality risk (Source: UNECE)

In case of losing vehicle stability, unintentional lane departure, pile-up happening from behind, there are technical solutions to warn the driver before a late recognition and - if it is not enough - assistance systems automatically intervene in the management of the vehicle, with which the consequences of the accident can be radically mitigated. Such intervention may be a last minute emergency braking, which may significantly reduce the kinetic energy of the vehicle before the collision. Driver assistance systems may also compensate for eventual erroneous reactions of the driver.

The very first level of providing assistance to the driver is to inform the driver about potentially dangerous situations. In this stage there is no further support or intervention, the driver is just warned about the potential danger. The warning information is transmitted to the driver via the primary human machine interface (HMI) of the vehicle, using either visible (e.g. dashboard), acoustic (e.g. alarm sound) or haptic (e.g. seat or steering wheel vibration) feedback. It is totally up to the driver if he takes actions or not after receiving the warning. A good example is driver drowsiness detection which warns the driver to take a coffee break in case of fatigue by continuously monitoring the steering wheel movement. The warning message is an icon of a coffee cup in front of an orange background and there is a text message ”please take a break” shown in the figure below on the display of the HAVEit HMI.

Figure 1.8. Driver drowsiness warning: Time to take a coffee break! (Source: HAVEit)

The second level of providing assistance is not just to warn the driver but support him guiding how to drive the car in a potentially hazardous situation. This level assumes that the driver is aware of the probably unsafe situation (warning) and there are hints provided further more to the driver directing him to the right manoeuvre.

Lane Departure Warning is a basic function inside supported control. It warns the driver if he gets too close to the lane markings and then warns the driver by acoustic and visual warnings or a slight vibration in the steering wheel.

Figure 1.9. LDW support guiding the driver back into the centre of the lane. (Source: Mercedes-Benz)

Additional support may also be providing a counter-steering torque with the electrically power assisted steering system (EPAS or EPS). The gently applied EPAS steering torque makes it harder to steer out (change lane without using the turn signal). The driver may still decide to change lane (without using the turn signal) and steer out from the lane by a stronger steering wheel movement or stay in the lane by gently steering back.

Figure 1.10. Counter-steering torque provided to support keeping the lane. (Source: TRW)

The function works camera based by analysing the lane markings on the road in front and by comparing the current vehicle direction in relation to them. It usually incorporates hands-off detection of the steering wheel to avoid the use (misuse) of the system for autonomous driving.

Figure 1.11. Warning for misuse of lane assist for autonomous driving. (Source: Audi)

Another not safety but typical comfort function example is the parking assistant system which can cover the support level and furthermore the intervention level. Into the support level three functions can be enrolled: parking space measurement, parking aid information and park steering information.

Figure 1.12. Parking space measurement (Source: Bosch)

The parking aid information system monitors the close-up area in front and/or behind the vehicle by the ultrasonic sensors and informs the driver with an optical and/or acoustical signal of how close the vehicle is to an obstacle.

Figure 1.13. Principle of the operation of the parking aid systems (Source: Bosch)

When the parking space measurement is active the ultrasonic sensors are scanning the roadside. As soon as the system identifies a suitable parking space long enough for the car, the driver will be immediately informed. The driver can activate the parking assistant with a push of button and initiate the parking manoeuvre.

The park steering information system provides clear instructions on steering-wheel position and the necessary stop and switching points through a display, thus guiding the driver into the perfect parking position.

The next level of driver assistance is the automated intervention into the vehicle control. Depending on the intervention scale there are categories of semi-automated driving and highly automated driving. While during semi-automated driving the vehicle’s longitudinal movement is under automated control, as during highly automated driving both longitudinal and lateral control takes part of the vehicle motion.

1.3.3.1. Semi-automated intervention

Basically semi-automated driving can be described as supported driving plus longitudinal control of vehicle movement. The market currently offers several functions in this segment like adaptive cruise control (ACC) with Stop & Go extension or Emergency Brake Assist. This function uses a radar sensor to monitor the traffic ahead adjusting vehicle speed and keeping safe distance to the vehicle in front via the throttle-by-wire and brake-by-wire intelligent actuators. While no front vehicle is detected speed control is active keeping constant vehicle speed regardless of the road inclination, but when a vehicle is detected ahead then distance control becomes active, keeping speed-dependant safe distance between the two vehicles. The following figure shows the moment when distance control becomes active.

Figure 1.14. ACC distance control function for commercial vehicles (Source: Knorr-Bremse)

On top of ACC function there may be a collision avoidance (mitigation) option. Based on the radar signal (and video sensors), the system warns the driver if there is a collision hazard, and in case the collision is inevitable for the driver the system automatically applies the brakes to reduce vehicle speed, thus collision severity. Such function physically mitigates the collision consequences by reducing collision speed.

Figure 1.15. Steps of collision mitigation with an ACC System (Source: Toyota)

1.3.3.2. Highly automated intervention

The former levels of automated driving all had operational examples on the market, whereas highly-automated driving represents the state-of-the-art of road vehicle automation that soon (2016-2020) will be introduced to the public domain with function like Temporary Auto Pilot, Automated support for Roadwork and Congestion.

High automation means integrated longitudinal and lateral control of the vehicle enabling not only acceleration and braking for the vehicle automatically, but also changing lane and overtaking. Such functions will be introduced initially on motorways since they are the most suitable roads for automated driving; the road path has only smooth alterations (curves or slopes), they are highlighted by easily recognizable lane markings, protected by side barriers, and last but not least there is only one-way traffic. Potential use-cases are monotonous driving situations like traffic jams or long distance travel in slight traffic.

Temporary Auto Pilot function of Volkswagen [7] (developed within the EU funded HAVEit project) offers highly-automated driving on motorways with a maximum speed of 130 kilometres per hour. TAP provides an optimal degree of automation as a function of the driving situation, perception of the surroundings, the driver and the vehicle status. TAP maintains a safe distance to the vehicle ahead, drives at a speed selected by the driver, reduces this speed as necessary before a bend, and keeps the vehicle in the centre of the lane by detecting lane markings.

Figure 1.16. Temporary Auto Pilot in action at 130 km/h speed (Source: Volkswagen)

The responsibility always stays by driver even he is temporarily gets out of the control loop. He acts as an observer when TAP is active and can take back the vehicle control from TAP any time in safety-critical situations. The vehicle can also give back control to the driver if the preconditions for enabling TAP are not met. The driver is continuously monitored for drowsiness and attention by the vehicle to prevent accidents due to driving errors by an inattentive, distracted driver. The system also obeys overtaking rules and speed limits. Stop and start driving manoeuvres - for example in traffic jams - are also automated.

From time-to-market point of view a definitive advantage of the TAP system is that it has been realized on a relatively production-like sensors, consisting of production-level radar-, camera-, and ultrasonic-based sensors enhanced by a laser scanner and an electronic horizon. (Source: Volkswagen)

BMW demonstrated in 2009 that based on sensor fusion of eHorizon, GPS and samara information a vehicle can autonomously drive around a closed race circuit, following the ideal path. After the impressive performance BMW decided to introduce highly automated driving functions in series production vehicles until 2020. Aside typical functions like braking, accelerating and overtaking other vehicles entirely autonomously, the system will focus on challenges, like motorway intersections, toll stations, road works and national borders. The research prototype vehicle has run approximately 10,000 test kilometres up to now on public road with no driver intervention. (Source: [8])

Figure 1.17. Highly automated driving on motorways (Source: BMW)

Highly automated driving can also help in case of emergency, for example if biosensors detect that the driver is having a heart attack. The Emergency Stop Assistant function is able to switch the vehicle to highly automated driving mode and taking into account the traffic situation, manoeuvres the vehicle in a safe and controlled way to a standstill on the roadside. By switching on the hazard warning lights and making an eCall to request medical assistance, the situation is handled with maximum efficiently. (Source: [8])

Toyota has presented its AHDA [9] test vehicle in 2013 that incorporates highly automated driving technologies to support safer highway driving and reduce driver workload. AHDA combines two automated driving technologies together Cooperative-adaptive Cruise Control and Lane Trace Control. The vehicle is fitted with cameras to detect traffic signals, radars and laser-scanner to detect vehicles, pedestrians, and obstacles in the surroundings. Via sensor fusion the vehicle is also able to identify traffic conditions, like intersections and merging traffic lanes.  

Figure 1.18. Test vehicle with automated highway driving assist function (Source: Toyota)

In addition to radar based adaptive cruise control (ACC), Cooperative-adaptive Cruise Control (CCC) uses wireless communication between the vehicles to exchange vehicle dynamics data that enables smoother and more efficient control for maintaining a safe distance. Cooperative-adaptive Cruise Control uses 700-MHz band vehicle-to-vehicle ITS communications (V2V) to broadcast acceleration and deceleration data of the leading vehicle so that the following vehicles can control their speed profile correspondingly to better maintain inter-vehicle distance. Eliminating unnecessary accelerations and decelerations for the following vehicles results in better fuel efficiency and avoiding traffic congestion.

Lane Trace Control is a new assistance function that aids steering to keep the vehicle on an optimal driving path within the lane. It uses high-performance cameras, radar and sensor fusion to determine the optimal road path and provide smooth driving for the vehicle at all speeds. The system automatically intervenes in the steering, the drivetrain and the braking system when necessary to maintain the optimal path within the lane.

The objective of the automated assistance in road works and congestion function (developed within the EU funded HAVEit project) is to support the driver in overload situations like driving in narrow lanes of roadwork areas on motorways with lot of vehicles driving closely beside. Entering the road works and driving for longer distance in the road works area is very challenging for the driver. Some drivers even feel fear while other vehicles are driving so close in the parallel lane.

An integrated approach of the lateral and longitudinal control is used to adapt the speed and the lateral position of the vehicle to run the optimal path. (This assistance function will work at speeds between 0 and 80 km/h.) When the pre-conditions for the Automated Roadwork Assistance function are met the driver can switch to highly automated driving mode, taking his hands off the steering wheel. By this, the driver could drive through the road construction, without using his hands or feet. This guarantees that the driver gets the best possible support available, in particular with respect to lateral vehicle control.

Figure 1.19. Demonstrating automated roadwork assistance functionality. (Source: HAVEit)

Another step in automation is the concept of platooning, which was motivated by intelligent highway systems and road infrastructure, see the PATH program in California and the MOC-ITS program in Japan. The European programmes were based on the existing road networks and infrastructure and focused mainly on commercial vehicles with their existing sensors and actuators. In a Hungarian project an automated vehicle platoon of heavy vehicles was developed. In a platoon system 5 to 10 vehicles are organized. The intervehicle spacing is small and constant at all speeds and vehicles. A well-organized platoon control may have advantages in terms of increasing highway capacity and decreasing fuel consumption and emissions. The control objective in platooning is to maintain vehicle following within the platoon and platoon stability under the constraint of comfortable ride. Since the desired intervehicle spacing is very small, the allowable position error is also small, which implies very accurate tracking of the desired spacing and speed trajectories. This accuracy puts constraints on the performance of the sensors and actuators as well as on the controller bandwidth.

Figure 1.20. Demonstration of a platoon control

A three-layer software architecture that moves from discrete to continuous signals was in the PATH project, see Figure 20 and Figure 21. It should be noted that our design is not necessarily unique or optimal, but as a preliminary approach is sufficient to prove our point.

• The stability and control layer deals with continuous signals and interfaces directly with the platform hardware. It contains several dynamic-positioning algorithms, a thruster allocation scheme, and sensor data processing and monitoring for fault detection. Control laws are given as vehicle state or observation feedback policies for controlling the vehicle dynamics. The corresponding events are sent to the manoeuvre coordination layer.

• In the manoeuvre coordination layer control and observation subsystems responsible for safe execution of atomic manoeuvres such as assemble, split, wind tracking, and go to a location. Manoeuvres may include several modes according to the lattice of preferred operating modes. Mode changes are triggered by events generated by the stability layer monitors. It also monitors incidents and reacts to minimize their impact on manoeuvres and maximize safety.

• The supervisory control layer: control strategies that the modules follow in order to minimize fuel consumption and maximize safety and efficiency. Discrete commands are given to achieve high-level goals of overall coordination and manoeuvring of the mobile offshore bases. This layer monitors the evolution of the system with respect to global mission goals. It receives commands and translates them into specific manoeuvres that the platforms need to carry out.

Figure 1.21. Hierarchical structure applied in the PATH project

Until the driver has to be (legally) responsible for the behaviour of the vehicle, the driver will always be an integral part of the control loop. That is why full automation of public road vehicles is not realistic in the near future. There are some promising experiments on fully automated manoeuvring of vehicles but most of them are realized on a separated area and are far from being able to adapt on public roads without an intelligent, independent road infrastructure.

Distinguished members of IEEE, the world’s largest professional organization dedicated to advancing technology for humanity, have selected autonomous vehicles as the most promising form of intelligent transportation, anticipating that they will account for up to 75 % of cars on the road by the year 2040, see [10].

The U.S. government also supports the autonomous car research, as keeping it the next evolutionary step in technology. In Nevada, Florida and California it is licensed to test vehicles without a driver, which is used by Google as getting the first self-driven car license in Nevada in 2012. (Source: [11])

Figure 1.22. Google’s self-driving test car, a modified hybrid Toyota Prius (Source: http://www.motortrend.com)

Google gathered the best engineers from the DARPA Challenge series, which was a legendary competition for American autonomous vehicles. It was started in 2004, funded by the Defense Advanced Research Projects Agency, which is a research institute of United States Department of Defense. Three events were held in 2004, 2005 and 2007 regarding to the autonomous passenger cars. The 2012 event has focused on autonomous emergency-maintenance robots.

Google’s self-driving car use video cameras, radar sensors and a LIDAR (see Section 3) for environment sensing, as well as detailed maps for navigation. The car is supported by the Google’s data centres, which can process the information gathered by the test cars when mapping the terrain. (Source: [12])

The main vehicle manufacturers are also heading toward the autonomous solutions, but they are trying to reach the goal step-by-step, and not at once like Google. They are developing (in cooperation with their OEMs) such subsystems which can increase the automation level of the car, and also can be marketed in production vehicles as new driver assistance systems. An example of this attitude can be found in an interview with Daimler head of development Thomas Weber, who said "Autonomous driving will not come overnight, but will be realized in stages". The plans to start selling self-driving cars is about 2020, primarily in North America. (Source: [13])

The bottom level, the “Electronic System Platform“ solutions contain all the necessary building elements that are required for building up an electronically controlled vehicle system. This includes basic electronic and mechatronic hardware components like sensors and actuators, includes ECUs, but also software components like operating systems, diagnostics software, peripheral drivers and so on. The platform characteristics lies behind the fact that these individual components, building block can be used widespread in different makes and vehicle types, resulting in high production volume, thus reliable design and low costs.

The second level, the “Intelligent Actuators“ are the electronically controllable separate five main units of the vehicle drivetrain, namely the engine, transmission, suspension, brake system and the steering system. These main units are individual intelligences meaning that each unit has its own ECU with sophisticated functionality for electronic control in itself having communication links (CAN, FlexRay) to other ECUs for interaction, but one ECU is only responsible for the control of one unit. Typical characteristics of the intelligent actuators are having a dedicated interface for the electronic control of the whole unit. This is why they are often called as „by-wire“ systems, meaning that there is no mechanical connection for the control. In this case these units are referred as drive-by-wire, shift-by-wire, suspension-by-wire, brake-by-wire abs last but not least steer-by-wire systems in a vehicle. These actuators are “must have” components for the intelligent vehicle control.

In the next level, in the “Integrated Vehicle Control” there is a harmonized control of the intelligent actuators (instead of individual control) on a vehicle level. This layer has an influence on the whole vehicle motion. Maybe it is not trivial, but it is easy to understand what synergies become available in cases of harmonized or in other words integrated control of the intelligent actuators. The most obvious example is the integrated control of the brake and steering system, where the harmonized control could result in a significantly shorter brake distance. Let us imagine a so called u-split situation, where the road surface is divided into two parts based on a longitudinal separation. In this case the vehicles left hand side wheels (front left, rear left) are running on a hi-u surface e.g. u=0,8, while the right hand side wheels (front right, rear right) are running on a low-u surface e.g. u=0,1. In this case there is a great potential for synergy when the brake system realizes the u-split situation and instead of using “select-low” strategy – meaning that the lower u surface determines the maximum brake force on each sides – uses the maximum allowable braking force on each side with the help of the steering system providing compensating yaw torque to stabilize the vehicle direction.

The fourth levels called “Direct V2V, V2I Interactions” where ad-hoc direct vehicle-to-vehicle (V2V) communications and ad-hoc direct vehicle-to-infrastructure (V2I) communications support the vehicle and its driver to be able to carry out the control task, safely and economically. The control is based on the relative position of the other vehicles or the elements of the infrastructure. The simplest example of these interactions is the Adaptive Cruise Control (ACC) function, where the distance and the velocity of the following vehicle is determined by a radar system, establishing ad-hoc direct V2V connection between the ego and the following vehicle. The ACC function is (currently) limited to the longitudinal direction, but such communication interactions can be established also in the lateral direction for e.g. automated trajectory planning. A currently market available example for ad-hoc direct V2I interaction is the automatic traffic sign recognition function.

Last but not least there is the fifth level with “Control of Vehicle Groups and Fleets”. This layer covers the control of a whole vehicle flow or a partial set of the vehicles, where the grouping is based on either the current location or on the vehicle ownership (fleets).Good example for the location based selection of the vehicle groups is the control of a road cross section or the so called platooning, where an ad-hoc virtual road train is formulated on a highway. Transportation fleets are generally controlled via a centralized informatics system, where current market availability is rather logistics, delivery control and vehicle operation related information acquisition (FMS, Telematics) than remote movement control of the vehicles.

The above described structuring of the intelligent vehicle control is rather system oriented while the following part will mostly concentrate on the internal control structure of a vehicle.

The command layer contains the high level algorithms for the longitudinal and the lateral control of the automated vehicle. The command layer calculates the desired acceleration and direction and communicates the results to the powertrain via the powertrain interface, which also provides feedback about the vehicle state for the high level control.

Based on the driver intention and the information coming from the perception layer the command layer defines the vehicle automation level and calculates the vehicle trajectory. The objective of the perception layer is to collect information about the external environment and the vehicle, thus providing information about vehicle status and objects in the surrounding environment. From this information the command layer determines the obtainable levels of vehicle automation and displays the options to the driver. Meanwhile the co-pilot calculates possible vehicle trajectories and prioritizes them based on the accident risk. The driver selects the desired level of vehicle automation from the available options via the HMI. Finally the mode selection unit decides the level of automation and selects the trajectory to be executed.

The motion vector acts as an interface between the command layer and the execution layer. It is bidirectional, delivering longitudinal and lateral control commands to the powertrain and providing vehicle status feedback information for the higher level control.

This middle layer contains predefined data transfer for the control and the feedback of the powertrain. It includes commands for the vehicle control including the desired status of the powertrain and the required acceleration and torque. This interface also includes status feedback from the powertrain to the application layer providing important information whether the control action resulted in the required movement.

The execution layer contains a full drivetrain control connected to intelligent actuators via a high speed communication network. As the implementation of a fully electronic interface (motion vector) for controlling the powertrain enables the replacement of the human driver for an electronic intelligence (auto-pilot), the execution layer cannot distinguish whether the commands are originated from a human driver or an auto-pilot. The commands are coming through the same interface, so the execution layer has to execute only. Considering that there are safety critical subsystems can be found among the intelligent actuators a fault-tolerant architecture is a prerequisite. This fault tolerant architecture not only includes duplicated powertrain controllers, steering and braking systems but also a redundant communication network, power supply and HMI to the driver. The following figure shows the powertrain control structure of the execution layer in the PEIT demonstrator vehicle[25].

Figure 2.5. Powertrain Control Structure of the execution layer (Source: PEIT)

As defined in [33] there are eight attributes that characterize image sensor performance. This attributes will be explained in details in the following paragraphs:

Responsivity, the amount of signal the sensor delivers per unit of input optical energy. CMOS imagers are marginally superior to CCDs, in general, because gain elements are easier to place on a CMOS image sensor. Their complementary transistors allow low-power high-gain amplifiers, whereas CCD amplification usually comes at a significant power penalty. Some CCD manufacturers are challenging this concept with new readout amplifier techniques.

Dynamic range, the ratio of a pixel’s saturation level to its signal threshold. It gives CCDs an advantage by about a factor of two in comparable circumstances. CCDs still enjoy significant noise advantages over CMOS imagers because of quieter sensor substrates (less on-chip circuitry), inherent tolerance to bus capacitance variations and common output amplifiers with transistor geometries that can be easily adapted for minimal noise. Externally coddling the image sensor through cooling, better optics, more resolution or adapted off-chip electronics cannot make CMOS sensors equivalent to CCDs in this regard.

Uniformity, the consistency of response for different pixels under identical illumination conditions. Ideally, behaviour would be uniform, but spatial wafer processing variations, particulate defects and amplifier variations create non-uniformities. It is important to make a distinction between uniformity under illumination and uniformity at or near dark. CMOS imagers were traditionally much worse under both regimes. Each pixel had an open-loop output amplifier, and the offset and gain of each amplifier varied considerably because of wafer processing variations, making both dark and illuminated non-uniformities worse than those in CCDs. Some people predicted that this would defeat CMOS imagers as device geometries shrank and variances increased. However, feedback-based amplifier structures can trade off gain for greater uniformity under illumination. The amplifiers have made the illuminated uniformity of some CMOS imagers closer to that of CCDs, sustainable as geometries shrink. Still lacking, though, is offset variation of CMOS amplifiers, which manifests itself as non-uniformity in darkness. While CMOS imager manufacturers have invested considerable effort in suppressing dark non-uniformity, it is still generally worse than that of CCDs. This is a significant issue in high-speed applications, where limited signal levels mean that dark non-uniformities contribute significantly to overall image degradation.

Shuttering, the ability to start and stop exposure arbitrarily. It is a standard feature of virtually all consumer and most industrial CCDs, especially interline transfer devices, and is particularly important in machine vision applications. CCDs can deliver superior electronic shuttering, with little fill-factor compromise, even in small-pixel image sensors. Implementing uniform electronic shuttering in CMOS imagers requires a number of transistors in each pixel. In line-scan CMOS imagers, electronic shuttering does not compromise fill factor because shutter transistors can be placed adjacent to the active area of each pixel. In area scan (matrix) imagers, uniform electronic shuttering comes at the expense of fill factor because the opaque shutter transistors must be placed in what would otherwise be an optically sensitive area of each pixel. CMOS matrix sensor designers have dealt with this challenge in two ways. A non-uniform shutter, called a rolling shutter, exposes different lines of an array at different times. It reduces the number of in-pixel transistors, improving fill factor. This is sometimes acceptable for consumer imaging, but in higher-performance applications, object motion manifests as a distorted image. A uniform synchronous shutter, sometimes called a non-rolling shutter, exposes all pixels of the array at the same time. Object motion stops with no distortion, but this approach consumes pixel area because it requires extra transistors in each pixel. Developers must choose between low fill factor and small pixels on a small, less-expensive image sensor, or large pixels with much higher fill factor on a larger, more costly image sensor.

Speed, an area in which CMOS arguably has the advantage over CCDs because all camera functions can be placed on the image sensor. With one die, signal and power trace distances can be shorter, with less inductance, capacitance and propagation delays. To date, though, CMOS imagers have established only modest advantages in this regard, largely because of early focus on consumer applications that do not demand notably high speeds compared with the CCD’s industrial, scientific and medical applications.

One unique capability (called windowing) of CMOS technology is the ability to read out a portion of the image sensor. This allows elevated frame or line rates for small regions of interest. This is an enabling capability for CMOS imagers in some applications, such as high-temporal-precision object tracking in a sub-region of an image. CCDs generally have limited abilities in windowing.

Anti-blooming, the ability to gracefully drain localized overexposure without compromising the rest of the image in the sensor. CMOS generally has natural blooming immunity. CCDs, on the other hand, require specific engineering to achieve this capability. Many CCDs that have been developed for consumer applications do, but those developed for scientific applications generally do not.

CMOS imagers have a clear edge in regard of biasing and clocking. They generally operate with a single bias voltage and clock level. Nonstandard biases are generated on-chip with charge pump circuitry isolated from the user unless there is some noise leakage. CCDs typically require a few higher-voltage biases, but clocking has been simplified in modern devices that operate with low-voltage clocks.

Both image chip types are equally reliable in most consumer and industrial applications. In ultra-rugged environments, CMOS imagers have an advantage because all circuit functions can be placed on a single integrated circuit chip, minimizing leads and solder joints, which are leading causes of circuit failures in extremely harsh environments. CMOS image sensors also can be much more highly integrated than CCD devices. Timing generation, signal processing, analogue-to-digital conversion, interface and other functions can all be put on the imager chip. This means that a CMOS-based camera can be significantly smaller than a comparable CCD camera.

The image sensors only measure the brightness of each pixel. In colour cameras a colour filter array (CFA) is positioned on top of the sensor to capture the red, green, and blue components of light falling onto it. As a result, each pixel measures only one primary colour, while the other two colours are estimated based on the surrounding pixels via software. These approximations reduce image sharpness. However, as the number of pixels in current sensors increases, the sharpness reduction becomes less visible.

Figure 3.12. Principle of colour imaging with Bayer filter mosaic (Source: http://en.wikipedia.org/wiki/File:Bayer_pattern_on_sensor_profile.svg)

The most commonly used CFA is the Bayer filter mosaic as shown on Figure 54. The filter pattern is 50% green, 25% red and 25% blue. It should be noted that both various modifications of colours and arrangement and completely different technologies are available, such as colour co-site sampling or the Foveon X3 sensor.

The Navigation Signal Timing and Ranging Global Positioning System (NAVSTAR GPS, in short GPS) is a U.S. government owned utility that provides users with positioning, navigation, and timing (PNT) services. The following summary of the operating principles is based on [41].

This system consists of three segments: the space segment, the control segment, and the user segment. The U.S. Air Force develops, maintains, and operates the space and control segments.

The space segment consists of a constellation of satellites transmitting radio signals to users. The United States is committed to maintaining the availability of at least 24 operational GPS satellites, 95% of the time. To ensure this commitment, the Air Force has been flying 31 operational GPS satellites for the past few years. The extra satellites may increase GPS performance but are not considered part of the core constellation. GPS satellites fly in medium Earth orbit (MEO) at an altitude of approximately 20,200 km. Each satellite circles the Earth twice a day. The satellites in the GPS constellation are arranged into six equally-spaced orbital planes surrounding the Earth. Each plane contains four "slots" occupied by baseline satellites. This 24-slot arrangement ensures users can view at least four satellites from virtually any point on the planet.

The control segment consists of a global network of ground facilities that track the GPS satellites, monitor their transmissions, perform analyses, and send commands and data to the constellation. The current operational control segment includes a master control station, an alternate master control station, 12 command and control antennas, and 16 monitoring sites.

The user segment consists of the GPS receiver equipment, which receives the signals from the GPS satellites and uses the transmitted information to calculate the user’s three dimensional position and time.

The GPS satellites broadcast radio signals providing their locations, status, and precise time (t1) from on-board atomic clocks.

The principle of the operation is based on distance measurement. The GPS satellites broadcast radio signals providing their locations, status, and precise time from on-board atomic clocks. The GPS radio signals travel through space at the speed of light. A GPS device receives the radio signals, noting their exact time of arrival, and uses these to calculate its distance from each satellite in view. To calculate its distance from a satellite, a GPS device applies this formula to the satellite's signal:

distance = rate x time

where rate is the speed of light and time is how long the signal travelled through space

(This requires quite precise time measurement, since 1 us time deviation would result in 300m distance deviation.) The signal's travel time is the difference between the time broadcast by the satellite and the time the signal is received.

Figure 3.24. Position calculation method based on 3 satellite data. (Source: http://www.e-education.psu.edu)

If distances from three satellites are known, the receiver's position must be one of two points at the intersection of three spherical ranges (see Figure 54). GPS receivers are usually smart enough to choose the location nearest to the Earth's surface. At a minimum, three satellites are required for a two-dimensional (horizontal) fix if our receiver’s clock would be perfect. In this theoretical case all our satellite ranges would intersect at a single point (which is our position). But with imperfect clocks, a fourth measurement, done as a cross-check, will not intersect with the first three. Since any offset from universal time will affect all of our measurements, the receiver looks for a single correction factor that it can subtract from all its timing measurements that would cause them all to intersect at a single point. That correction brings the receiver's clock back into sync with universal time. Once it has that correction it applies to all the rest of its measurements and now we've got precise positioning. One consequence of this principle is that any decent GPS receiver will need to have at least four channels so that it can make the four measurements simultaneously.

The modernization program of the GPS was started in May 2000 with turning off the GPS Selective Availability (SA) feature. SA was an intentional degradation of civilian GPS accuracy, implemented on a global basis through the GPS satellites. During the 1990s, civil GPS readings could be incorrect by as much as a football field (100 meters). On the day SA was deactivated, civil GPS accuracy improved tenfold, benefiting civil and commercial users worldwide. In 2007, the government announced plans to permanently eliminate SA by building the GPS III satellites without it. The GPS modernization program involves a series of consecutive satellite acquisitions. It also involves improvements to the GPS control segment, including the Architecture Evolution Plan (AEP) and the Next Generation Operational Control System (OCX). The main goal is to improve GPS performance using new civilian and military signals. In addition GPS modernization is introducing modern technologies throughout the space and control segments that will enhance overall performance. For example, legacy computers and communications systems are being replaced with a network-centric architecture, allowing more frequent and precise satellite commands that will improve accuracy for everyone.

From the point of view of the transportation the Second and the Third Civil Signals (L2C and L5) will be the most significant changes in the modernized GPS system. L2C is designed specifically to meet commercial needs. When combined with L1 C/A in a dual-frequency receiver, L2C enables ionospheric correction, a technique that boosts accuracy. Civilians with dual-frequency GPS receivers enjoy the same accuracy as the military (or better). For professional users with existing dual-frequency operations, L2C delivers faster signal acquisition, enhanced reliability, and greater operating range. L2C broadcasts at a higher effective power than the legacy L1 C/A signal, making it easier to receive under trees and even indoors.

L5 is designed to meet demanding requirements for safety-of-life transportation and other high-performance applications. L5 is broadcast in a radio band reserved exclusively for aviation safety services. It features higher power, greater bandwidth, and an advanced signal design. Future aircraft will use L5 in combination with L1 C/A to improve accuracy (via ionospheric correction) and robustness (via signal redundancy). In addition to enhancing safety, L5 use will increase capacity and fuel efficiency within U.S. airspace, railroads, waterways, and highways.

Both signals are in launching phase. The L2C will be available on 24 GPS satellites around 2018 and the L5 around 2021.

GLONASS (Globalnaya Navigatsionnaya Sputnikovaya Sistema or Global Navigation Satellite System) is a GNSS operated by the Russian Aerospace Defence Forces. Development of GLONASS began in 1976, with a goal of global coverage by 1991 [42]. Beginning on 12 October 1982, numerous rocket launches added satellites to the system until the constellation was completed in 1995. GLONASS was developed to provide real-time position and velocity determination, initially for use by the Soviet military in navigating and ballistic missile targeting. With the collapse of the Russian economy GLONASS rapidly degraded, mainly due to the relatively short design life-time of the GLONASS satellites. Beginning in 2001, Russia committed to restoring the system, and in recent years has diversified, introducing the Indian government as a partner. This plan calls for 18 operational satellites in orbit by 2008 and 24 satellites (21 operational and 3 on-orbit spares deployed in three orbital planes) in place by 2009; and a level of performance matching that of the U.S Global Positioning System by 2011. Finally by 2010, GLONASS has achieved 100% coverage of Russia's territory and in October 2011, the full orbital constellation of 24 satellites was restored, enabling full global coverage. It both complements and provides an alternative to the United States' Global Positioning System (GPS) and is the only alternative navigational system in operation with global coverage and of comparable precision.

At present GLONASS provides real time position and velocity determination for military and civilian users. The fully operational GLONASS constellation consists of 24 satellites, with 21 used for transmitting signals and three for in-orbit spares, deployed in three orbital planes. The three orbital planes' ascending nodes are separated by 120° with each plane containing eight equally spaced satellites. The orbits are roughly circular, with an inclination of about 64.8°, and orbit the Earth at an altitude of 19,100 km, which yields an orbital period of approximately 11 hours, 15 minutes. The planes themselves have a latitude displacement of 15°, which results in the satellites crossing the equator one at a time, instead of three at once. The overall arrangement is such that, if the constellation is fully populated, a minimum of 5 satellites are in view from any given point at any given time. This guarantees for continuous and global navigation for users world-wide.

Galileo is Europe’s own global navigation satellite system, providing a highly accurate, guaranteed global positioning service under civilian control, which is introduced from [43].

By offering dual frequencies as standard, Galileo will deliver real-time positioning accuracy down to the metre range. It will guarantee availability of the service under all but the most extreme circumstances and will inform users within seconds of any satellite failure, making it suitable for safety-critical applications such as guiding cars, running trains and landing aircraft. On 21 October 2011 came the first two of four operational satellites designed to validate the Galileo concept in both space and on Earth. Two more followed on 12 October 2012. This In-Orbit Validation (IOV) phase is now being followed by additional satellite launches to reach Initial Operational Capability (IOC) by mid-decade.

Galileo services will come with quality and integrity guarantees, which mark the key difference of this first complete civil positioning system from the military systems that have come before. A range of services will be extended as the system is built up from IOC to reach the Full Operational Capability (FOC) by this decade’s end. The fully deployed Galileo system consists of 30 satellites (27 operational + 3 active spares), positioned in three circular Medium Earth Orbit (MEO) planes at 23 222 km altitude above the Earth, and at an inclination of the orbital planes of 56 degrees to the equator.

The four operational satellites launched so far - the basic minimum for satellite navigation in principle - serve to validate the Galileo concept with both segments: space and related ground infrastructure. On March 12th, 2013, a first positioning test based on the 4 first operational Galileo satellites was conducted by ESA. This first position fix returned an accuracy range of +/- 10 meters, which is considered as very good, considering that only 4 satellites (out of the total constellation of 30) are already deployed. The Open Service, Search and Rescue and Public Regulated Service will be available with initial performances soon. Then as the constellation is built-up beyond that, new services will be tested and made available to reach Full Operational Capability (FOC). Once this is achieved, the Galileo navigation signals will provide good coverage even at latitudes up to 75 degrees north, which corresponds to Norway's North Cape - the most northerly tip of Europe - and beyond. The large number of satellites together with the carefully-optimised constellation design, plus the availability of the three active spare satellites, will ensure that the loss of one satellite has no discernible effect on the user.

The BeiDou Navigation Satellite System (BDS) is a Chinese satellite navigation system. It consists of two separate satellite constellations – a limited test system that has been operating since 2000, and a full-scale global navigation system that is currently under construction.

The first BeiDou system, officially called the BeiDou Satellite Navigation Experimental System and also known as BeiDou-1, consists of three satellites and offers limited coverage and applications. It has been offering navigation services, mainly for customers in China and neighbouring regions, since 2000.

Figure 3.25. Long March rocket head for launching Compass G4 satellite (Source: http://www.beidou.gov.cn)

The second generation of the system officially called the BeiDou Satellite Navigation System (BDSNS) and also known as COMPASS or BeiDou-2, will be a global satellite navigation system. According to the plans the full constellation will consist of 35 satellites. Since December 2012 there are 10 satellites providing local services to the Asia-Pacific area and it is planned to cover all over the world serving global customers before 2020. [44]

Figure 3.26. BeiDou 2nd Deployment Step(Source: http://gpsworld.com/the-system-vistas-from-the-summit/)

The COMPASS Phase II has successfully established as planned. On December 27, 2013, the first anniversary of BeiDou Navigation Satellite System providing full operational regional service was held in Beijing. At the meeting, China Satellite Navigation System Management Office Director Ran Chengqi announced the BeiDou Navigation Satellite System Public Service Performance Standard.

The result comparison with GPS created by John Lavrakas (Advanced Research Corporation) [45] shows mixed results. In some cases, the commitments from BeiDou were stronger (URE accuracy, the table to show green for the GNSS service committing to a more stringent standard over the other vertical position), and in other cases the commitments from GPS were stronger (continuity of service, advance notice of outages).

Figure 3.27. Comparison of BeiDou with GPS(Source: http://gpsworld.com/china-releases-public-service-performance-standard-for-beidou/)

Differential correction techniques are used to enhance the quality of location data gathered using global positioning system (GPS) receivers. Differential correction can be applied either in real-time (directly in the field, during measurement) or when post-processing data in the office (later during data evaluation). Although both methods are based on the same underlying principles, each accesses different data sources and achieves different levels of accuracy. Combining both methods provides flexibility during data collection and improves data integrity.

The underlying assumption of differential GPS (DGPS) technology is that any two receivers that are relatively close together will experience similar atmospheric errors. DGPS requires that one GPS receiver must be set up on a precisely known location. This GPS receiver is the base or reference station. The base station receiver calculates its position based on satellite signals and compares this location to the already known precise location resulting in a difference. This difference then is applied to the GPS data recorded by the second GPS receiver, which is known as the roving receiver. The corrected information can be applied to data from the roving receiver in real time in the field using radio signals or through post-processing after data capture using special processing software.

Figure 3.28. Differential GPS operation (source: http://www.nuvation.com)

Real-time DGPS occurs when the base station calculates and broadcasts corrections for each satellite as it receives the data. The correction is received by the roving receiver via a radio signal if the source is land based or via a satellite signal if it is satellite based and applied to the position it is calculating. As a result, the position displayed and logged to the data file of the roving GPS receiver is a differentially corrected position.

In the US, the most widely available DGPS system is the Wide Area Augmentation System (WAAS). The WAAS was created by the Federal Aviation Administration and the Department of Transportation for use in precision flight approaches. However, the system is not limited to aviation applications, and many of the commercial GPS receivers intended for maritime and automotive use support WAAS. WAAS provides GPS correction data across North America with a typical accuracy less than 5 meters. Similar systems are available in Europe.

The European Geostationary Navigation Overlay Service (EGNOS) is the first pan-European satellite navigation system. It augments the US GPS satellite navigation system and makes it suitable for safety critical applications such as flying aircraft or navigating ships through narrow channels.

Consisting of three geostationary satellites and a network of ground stations, EGNOS achieves its aim by transmitting a signal containing information on the reliability and accuracy of the positioning signals sent out by GPS. It allows users in Europe and beyond to determine their position to within 1.5 meters. (Source: [46])

Another high precision GPS solution is the real-time kinematic GPS (RTK GPS). RTK is a GBS-based survey that utilizes a fixed, nearby ground base station that is in direct communication with the rover receiver through a radio link. RTK is capable of taking survey grade measurements in real time and providing immediate accuracy to within 1-5 cm. RTK systems are the most precise of all GNSS systems. They are also the only systems that can achieve complete repeatability, allowing the returning to the exact location indefinitely. All this precision and repeatability comes at a fairly high cost. But there are other drawbacks to RTK also. RTK requires a base station within about 10-15 km of the rover. [47]

Assisted GPS describes a system where outside sources, such as an assistance server and reference network, help a GPS receiver perform the tasks required to make range measurements and position solutions. The assistance server has the ability to access information from the reference network and also has computing power far beyond that of the GPS receiver. The assistance server communicates with the GPS receiver via a wireless link (generally mobile network data link). With assistance from the network, the receiver can operate (start-up) more quickly and efficiently than it would unassisted, because a set of tasks that it would normally handle is shared with the assistance server. The resulting AGPS system, consisting of the integrated GPS receiver and network components, boosts performance beyond that of the same receiver in a stand-alone mode. It improves the start-up performance or time-to-first-fix (TTFF) of the GPS receiver. The assistance server is also able to compute position solutions, leaving the GPS receiver with the sole job of collecting range measurements. The only disadvantage of AGPS is that it depends on the infrastructure (requiring an assistant service and an on-line data connection). (Source: [48])

The primary HMI components are used for operating the vehicle basic functions and let the driver control the movement of the vehicle. Furthermore some of these components (and the minimum set of the devices) are regulated by the legal authorities. The following primary components can be considered as a basic primary HMI set in a passenger car with manual gearbox:

The role of the secondary HMI components is to operate and display the comfort and infotainment functions. The controls can be found on the dashboard, around the central armrest, on the door armrest, on the steering wheel and sometimes over the central mirror. Naturally nowadays it is a very diverse and large component group. The size and content is highly dependent on the equipment features of the vehicle. Basically it contains the control inputs of the heating, ventilation, and air conditioning (HVAC) system and the radio, and some indicator lights and alphanumeric LCDs.

4.2.2.1. Input channels

The evolution of the secondary input controls is very diverse, but it has a common property at each manufacturer, namely the multiplication of the comfort and infotainment functions. It has resulted many buttons, switches, sliders and knobs all around the driver. The first solutions for the simplification were the integrated controllers with which the driver can navigate through a menu on an LCD display.

The BMW iDrive Controller was designed to be one single interface for many functions and features of the vehicle through the central console display, as replacing the array of controls of the comfort and infotainment functions with an all-in-one unit. The rotate-and-press mechanism enables one-handed operation: right means ‘continue’, left ‘back’, turning the button allows you to scroll through a list and pressing it selects an option. Frequently used functions like multimedia, radio or navigation have direct access keys. (Source: [51])

Figure 4.4. BMW iDrive controller knob (Source: BMW)

Several other methods exist to simplify the secondary input HMIs, such as steering wheel buttons and touchscreens. Generally most of these solutions are based on a menu, displayed on a relatively large screen.

The design of the secondary input devices is a more and more challenging task, because of the conflicting requirements, i.e. the increasing number of functions (especially infotainment) and the traffic safety.

4.2.2.2. Output channels

The secondary HMI output channels do not differ that much from the primary output channels, meaning that they are mostly relatively big LCD displays, but they may include special features like touch-screen control or split view of the central console, where on the same display device the driver and the “navigator” views different pictures at the same time.

Figure 4.5. Splitview technology of an S-Class vehicle (Source: Mercedez-Benz)

In this subsection the mechanical interfaces are introduced. We classified those technologies into this group, which require mechanical impact from the driver, which could be the following:

4.3.1.3. Button, switch, stalk, slider

With the help of these components the driver can activate/deactivate or set the vehicle’s primary and secondary functions. These switching components could be single buttons or switches for each function, a stalk or a slider.

Figure 4.6. Indicator stalk with cruise control and light switches (Source: http://www.carthrottle.com/)

4.3.1.4. Integrated controller knob

The integrated controller knobs are such input devices which integrate more input functions into one device to support the much easier and more intuitive handling of the vehicle. These input functions could be rotation, push/pull and 4-way joystick.

A typical example is the BMW iDrive as mentioned in Section Input channels4.2.2.1, but a lot of simpler utilizations exist, such HVAC and radio control knobs.

Figure 4.7. Integrated radio and HVAC control panel with integrated knobs (Source: TRW)

4.3.1.5. Touchscreen

Infotainment displays often have touchscreen features enabling the driver to select functions via touching the display. Touchscreen technology is the direct manipulation type gesture based technology. A touchscreen is an electronic visual display capable of detecting and locating a touch over its display area. It is sensitive to the touch of a human finger, hand, pointed finger nail and passive objects like stylus. Users can simply move things on the screen, scroll them, zoom them and many more.

There are four main touchscreen technologies:

• Resistive

• Capacitive

• Surface Acoustic Wave

• Infrared

The most wide-spread ones are the resistive and the capacitive touchscreens, thus these types will be detailed in the following paragraphs.

Resistive LCD touchscreen monitors rely on a touch overlay, which is composed of a flexible top layer and a rigid bottom layer separated by insulating dots, attached to a touchscreen controller. The inside surface of each of the two layers is coated with a transparent metal oxide coating (ITO) that facilitates a gradient across each layer when voltage is applied. Pressing the flexible top sheet creates electrical contact between the resistive layers, producing a switch closing in the circuit. The control electronics alternate voltage between the layers and pass the resulting X and Y touch coordinates to the touchscreen controller. The touchscreen controller data is then passed on to the computer operating system for processing.

Figure 4.8. Resistive touchscreen (Source: http://www.tci.de)

Because of its versatility and cost-effectiveness, resistive touchscreen technology is the touch technology of choice for many markets and applications. Resistive touchscreens are used in food service, retail point-of-sale (POS), medical monitoring devices, industrial process control and instrumentation, portable and handheld products. Resistive touchscreen technology possesses many advantages over other alternative touchscreen technologies (acoustic wave, capacitive, infrared). Highly durable, resistive touchscreens are less susceptible to contaminants that easily infect acoustic wave touchscreens. In addition, resistive touchscreens are less sensitive to the effects of severe scratches that would incapacitate capacitive touchscreens. Drawback can be the too soft feeling when pressing it, since there is a mechanical deformation required to connect the two resistive layers to each-other. (Source: [53])

One can use anything on a resistive touchscreen to make the touch interface work; a gloved finger, a fingernail, a stylus device – anything that creates enough pressure on the point of impact will activate the mechanism and the touch will be registered. For this reason, resistive touchscreen require slight pressure in order to register the touch, and are not always as quick to respond as capacitive touchscreens. In addition, the resistive touchscreen’s multiple layers cause the display to be less sharp, with lower contrast than we might see on capacitive screens. While most resistive screens don’t allow for multi-touch gestures such as pinch to zoom, they can register a touch by one finger when another finger is already touching a different location on the screen. (Source: [54])

The capacitive touchscreen technology is the most popular and durable touchscreen technology used all over the world. It consists of a glass panel coated with a capacitive (conductive) material Indium Tin Oxide (ITO). The capacitive systems transmit almost 90% of light from the monitor. In case of surface-capacitive screens, only one side of the insulator is coated with a conducting layer. While the screen is operational, a uniform electrostatic field is formed over the conductive layer. Whenever, a human finger touches the screen, conduction of electric charges occurs over the uncoated layer which results in the formation of a dynamic capacitor. The controller then detects the position of touch by measuring the change in capacitance at the four corners of the screen. In the projected-capacitive touchscreen technology, the conductive ITO layer is etched to form a grid of multiple horizontal and vertical electrodes. It involves sensing along both the X and Y axis using clearly etched ITO pattern. The projective screen contains a sensor at every intersection of the row and column, thereby increasing the accuracy of the system. (Source: [55])

Figure 4.9. Projected capacitive touchscreen (Source: http://www.embedded.de)

Since capacitive screens are made of one main layer, which is constantly getting thinner as technology advances, these screens are not only more sensitive and accurate, the display itself can be much sharper. Capacitive touchscreens can also make use of multi-touch gestures, but only by using several fingers at the same time. If one finger is touching one part of the screen, it won’t be able to sense another touch accurately. (Source: [54])

The acoustic interfaces have long been common output interfaces in vehicles. They do not require that the driver take off his eyes from the road, thus it is safer than a visual output.

In the following subsections the several acoustic technologies are outlined in order of the development.

For the haptic channel, haptic feedback components on the steering wheel, the pedals and the driver’s seat are planned. The haptic functions that have already been mentioned in the corresponding sections, can be summarized as follows:

The purpose is to design speed trajectory, with which the longitudinal energy, thus fuel requirement can be reduced. If the inclination of the road and the speed limits are assumed to be known, the speed trajectory can be designed. By choosing the speed fitting in these factors the number of unnecessary accelerations and brakes can be reduced.

The road ahead of the vehicle is divided into several sections and reference speeds are selected for them, see Figure 87. The rates of the inclinations of the road and those of the speed limits are assumed to be known at the endpoints of each section. The knowledge of the road slopes is a necessary assumption for the calculation of the velocity signal. In practice the slope can be obtained in two ways: either a contour map which contains the level lines is used, or an estimation method is applied. In the former case a map used in other navigation tasks can be extended with slope information. Several methods have been proposed for slope estimation. They use cameras, laser/inertial profilometers, differential GPS or a GPS/INS systems, see [72], [73], [74]. An estimation method based on a vehicle model and Kalman filters was proposed by [75].

Figure 6.1. Division of road

The simplified model of the longitudinal dynamics of the vehicle is shown in Figure 88. The longitudinal movement of the vehicle is influenced by the traction force as the control signal and disturbances . Several longitudinal disturbances influence the movement of the vehicle. The rolling resistance is modeled by an empiric form where is the vertical load of the wheel, and are empirical parameters depending on tyre and road conditions and is the velocity of the vehicle, see GOTOBUTTON GrindEQbibitem24 . The aerodynamic force is formulated as where is the drag coefficient, is the density of air, is the reference area, is the velocity of vehicle relative to the air. In case of a lull , which is assumed in the paper. The longitudinal component of the weighting force is , where m is the mass of the vehicle and is the angle of the slope. The acceleration of the vehicle is the following: , where is the mass of the vehicle, is the position of the vehicle, and are the traction force and the disturbance force (), respectively.

Figure 6.2.  Simplified vehicle model

Although between the points may be acceleration and declaration an average speed is used. Thus, the rate of accelerations of the vehicle is considered to be constant between these points. In this case the movement of the vehicle using simple kinematic equations is: , where is the velocity of vehicle at the initial point, is the velocity of vehicle at the first point and is the distance between these points. Thus the velocity of the first section point is the following: . The velocity of the first section point is defined as the reference velocity . This relationship also applies to the next road section: . It is important to emphasize that the longitudinal force is known only the first section. Moreover, the longitudinal forces are not known during the traveling in the first section. Therefore at the calculation of the control force it is assumed that additional longitudinal forces will not act on the vehicle, i.e., the longitudinal forces will not affect the next sections. At the same time the disturbances from road slope are known ahead. Similarly, the velocity of the vehicle can be formalized in the next section points. Using this principle a velocity-chain, which contains the required velocities along the way of the vehicle, is constructed. At the calculation of the control force it is assumed that additional longitudinal forces will not affect the next sections. The velocities of vehicle are described at each section point of the road by using similar expressions to GOTOBUTTON GrindEQequation22 . The velocity of the section point is the following: . It is also an important goal to track the momentary value of the velocity. It can also be considered in the following equation:

The disturbance force can be divided in two parts: the first part is the force resistance from road slope , while the second part contains all of the other resistances such as rolling resistance, aerodynamic forces etc. We assume that is known while is unknown. depends on the mass of the vehicle and the angle of the slope . When the control force is calculated, only influences the vehicle of all of the unmeasured disturbances. In the control design the effects of the unmeasured disturbances are ignored. The consequence of this assumption is that the model does not contain all the information about the road disturbances, therefore it is necessary to design a robust speed controller. This controller can ignore the undesirable effects. Consequently, the equations of the vehicle at the section points are calculated in the following way:

The vehicle travels in traffic and it may happen that the vehicle is overtaken. Because of the risk of collision it is necessary to consider the preceding velocity on the lane:

The number of the segments is important. For example in the case of flat roads it is enough to use relatively few section points because the slopes of the sections do not change abruptly. In the case of undulating roads it is necessary to use relatively large number of section points and shorter sections, because it is assumed in the algorithm that the acceleration of the vehicle is constant between the section points. Thus, the road ahead of the vehicle is divided unevenly, which is consistent with the topography of the road.

In the following step weights are applied to reference speeds. An additional weight is applied to the momentary speed. An additional weight is applied to the leader speed. While the weights represent the rate of the road conditions, weight has an essential role: it determines the tracking requirement of the current reference velocity . By increasing the momentary velocity becomes more important while road conditions become less important. Similarly, by increasing the road conditions and the momentary velocity become negligible. The weights should sum up to one, i.e. .

Weights have an important role in control design. By making an appropriate selection of the weights the importance of the road condition is taken into consideration. For example when and the control exercise is simplified to a cruise control problem without any road conditions. When equivalent weights are used the road conditions are considered with the same importance, i.e., and . When and only the tracking of the preceding vehicle is carried out. The optimal determination of the weights has an important role, i.e., to achieve a balance between the current velocity and the effect of the road slope. Consequently, a balance between the velocity and the economy parameters of the vehicle is formalized.

By summarizing the above equations the following formula is yielded:

where the value depends on the road slopes, the reference velocities and the weights

In the final step a control-oriented vehicle model, in which reference velocities and weights are taken into consideration, is constructed. The momentary acceleration of the vehicle is expressed in the following way: where . Equation (6) is rearranged:

where the parameter is calculated based on the designed . Consequently, the road conditions can be considered by speed tracking. The momentary velocity of vehicle should be equal to parameter , which contains the road information. The calculation of requires the measurement of the longitudinal acceleration .

In the following step the task is to find an optimal selection of the weights in such a way that both the minimization of control force and the traveling time are taken into consideration. Equation (6) shows that depends only on the weights in the following way:

Since depends on the weight , therefore depends on the weights and . The longitudinal control force must be minimized, i.e., . Instead, in practice the optimization is used because of the simpler numerical computation. Simultaneously, the difference between momentary velocity and modified velocity must be minimized, i.e.,

The two optimization criteria lead to different optimal solutions. In the first criterion the road inclinations and speed limits are taken into consideration by using appropriately chosen weights . At the same time the second criterion is optimal if the information is ignored. In the latter case the weights are noted by . The first criterion is met by the transformation of the quadratic form into the linear programming using the simplex algorithm. It leads to the following form: with the following constrains and . This task is nonlinear because of the weights. The optimization task is solved by a linear programming method, such as the simplex algorithm.

The second criterion must also be taken into consideration. The optimal solution can be determined in a relatively easy way since the vehicle tracks the predefined velocity if the road conditions are not considered. Consequently, the optimal solution is achieved by selecting the weights in the following way: and .

In the proposed method two further performance weights, i.e., and , are introduced. The performance weight () is related to the importance of the minimization of the longitudinal control force while the performance weight is related to the minimization of . There is a constraint according to the performance weights . Thus the performance weights, which guarantee balance between optimizations tasks, are calculated in the following expressions:

, i=1,n(11)

Based on the calculated performance weights the speed can be predicted.

The tracking of the preceding vehicle is necessary to avoid a collision, therefore is not reduced. If the preceding vehicle accelerates, the tracking vehicle must accelerate as well. As the velocity increases so does the braking distance, therefore the following vehicle strictly tracks the velocity of the preceding vehicle. On the other hand it is necessary to prevent the velocity of the vehicle from increasing above the official speed limit. Therefore the tracked velocity of the preceding vehicle is limited by the maximum speed. If the preceding vehicle accelerates and exceeds the speed limit the following vehicle may fall behind.

Remark 3.1 Look-ahead control considering efficiency and safe cornering

The maximum cornering velocity for each section can be calculated in advance knowing the designed path of the vehicle. If the speed limit at section point exceeds the safe cornering velocity then the speed limit is substituted with the smallest value concerning skidding or rollover, i.e.,

The control system can be realized in three steps as Figure 89 shows. The aim of the first step is the computation of the reference velocity. The results of this computation are the weights and the modified velocity which must be tracked by the vehicle. In the second step the longitudinal control force of the vehicle () is designed. The role of the high-level controller is to calculate this required longitudinal force. In third step the real physical inputs of the system, such as the throttle, the gear position and the brake pressure are generated by the low-level controller.

Figure 6.3. Implementation of the controlled system

In the proposed method the steps are separated from each other. The reference velocity signal generator can be added to the upper-lower Adaptive Cruise Control (ACC) system. It is possible to design a reference signal generator unit almost individually, and to attach it to the ACC system. Thus the reference signal unit can be designed and produced independently from automobile suppliers, only a few vehicle data are needed. The independent implementation possibility is an important advantage in practice. The high-level controller calculates positive and negative forces as well, therefore the driving and braking systems are also actuated. Figure 90 shows the architecture of the low-level controller.

Figure 6.4. Architecture of the low-level controller

The engine is controlled by the throttle, which could be a butterfly gate or a quantity of injected fuel. The engine-management system and the fuel-injection system have their own controllers, thus in the realization of the low-level controller only the torque-rev-load characteristics of the engine are necessary. In this case the rev of the engine is measured, the required torque is computed from the longitudinal force of the high-level controller, thus the throttle is determined by an interpolation step using a look-up table. The position of the automatic transmission is determined by logic functions, thus it depends on the fuel consumption and the maximum rev of the engine. The pressures on the cylinders of the wheels increase during braking. At normal traveling the ABS actuation is not necessary, in case of an emergency the optimal driving is overwritten by the safety requirements. The necessary braking pressure for the required braking force is computed from the ratios of the hydraulic/pneumatic parts.

In the simulation example a transportational route with real data is analyzed. The terrain characteristics and geographical information are those of the M1 Hungarian highway between Tatabánya and Budapest in a --long section. In the simulation a typical F-Class truck travels along the route. The mass of the 6--gear truck is and its engine power is (). The regulated maximal velocity is , but the road section contains other speed limits (e.g. or ), and the road section also contains hilly parts. Thus, it is an acceptable route for the analysis of road conditions, i.e., inclinations and speed limits. Publicly accessible up-to-date geographical/navigational databases and visualisation programs, such as Google Earth and Google Maps, are used for the experiment.

Figure 91: Real data motorway simulation

In this example two different controllers are compared. The first is the proposed controller, which considers the road conditions such as inclinations and speed limits and is illustrated by solid line in the figures, while the second controller is a conventional ACC system, which ignores this information and is illustrated by dashed line. Figure 91 shows the time responses of the simulation.

Figure 91(a) shows that the motorway contains several uphill and downhill sections. Figure 91(b) shows the velocity of the vehicle with speed limits in both cases. The conventional ACC system tracks the predefined speed limits as accurately as possible and the tracking error is minimal. In the proposed method the speed is determined by the speed limits and simultaneously it takes the road inclinations into consideration according to the optimal requirement. Figure 91(c) shows the required longitudinal force. The high-precision tracking of the predefined velocities in the conventional ACC system often requires extremely high forces with abrupt changes in the signals. As a result of the road conditions less energy is required during the journey in the proposed control method, see Figure 91(f). The proposed method requires smaller energy () than the conventional method (), and the energy saving is , which is . The fuel consumption can also be calculated by using the following approximation: where is the efficiency of the driveline system, is the heat of combustion and is the density of petrol. The fuel consumption of the conventional system is while that of the proposed method is , which results in reduction in fuel consumption in the analyzed length section.

Remark 3.2 Speed control based on the preceding vehicle

In addition to the consideration of road conditions it is also important to consider the traffic environment. It means that the preceding vehicle must be considered in the reference speed design because of the risk of collision. Thus, all of the kinetic energy of the vehicle is dissipated by friction. This estimation of the safe stopping distance may be conservative in a normal traffic situation, where the preceding vehicle also brakes, therefore the distance between the vehicles may be reduced. In this paper the safe stopping distance between the vehicles is determined according to the 91/422/EEC, 71/320/EEC UN and EU directives (in case of vehicle category, the velocity in ): . It is also necessary to consider that without the preceding vehicle the consideration of safe stopping distance is neither possible nor necessary. The consideration of the preceding vehicle is determined by . The following example analyses the incidence when another vehicle overtakes the vehicle with an adaptive control proposed in the paper or the vehicle catches up with a preceding vehicle.

In the simulation example, the preceding vehicle is slower, however, in the second part its velocity is higher than that of the follower vehicle. Furthermore, in the example the preceding vehicle also exceeds the official speed limit (). Figure 92(a) and Figure 92(b) show that in the first part of the simulation the follower vehicle approaches the preceding vehicle taking the braking distance into consideration, while in the second part the follower vehicle avoids exceeding the speed limit and falls behind. This velocity control is achieved by using the value of as it is shown in Figure 92(d). In the first part of the simulation the weight is increased by the risk incident while in the second part it is reduced by the increasing distance. The solution requires radar information, which is available in a conventional adaptive cruise control vehicle. This simulation example shows that the designed control system is able adapt to external circumstances.

Figure 92: Adaptive control systems with a preceding vehicle

The main idea behind the design is that each vehicle in the platoon is able to calculate its speed independently of the other vehicles. Since traveling in a platoon requires the same speed, the optimal speed must be modified according to the other vehicles. In the platoon, the speed of the leader vehicle determines the speed of all the vehicles. The goal is to determine the common speed at which the speeds of the members are as close as possible to their own optimal speed. In the case of a platoon each vehicle has its own optimal reference speed . Moreover, speeds of the vehicles are not independent of each other, because the speed of the leader influences the speed of every member of platoon . The goal is to find an optimal reference speed for the leader .

It is important to note that there is an interaction between the speeds of the vehicles in a platoon. If a preceding vehicle changes its speed, the follower vehicles will modify their speeds and track the motion of the preceding vehicle within a short time. The members of the platoon are not independent of the leader, therefore it is necessary to formulate the relationship between the speed of the platoon member and the leader and the preceding vehicles. It is formulated with a transfer function with its input and output. The output contains the information, i.e., the position, speed and acceleration of the vehicle, which is sent to the follower vehicles. The input contains the position, speed, the acceleration of the preceding vehicle and the leader. The transfer function between and is with the controller of the vehicle and its longitudinal dynamics . Similarly, the effect of the leader and the preceding vehicles on the platoon member is formulated: , where , which finally leads to . Consequently, the speed of the vehicle is determined by the next formula: . The value of is used for the computation of the optimal reference speed of the platoon .

Finally the required reference speed of the leader vehicle is designed. The aim of the design is that the generated speeds of all the vehicles are as close to as their modified reference speed as possible:

Since the speed of the vehicle is formulated as , the following optimization form is used: , where . It can be stated that in GOTOBUTTON GrindEQequation48 the only unknown variable is . The optimization leads to the following equation: The solution of the optimization problem can be achieved. The deduction of the optimization of is as follows:

It means that the leader vehicle must track the required reference speed .

Figure 6.5. Architecture of the control system

Government transportation agencies have been evaluating several studies in the field of highway road planning in respect to horizontal curve design. Road design manuals determine a minimum curve radius for a predefined velocity, road superelevation and adhesion coefficient, see [76], [77]. The calculation is based on assuming the vehicle to move on a circular path, where the vehicle is subject to centrifugal force that acts away from the center of the curve as illustrated in Figure 94, see [78]. The slip angle is assumed to be small enough for the lateral force to point along the path radius, and the longitudinal acceleration is also small enough not to degrade the lateral friction of the vehicle considerably.

Figure 6.6. Counterbalancing side forces in cornering maneuver

The mass of the vehicle along with the road superelevation (cross slope) and the side friction between the tire and the road surface counterbalance the centrifugal force. Assuming that there is the same at each wheel of the vehicle the sum of the lateral forces is: , where is the gravitational constant. At cornering the dynamics of vehicle is described by the equilibrium of the two forces: , where is the radius of the curve. Assuming that the road geometry is known by on-board devices such as GPS, it is possible to calculate a safe cornering velocity.

The following relationship holds for the maximum safe cornering velocity regarding the danger of skidding out of the corner:

(15) is also used in crash reconstruction and is referred to as the Critical Speed Formula (CSF), see [79], [80]. In the so-called yaw mark method the critical speed of the vehicle is determined by using the calculated radius of the vehicle path from the tire skid marks left on the road.

Note that in the calculation of the safe cornering velocity the value of the side friction factor plays a major role. This factor depends on the quality and the texture of the road, the weather conditions and the velocity of the vehicle and several other factors. The estimation of has been presented by several important papers, see e.g. [81], [82], [83]. However, these estimations are based on instant measurements, thus they are not valid for look-ahead control design, where the friction of future road sections should be estimated.

In road design handbooks the value of the friction factor is given in look-up tables as a function of the design speed, and it is limited in order to determine a comfortable side friction for the passengers of the vehicle. For the calculation of safe cornering velocity these friction values give a very conservative result.

A method to evaluate side friction in horizontal curves using supply-demand concepts has been presented by [84]. Here the side friction has an exponential relation with the design speed as follows:

where and are constant values depending on the texture of the pavement. Note that is the estimated reference friction at a measurement speed of . Thus, by using (15) and (16) the maximum safe cornering speed can be determined on a given road surface along with the side friction factor, as it is illustrated inFigure 95. The intersections of the supply and demand curves give the safe cornering velocities and the corresponding maximum side frictions. Note that whereas the friction supply only depends on the velocity of the vehicle, the friction demand is a function of the velocity and the curve radius as well.

Figure 6.7. Relationship between supply and demand of side friction in a curve

The relationship between the curve radius and safe cornering velocity can be observed in Figure 5. The data points of the diagram are given by the intersections of the supply and demand curves. It shows that the safe cornering velocity increases with the curve radius. However, the relationship is not linear, i.e by increasing an already big cornering radius results in only moderated growth of the safe cornering velocity.

Figure 6.8. Relationship between curve radius and safe cornering velocity

Rollover danger estimation and prevention control methods have already been studied by several authors, see [85], [86], [87]. A quasi-static analysis of maximal safe cornering velocity regarding the danger of rollover has been presented by [88]. Assuming a rigid vehicle and using small angle approximation for superelevation (, ), a moment equation can be written for the outside tires of the vehicle during cornering as follows:

where is the height of the center of gravity, is the track width, is the load of the inside wheel at cornering.

The vehicle stability limit occurs at the point when the load reaches zero, which means the vehicle can no longer maintain equilibrium in the roll plane. Thus by reorganizing (17) and substituting the rollover threshold is given as follows:

Thus, to ensure safe cornering of the vehicle in a cornering maneuver without the danger of skidding or rollover, the velocity of the vehicle has to chosen to meet the two constraints defined by (15) and (16).

Another important task is to calculate the radius of curves ahead of the vehicle in order to define the safe cornering velocities in advance. The road ahead of the vehicle can be divided into number of sections. The goal is to calculate the curve radius at each section points ahead of the vehicle to determine the safe cornering velocities corresponding to the curve radius.

The calculation of the cornering radius , is as follows. It is assumed that the global trajectory coordinates and of the vehicle path are known. Considering a sufficiently small distance the trajectory of the vehicle around a section point can be regarded as an arc, as it is shown in Figure 97. The arc can be divided into data points.

The length of the arc can be approximated by summing up the distances between data points: These distances are calculated as: The length of the chord is calculated as follows: Knowing the length and , it is possible to calculate a reasonable estimation of the curve radius . Note that the length of the arc must be chosen carefully. A too short section with fewer data points may give an unacceptable approximation of the radius. On the other hand a too big distance can be inappropriate as well, since then the section may not be approximated by a single arc. The number of data points selected is also important, and by increasing the number , the accuracy of the following calculation can be enhanced. The angle of the arc is as follows . The length of the chord is also expressed as a function of the radius: . Expressing the radius the following equation is derived:

Figure 6.9. The arc of the vehicle path

This expression can be transformed by introducing and using the Taylor series for the approximation of the function, i.e., . Then the following expression is gained for the radius:

The curve radius , can be calculated at each section point ahead of the vehicle path. The calculation method is validated through the CarSim simulation environment. Here, the vehicle follows the desired path while the curve radius is being measured and at the same time the calculation method is running giving a close approximation of the real value. The comparison of the real and the calculated radius is shown in Figure 98.

Figure 6.10. Validation of the calculation method

By calculating the radius of the curve using (20) the safe cornering velocity of the vehicle can be determined by using (15) and (18). This velocity can be considered as the maximum velocity that the vehicle is capable of in a corner without the danger of slipping and leaving the track or rolling over. It is important to state that in severe weather conditions this safe velocity may be smaller than that of the speed limit, thus the consideration of the maximum safety velocity in the cruise control design is essential.

EPB stands for Electro Pneumatic Braking system, which is practically a brake-by-wire system with pneumatic actuators and pneumatic backup system. Today these systems, generally called as EBS are standard in all modern heavy duty commercial vehicles (motor vehicles and trailers). EBS was introduced by Wabco in 1996 as the series production brake system of Mercedes Benz Actros.

The basic functionality of the EBS can be observed in Figure 111 [101]. The driver presses the brake pedal which is connected to the foot brake module. It measures the pedal’s path with redundant potentiometers and sends the drivers demand to the central ECU. The EBS’s central ECU calculates the required pressure for each wheel and sends the result to the brake actuator ECUs. In this ECU there is closed loop pressure control software, which sets the air pressure in the brake chambers to the prescribed value. The actuator ECU also measures the wheel speed and sends this information back to the central EBS ECU. During the normal operation the actuator ECUs energize the so-called backup valves, which results in the pneumatic backup system’s deactivation. In case of any detected or unexpected error (and when unpowered) the backup valves are dropped and the conventional pneumatic brake system becomes active again.

Figure 7.14. Layout of an Electro Pneumatic Braking System (Source: Prof. Palkovics)

The advantages of electronic control over conventional pneumatic control are shorter response times and build-up times in brake cylinders, reducing the brake distance and the integration possibility of several active safety and comfort functions like the followings.

The intention of eliminating the hydraulic connection between the brake pedal and the actuator is primarily motivated by the need of integration of the several active safety functions such as ABS, ASR and VDC (ESP).

An Electro Hydraulic Brake system is practically a brake-by-wire system with hydraulic actuators and a hydraulic backup system. Normally there is no mechanical connection between the brake pedal and the hydraulic braking system. When the brake pedal is pressed the pedal position sensor detects the amount of movement of the driver, thus the distance the brake pedal travelled. In addition the ECU feedbacks to the brake pedal’s actuator which hardens the pedal to help the driver to feel the amount of the braking force. As a function of distance the ECU determines the optimum brake pressure for each wheel and applies this pressure using the hydraulic actuators of the brake system. The brake pressure is supplied by a piston pump driven by an electric motor and a hydraulic reservoir which is sufficient for several consecutive brake events. The nominal pressure is controlled between 140 and 160 Ba. The system is capable of generating or releasing the required brake pressure in a very short time. It results in shorter stopping distance and in more accurate control of the active safety systems. Should the electronic system encounter any errors the hydraulic backup brake system is always there to take over the braking task. The following figure shows the layout of an electronically controlled hydraulic brake system[100].

Figure 7.15. Layout of an Electro Hydraulic Braking System (Source: Prof. von Glasner)

The first brake-by-wire system with hydraulic backup was born from the cooperation of Daimler and Bosch in 2001, which was called Sensotronic Brake Control (SBC). It was not a success story, because of software failures. The customers complained because the backup mode resulted in longer stopping distance and higher brake pedal effort by the driver. In May 2004, Mercedes recalled 680,000 vehicles to fix the complex brake-by-wire system. Then, in March 2005, 1.3 million cars were recalled, partly because of further unspecified problems with the Sensotronic Brake Control system. (Source: [102])

The EMB stands for electromechanical braking system, which is a fully brake-by-wire system. It does not use any hydraulic nor pneumatic actuators as shown on Figure 113 [100]. EMB reduces the stopping distance on account of its rapid brake response, eliminates the necessity of a brake cylinder, brake lines and hoses, as all these components are replaced by electric wiring. The use of electrics reduces maintenance expense, and also eliminates the expense of brake fluid disposal. EMB likewise measures the force of the driver's intention to brake the vehicle via sensors monitoring the system in the brake-pedal feel simulator. The ECU processes the signals received, links them where appropriate to data from other sensors and control systems, and calculates the force to be generated by the electronic brake caliper (e-caliper) of each wheel when pressing the brake pads onto the brake disc. The wheel brake modules essentially consist of an electric control unit, an electric motor and a transmission system. The electric motor and transmission system form the so-called actuator which generates the brake application forces in the brake caliper. These actuators are capable of delivering forces of up to several tons in just a few milliseconds. (Source: [103])

Figure 7.16. Layout of an Electro Mechanic Brake System (Source: Prof. von Glasner)

Power requirements for EMB are high and would overload the capabilities of conventional 12 volt systems installed in today's vehicles. Therefore the electro-mechanical brake is designed for a working voltage of 42 volts, which can be ensured with extra batteries.

The purpose of the clutch is to establish a releasable torque transmission link between the engine and the transmission through friction, allowing the gears to be engaged. Besides changing gears it enables functions like smooth starting of the vehicle or stopping the car without having to stop the internal combustion engine. In traditional vehicles the clutch is operated by the driver through the clutch pedal that uses mechanical Bowden or a hydraulic link to the clutch mechanism. In clutch-by-wire applications there is no need for a clutch pedal, the release and engage of the clutch is controlled by an electronic system.

In the SensoDrive electronically controlled manual transmission system of Citroen there is no clutch pedal. The gear shifting is simply done by selecting the required gear with the gear stick or the paddle integrated into the steering wheel. During gear shifting the driver even does not have to release the accelerator pedal. The SensoDrive system is managed by an electronic control unit (ECU), which controls two actuators. One actuator changes gears while the other, which is equipped with a facing wear compensation system, opens and closes the clutch. The following figure illustrates the operation of the clutch-by-wire system of Citroen [104].

Figure 7.17. Clutch-by-wire system integrated into an AMT system (Source: Citroen)

In case of an automated manual transmission (AMT) a simple manual transmission is transformed into an automatic transmission system by installing a clutch actuator, a gear selector actuator and an electronic control unit. The shift-by-wire process is composed of the following steps. After the clutch is opened by the electromechanical clutch actuator, the gear shifting operation in the gearbox is carried out by the electromechanical transmission actuator. When the appropriate gear is selected then the electromechanical clutch actuator closes the clutch and drive begins. These two actuators are controlled by an electronic control unit. If required, the system determines the shift points fully automatically, controls the shift and clutch processes, and cooperates with the engine management system during the shift process with respect to engine revolution and torque requests [105].

Figure 7.18. Schematic diagram of an Automated Manual Transmission (Source: ZF)

Automated Manual Transmissions have a lot of favourable properties; the disadvantage is that the power flow (traction) is lost during switching gear, as it is necessary to open the clutch. This is what automatic transmission systems have eliminated providing continuous traction during acceleration.

The heart of the Dual Clutch Transmission (DCT) is the combined dual clutch system. The DSG acronym is originally derived from the German word of “DoppelSchaltGetriebe” but it also has an English alternative of “Direct Shift Gearbox”. The reason for the naming is that there are two transmission systems integrated into one. Transmission one includes the odd gears (first, third, fifth and reverse), while transmission two contains the even gears (second, fourth and sixth). The combined dual clutch system switches from one to the other very quickly, releasing an odd gear and at the same time engaging a preselected even gear and vice versa. Using this arrangement, gears can be changed without interrupting the traction from the engine to the driven wheels. This allows dynamic acceleration and extremely fast gear shifting times that are below human perception[106].

Figure 7.19. Layout of a dual clutch transmission system (Source: howstuffworks.com)

The hydrodynamic torque converter and planetary gear transmissions can be found in premium segment passenger cars, commercial vehicles and buses. The design of the hydrodynamic transmission with planetary gear is simple and clear as can be observed on the figure below[107]. The main piece is the hydrodynamic counter-rotating torque converter. Situated in front of it are the impeller brake, the direct gear clutch, the differential transmission, the input clutch and the overdrive clutch. A hydraulic torsional vibration damper at the transmission input reduces engine vibrations effectively. Behind the converter, an epicyclical gear combines the hydrodynamic and mechanical forces. The final set of planetary gears activates the reverse gear and, during braking, also the retarder Gear-shifting commands are placed by the electronic control system; gear shifting occurs electro-hydraulically, with solenoid valves. The transmission electronic control unit is in continuous data exchange with other ECUs like engine and the brake system management to provide a harmonized control.

Figure 7.20. Cross sectional diagram of a hydrodynamic torque converter with planetary gear (Source: Voith)

The Continuously Variable Transmission (CVT) ideally matches the needs of vehicle traction. This is also beneficial in terms of fuel consumption, pollutant emissions, acceleration and driving comfort. The first series production CVT appeared in 1959 in a DAF city car, but technology limitations made it suitable for engines with less than 100 horsepower. The enhanced versions with electronic control capable of handling more powerful engines can be found in the production line of several major OEM.

While traditional automatic transmissions use a set of gears that provides a given number of ratios there are no gears in CVT transmissions, but two V-shaped variable-diameter pulleys connected with a metal belt. One pulley is connected to the engine, the other to the driven wheels.

Figure 7.21. CVT operation at high-speed and low-speed (Source: Nissan)

Changing the diameter of the pulleys varies the transmission ratio (the number of times the output shaft spins for each revolution of the engine). Illustrated in the figures above, as the engine (input) pulley width increases and the tire (output) pulley width decreases, the engine pulley becomes smaller than the tire pulley for the diameter of the part where the pulley and the belt are in contact. This is the state of being in low gear. Vice versa, as the engine pulley width decreases and the tire pulley width increases, the diameter of the part where belt and pulley are in contact grows larger for the engine pulley than the tire pulley, which is the state of being in high gear. The main advantage of the CVT is that pulley width can be continuously changed, allowing the system to change transmission gear ratio smoothly and without steps [108].

The controls for a CVT are the same as an automatic: Two pedals (accelerator and brake) and a P-R-N-D-L-style shift pattern.

Since the topology of the network is constantly changing, the issue of routing packets between any pair of nodes becomes a challenging task. In a MANET routers (i.e. hosts) can be mobile and inter -router connectivity can change frequently during normal operation. In contrast the Internet (like most of the telecom networks) possesses a quasi-fixed infrastructure consisting of routers or switches that forward data over hardwired links. Traditionally end-user devices, such as host computers or telephones, attach to these networks at fixed locations. As a consequence they are assigned addresses based on their location in a fixed network-addressing hierarchy. It simplifies routing in these systems, as a user’s location does not change.

The end devices are increasingly mobile, meaning that they can change their point of attachment to the fixed infrastructure. This is the paradigm of cellular telephony and its Internet equivalent, mobile data network. In this approach a user’s identity depends upon whether the user adopts a location-dependent (temporary) or location-independent (permanent) identifier. Users with temporary identifiers are sometimes referred to as nomadic, whereas users with permanent identifiers are referred to as mobile. The distinction is that although nomadic users may move, they carry out most network related functions in a fixed location. Mobile users must work “on the go” changing points of attachment as necessary. In either case additional networking support may be required to track a user’s location in the network so that information can be forwarded to its current location using the routing support within the more traditional fixed hierarchy.

Figure 8.4. Multi-hop routing (Source: http://sar.informatik.hu-berlin.de)

Internet is hardly tuned to allow mobility during the data transfers because protocols are not conceived for devices that frequently change their point of attachment in the topology. There is typically a change of the physical IP address each time a mobile node changes its point of attachment and thus its reachability to the Internet topology. This results in losing packets in transit and breaking transport protocols connections if mobility is not handled by specific services. The protocol stack must therefore be upgraded with the ability to cross networks during data transfers, without breaking the communication session and with minimum transmission delays and signalling overhead. This is commonly referred to as mobility support. Host mobility support is handled by Mobile IPv6.

In contrast, the goal of mobile ad hoc networking is to extend mobility into the field of autonomous, mobile, wireless domains, where a set of nodes, which may be combined routers and hosts, themselves form the network routing infrastructure in an ad hoc way. With Mobile Ad Hoc Networking, the routing infrastructure can move along with the end devices. Thus the infrastructure’s routing topology can change, and the addressing within the topology can change. In this paradigm an end-user’s association with a mobile router (its point of attachment) determines its location in the MANET. As mentioned above a user’s identity may be temporary or permanent. The fundamental differences in the composition of the routing infrastructure (fixed, hardwired, and bandwidth-rich opposite to dynamic, wireless, and bandwidth-constrained) causes that much of the fixed infrastructure’s control technology is no longer useful. The infrastructure’s routing algorithms and much of the networking suite must be reworked to function efficiently and effectively in mobile environment.

Figure 8.5. Multicast communication (Source: http://en.wikipedia.org/wiki/File:Multicast.svg)

Multicast routing (delivery of information to a group of destination devices simultaneously in a single transmission) is another challenge because the multicast tree is no longer static due to the random movement of nodes within the network. Routes between nodes may potentially contain multiple hops, which is more complex than the single hop communication.

In addition to the common vulnerabilities of wireless connection an ad hoc network has its particular security problems. Mobile hosts join in on the fly and create a network on their own. With the network topology changing dynamically and the lack of a centralized network management functionality, these networks tend to be vulnerable to a number of attacks. If such networks are to succeed in the transportation, the security aspect naturally assumes importance. The unreliability of wireless links between nodes, constantly changing topology owing to the movement of nodes in and out of the network and lack of incorporation of security features in statically configured wireless routing protocols not meant for ad hoc environments all lead to increased vulnerability and exposure to attacks.

Security in wireless ad hoc networks is particularly difficult to achieve, because of the vulnerability of the links, the limited physical protection of each of the nodes, the sporadic nature of connectivity, the dynamically changing topology, and the absence of a certification authority and the lack of a centralized monitoring or management point. This underscores the need for intrusion detection, intrusion prevention, and related countermeasures. The wireless links between nodes are highly susceptible to link attacks, which include the following threats (See [111]).

Eavesdropping might give an adversary access to secret information, violating confidentiality. Active attacks might allow the adversary to delete messages, to inject erroneous messages, to modify messages, and to impersonate a node, thus violating availability, integrity, authentication, and non-repudiation (these and other security needs are discussed in the next section). Ad hoc networks do not have a centralized piece of machinery such as a name server, which could lead to a single point of failure and thus, make the network that much more vulnerable.

An additional problem related to the compromised nodes is the potential byzantine failures encountered within Mobile Ad hoc Network (MANET) routing protocols wherein a set of the nodes could be compromised in such a way that the incorrect and malicious behaviour cannot be directly noted at all. The compromised nodes may seemingly operate correctly, but, at the same time, they may make use of the flaws and inconsistencies in the routing protocol to undetectably distort the routing fabric of the network. In addition such malicious nodes can also create new routing messages and advertise non-existent links, provide incorrect link state information and flood other nodes with routing traffic, thus inflicting byzantine failures on the system. Such failures are severe, because they may come from seemingly trusted nodes. Even if the compromised nodes were noticed and prevented from performing incorrect actions, the erroneous information generated by the byzantine failures might have already been propagated through the network.

Finally scalability is another issue that has to be addressed when security solutions are being devised, for the simple reason that an ad hoc network may consist of hundreds or even thousands of nodes. The scalability requirements directly affect the scalability requirements targeted to various security services such as key management. Standard security solutions would not be good enough since they are essentially for statically configured systems. This gives rise to the need for security solutions that adapt to the dynamically changing topology and movement of nodes in and out of the network.

These vulnerabilities allow a lot of special attacks in the vehicular networks including but not limited to the following examples:

Figure 8.6. Bogus information attack

As apparent from the above the security aspects are the most critical challenges before the wide spreading of the vehicular networks.

The goal of QoS support is to achieve a more deterministic communication behaviour, so that information carried by the network can be better prioritized and network resources can be better utilized. The notion of QoS is an agreement or a guarantee by the network to provide a set of measurable pre-specified service attributes to the user in terms of network delay, delay variance (jitter), available bandwidth, probability of packet loss (loss rate), etc. The IETF RFC 2386 characterizes QoS as a set of service requirements to be met by the network while transporting a packet stream from source to destination. A network’s ability to provide a specific QoS depends upon the properties of the network itself, which span over all the elements in the network. For the transmission link, the properties include link delay, throughput, loss rate, and error rate. For the nodes, the properties include hardware capability, such as processing speed and memory size. Above the physical qualities of nodes and transmission links, the QoS control algorithms operating at different layers of the network also contribute to the QoS support in networks. Unfortunately the features of MANETs show weak support for QoS. The wireless link has low, time-varying raw transmission capacity with relatively high error rate and loss rate. In addition, the possible various wireless physical technologies that nodes may use simultaneously to communicate make MANETs heterogeneous in nature. Each technology will require a MAC layer protocol to support QoS. Therefore the QoS mechanisms above the MAC layer should be flexible to fit the heterogeneous underlying wireless technologies. Providing different quality of service levels in a constantly changing environment will be a challenge. The inherent stochastic feature of communications quality in a MANET makes it difficult to offer fixed guarantees on the services offered to a device.

In addition to the communication within an ad hoc network, internetworking between MANET and fixed networks (mainly IP based) is often expected in many cases. The coexistence of routing protocols in such a mobile device is a challenge for the harmonious mobility management.

For most of the light-weight (handheld) mobile terminals and also the built-in automotive ECUs, the communication-related functions should be optimised for low power consumption. Conservation of power and power-aware routing must be taken into account.

The IEEE 802.11p WAVE standardization process originates from the allocation of the Dedicated Short Range Communications (DSRC) spectrum band in the United States and the effort to define the technology for usage in the DSRC band. The evolutions and the basics are explained in the following chapters from [112].

In 1999 the U.S. Federal Communication Commission allocated 75MHz of Dedicated Short Range Communications (DSRC) spectrum at 5.9 GHz to be used exclusively for vehicle-to-vehicle and infrastructure-to-vehicle communications. The primary goal is to enable public safety applications that can save lives and improve traffic flow. Private services are also permitted in order to spread the deployment costs and to encourage the quick development and adoption of DSRC technologies and applications.

As shown in Figure 126 the DSRC [113] spectrum is structured into seven 10 MHz wide channels. Channel 178 is the control channel (CCH), which is restricted to safety communications only. The two channels at the ends of the spectrum band are reserved for special uses. The rest are service channels (SCH) available for both safety and non-safety usage.

Figure 8.8. DSRC spectrum band and channels in the U.S.

The DSRC band is a free but licensed spectrum. It is free because the Federal Communications Commission (FCC) does not charge a fee for the spectrum usage. Yet it should not be confused with the unlicensed bands in 900 MHz, 2.4 GHz and 5 GHz that are also free in usage. These unlicensed bands, which are increasingly populated with WiFi, Bluetooth and other devices, place no restrictions on the technologies other than some emission and co-existence rules. The DSRC band, on the other hand, is more restricted in terms of the usages and technologies. FCC rulings regulate usage within certain channels and limit all radios to be compliant to a standard. In other words one cannot develop a different radio technology (e.g. that uses all 75 MHz of spectrum) for usage in the DSRC band even if it is limited in transmission power as related to the unlicensed band.

Similar efforts are occurring in Europe to set spectrum aside for vehicular usage. Since 2008 European Commission’s decision provides a single EU-wide frequency band that can be used for immediate and reliable communication between cars, and between cars and roadside infrastructure. It is 30 MHz of spectrum in the 5.9 Gigahertz (GHz) band which is allocated by national authorities across Europe to road safety applications, without barring other services already in place (such as radio amateur services). The intention is that compatibility with the USA will be ensured even if the allocation is not exactly the same; frequencies will be sufficiently close to enable the use of the same antenna and radio transmitter/receiver. (Source: [114])

The worldwide DSRC spectrum allocation is shown in Figure 127 [113].

Figure 8.9. DSRC spectrum allocation worldwide

In the U.S. the initial effort at standardizing DSRC radio technology took place in an American Society for Testing and Materials (ASTM) working group. In particular the FCC rule and order specifically referenced this document for DSRC spectrum usage rules. In 2004 this effort migrated to the IEEE 802.11 standard group as DSRC radio technology is essentially IEEE 802.11a adjusted for low overhead operations in the DSRC spectrum. Within IEEE 802.11 DSRC is known as IEEE 802.11p WAVE. IEEE 802.11p is not a standalone standard. It is intended to amend the overall IEEE 802.11 standard.

One particular implication of moving the DSRC radio technology standard into the IEEE 802.11 space is that now WAVE is fully intended to serve as an international standard applicable in other parts of the world as well as in the U.S. The IEEE 802.11p standard is meant to:

The IEEE 1609 family of standards defines the following parts (see [113]):

The primary architectural components defined by these standards are the On Board Unit (OBU), Road Side Unit (RSU) and WAVE interface.

The IEEE 1609.3 standard covers the WAVE connection setup and management. The IEEE 1609.4 standard sits right on top of the IEEE 802.11p and enables operation of upper layers across multiple channels, without requiring knowledge of PHY parameters. The standards also define how applications that utilize WAVE will function in WAVE environment. They provide extensions to the physical channel access defined in WAVE.

The third important standard related to vehicle communication is the J2735, Dedicated Short Range Communications (DSRC) Message Set Dictionary, maintained by the Society of Automotive Engineershttp://www.sae.org). This SAE Standard specifies a message set, its data frames and data elements specifically for use by applications intended to utilize the (DSRC/WAVE) communications systems. Although the scope of this Standard is focused on the message set and data frames of DSRC. This standard therefore specifies the definitive message structure and provides sufficient background information for the proper interpretation of the message definitions from the point of view of an application developer implementing the messages according to the DSRC standards.

It supports interoperability among DSRC applications through the use of standardized message sets, data frames and data elements. The message sets specified in J2735 define the message content delivered by the communication system at the application layer and thus defines the message payload at the physical layer. The J2735 message sets depend on the lower layers of the DSRC protocol stack to deliver the messages from applications at one end of the communication system (OBU of the vehicle) to the other end (a roadside unit). The lower layers are addressed by IEEE 802.11p, and the upper layer protocols are covered in the IEEE 1609.x series of standards.

The message set dictionary contains:

The most important message type is the basic safety message (often informally called “heartbeat” message because it is constantly being exchanged with nearby vehicles). Frequent transmission of “heartbeat” messages extends the vehicle’s information about the nearby vehicles complementing autonomous vehicle sensors. Its major attributes are the following [115]:

The other kinds of messages are the following (See [113]):

Safety use cases are those where a safety benefit exists when the vehicle enters into a scenario applicable to the use case. The following safety applications can be relevant with the help of V2V communication.

Figure 8.11. Hazardous location warning (source: http://car-to-car.org)

Figure 8.12. Privileging fire truck (source: http://car-to-car.org)

Figure 8.13. Reporting accidents (source: http://car-to-car.org)

Traffic Efficiency use cases are those meant to improve efficiency of the transportation network by providing information either to the owners of the transportation network or to the drivers on the network.

Figure 8.14. Intelligent intersection (source: http://car-to-car.org)

This category does not contain direct traffic related applications, rather comfort services. Many of these use cases interact more directly with the vehicle owner on daily basis providing entertainment or information on a regular basis. Others are transparent to the driver but still perform a valuable function such as increasing fuel economy.

The electronic payment applications result in convenient payments and avoiding congestions caused by toll collection and makes pricing more manageable and flexible.

The V2V communication system can support the currently available driver assistance systems. With help of the broadcasted vehicle parameters the adaptive cruise control and park pilot functions can be improved.

Figure 8.15. V2V based Cooperative-adaptive Cruise Control test vehicle. (Source: Toyota)

With special low-cost roadside units (RSU) the road sign recognition function can be supported and the reliability can be improved. In special cases it could offer safety functions in case of bridge or tunnel height or gate width.

Another important field of usage could be the policing and enforcement. Police could use the V2V communication in several ways especially checking the traffic rules such as:

For V2I applications the same communication standards are used as in V2V systems. The standards mentioned in Section 8.2 take into consideration the infrastructure and its requirements. In the architecture definitions the concept of the RSU has been elaborated.

Bluetooth technology is a wireless communications technology that is simple, secure, and can be found almost everywhere. You can find it in billions of devices ranging from mobile phones and computers to medical devices and home entertainment products. It is intended to replace the cables connecting devices, while maintaining high levels of security. Automotive applications of Bluetooth technology began with implementing the Hands Free Profile for mobile phones in cars. The development is coordinated by the Car Working Group (CWG) and is ongoing ever since 2000 by implementing different profiles and new features. The key features of Bluetooth technology are ubiquitousness, low power, and low cost. The Bluetooth Specification defines a uniform structure for a wide range of devices to connect and communicate with each other.

When two Bluetooth enabled devices connect to each other, is the so-called pairing. The structure and the global acceptance of Bluetooth technology means any Bluetooth enabled device, almost everywhere in the world, can connect to other Bluetooth enabled devices located in proximity to one another.

Connections between Bluetooth enabled electronic devices allow these devices to communicate wirelessly through short-range, creating ad hoc networks commonly known as piconets. Piconets are established dynamically and automatically as Bluetooth enabled devices enter and leave radio proximity meaning that you can easily connect whenever and wherever it's convenient for you. Each device in a piconet can also simultaneously communicate with up to seven other devices within that single piconet and each device can also belong to several piconets simultaneously. This means the ways in which you can connect your Bluetooth devices is almost limitless. There are applications that even do not require a connection establishment. It may be enough if the Bluetooth device’s wireless option is set to “visible” and “shown to all”, because fixed positioned Bluetooth access points may detect the movement of the Bluetooth device from one AP to another AP. This technology can easily be used for measuring the traffic flow.

A fundamental strength of Bluetooth wireless technology is the ability to simultaneously handle data and voice transmissions, which provides users with a variety of innovative solutions such as hands-free sets for voice calls, printing and fax capabilities, and synchronization for PCs and mobile phones, just to name a few.

The range of Bluetooth technology is application specific. The Core Specification mandates a minimum range of 10 meters or 30 feet, but there is no set limit and manufacturers can tune their implementations to provide the range needed to support the use cases for their solutions.

Range may vary depending on class of radio used in an implementation:

Bluetooth technology operates in the open and unlicensed industrial, scientific and medical (ISM) band at 2.4 to 2.485 GHz, using a spread spectrum, frequency hopping, full-duplex signal at a nominal rate of 1600 hops/sec. The 2.4 GHz ISM band is available and unlicensed in most countries. The most commonly used radio is Class 2 and uses 2.5 mW of power. Bluetooth technology is designed to have very low power consumption. This is reinforced in the specification by allowing radios to be powered down when inactive.

Bluetooth technology's adaptive frequency hopping (AFH) capability was designed to reduce interference between wireless technologies (such as WLAN) sharing the 2.4 GHz spectrum. AFH works within the spectrum to take advantage of the available frequency. This is done by the technology detecting other devices in the spectrum and avoiding the frequencies they are using. This adaptive hopping among 79 frequencies at 1 MHz intervals gives a high degree of interference immunity and also allows for more efficient transmission within the spectrum. For users of Bluetooth technology this hopping provides greater performance even when other technologies are being used along with Bluetooth technology. The AFH technology is shown in Figure 135 [118].

Figure 9.2. Collisions avoided using Adaptive Frequency Hopping

The newest Bluetooth Technology is Bluetooth 4.0 called Bluetooth Smart (Low Energy) Technology. While the power-efficiency of Bluetooth Smart makes it perfect for devices needing to run off a tiny battery for long periods, the most important attribute of Bluetooth Smart is its ability to work with an application on the smartphone or tablet you already own. Bluetooth Smart wireless technology features:

In automotive industry the primary usage of Bluetooth connects hands-free car systems which help drivers focus on the road. Another special usage is health monitoring e.g. people with diabetes can monitor their blood-glucose levels by using a Bluetooth glucose-monitoring device paired with the car. Also in-vehicle intelligent interfaces may provide e.g. vehicle related technical information to the driver via a Bluetooth channel. (Source: [119])

In V2I systems Bluetooth can be used to provide communication channel between the car and the traffic signal systems. Nowadays several manufacturers offer Bluetooth capable traffic control devices. It is capable for privileging the public transport at the intersections or measuring the traffic and pedestrian flows with the help of the electronic devices installed with Bluetooth radio (such as smartphones, tablets, navigation units etc.). These systems detect anonymous Bluetooth signals transmitted by visible Bluetooth devices located inside vehicles and carried by pedestrians. This data is then used to calculate traffic journey times and movements. It reads the unique MAC address of Bluetooth devices that are passing the system. By matching the MAC addresses of Bluetooth devices at two different locations, not only the accurate journey time is measured, privacy concerns typically associated with probe systems are minimized.

WiFi (Wireless Fidelity) or WLAN (Wireless Local Network) communication system is based on the Institute of Electrical and Electronics Engineers' (IEEE) 802.11 standards. The 802.11 family consists of a series of half-duplex over-the-air modulation techniques that use the same basic protocol. The most popular are those defined by the 802.11b and 802.11g protocols, which are amendments to the original standard. 802.11-1997 was the first wireless networking standard in the family, but 802.11b was the first widely accepted one, followed by 802.11a, 802.11g and a multi-streaming modulation 802.11n. Other standards in the family (c–f, h, j) are service amendments and extensions or corrections to the previous specifications. The IEEE 802.11p WAVE is intended to amend the overall IEEE 802.11 standard as mentioned in Section IEEE 802.11p (WAVE) 8.2.1.

802.11b and 802.11g use the 2.4 GHz ISM band. Because of this choice of frequency band, 802.11b and equipment may occasionally suffer interference from microwave ovens, cordless telephones and Bluetooth devices. 802.11b and 802.11g control their interference and susceptibility to interference by using direct-sequence spread spectrum (DSSS) and orthogonal frequency-division multiplexing (OFDM) signalling methods, respectively.

The “general” IEEE 802.11 standards (that used in consumer electronics) can support only infotainment applications in the vehicle. Only the IEEE 802.11p WAVE (DSRC) is capable for safe and reliable communications in V2X applications.

The most wide-spread mobile (cellular) network technology is GSM (Global System for Mobile communication). GSM was designed principally for voice telephony, but a range of bearer services was defined (a subset of those available for fixed line Integrated Services Digital Networks, ISDN), allowing circuit-switched data connections at up to 9600 bits/s. The technology behind the Global System for Mobile communication (GSMTM) uses Gaussian Minimum Shift Keying (GMSK) modulation, a variant of Phase Shift Keying (PSK) with Time Division Multiple Access (TDMA) signalling over Frequency Division Duplex (FDD) carriers. Although originally designed for operation in the 900 MHz band, it was soon adapted also for 1800 MHz. The introduction of GSM into North America meant further adaptation to the 800 and 1900 MHz bands. Over the years, the versatility of GSM has resulted in the specifications being adapted to many more frequency bands to meet niche markets.

At the time of the original system design, this rate compared favourably to those available over fixed connections. However, with the passage of time, fixed connection data rates increased dramatically. The GSM channel structure and modulation technique did not permit faster rates, and thus the High Speed Circuit-Switched Data (HSCSD) service was introduced in the GSM Phase 2+.

During the next few years, the General Packet Radio Service (GPRS) was developed to allow aggregation of several carriers for higher speed, packet-switched applications such as always-on internet access. The first commercial GPRS offerings were introduced in the early 2000s. Meanwhile, investigations had been continuing with a view to increasing the intrinsic bit rate of the GSM technology via novel modulation techniques. This resulted in Enhanced Data-rates for Global Evolution (EDGE), which offers an almost three-fold data rate increase in the same bandwidth. The combination of GPRS and EDGE brings system capabilities into the range covered by the International Telecommunication Unions IMT-2000 (third generation) concept, and some manufacturers and network operators consider the EDGE networks to offer third generation services.

In 1998, the ETSI (European Telecommunications Standards Institute) General Assembly took the decision on the radio access technology for the third generation cellular technology: wideband code-division multiple access, W-CDMA, would be employed. A dramatic innovation was attempted: a partnership project was formed with other interested regional standards bodies, allowing a common system to be developed for Europe, Asia and North America. The Third Generation Partnership Project (3GPP) was born. (Source: [120])

The Third Generation mobile cellular technology developed by 3GPP - known variously as Universal Mobile Telecommunications System (UMTS), Freedom of Mobile Multimedia Access (FOMA), 3GSM, etc., is based on wideband code division multiple access (W-CDMA) radio technology offering greater spectral efficiency and higher bandwidth than GSM. UMTS was originally specified for operation in several bands in the 2 GHz range. Subsequently, UMTS has been extended to operate in a number of other bands, including those originally reserved for Second Generation (2G) services. The UMTS radio technology is direct-sequence CDMA, each 10 ms radio frame is divided into 15 slots.

As a development of the original radio scheme, a high-speed download packet access (HSDPA, offering download speeds potentially in excess of 10 Mbit/s), and an uplink equivalent (HSUPA, also sometimes referred to as EDCH) were developed. Collectively the pair are tagged HSPA, and permit the reception of multimedia broadcast/multicast, interactive gaming and business applications, and large file download challenging traditional terrestrial or satellite digital broadcast services and fixed-line broadband internet access. The radio frames are divided into 2 ms subframes of 3 slots, and gross channel transmission rates are around 14 Mbit/s.

3GPP's radio access undergoes continuous development and the 'long-term evolution' (LTE) exercise aims to extend the radio technology to keep UMTS highly competitive to potential rival technologies, with data rates approaching 100 Mbit/s by the end of the decade. (Source: [121])

Short range radio means an older technology, which is wide-spread in case of public transport vehicles. They have been installed with a short range radio transmitter which works on a lower ISM band (such as 433 or 868 MHz). It can broadcast an identifier which can be received by the traffic control systems’ roadside beacon and thus the public transport vehicles can be prioritized in the intersections or by the stops.

The safety applications aim to decrease the number of accident by prediction and notifying the drivers of the information obtained through the communications between the vehicles and sensors installed on the road.

Figure 9.3. Example safety applications with the integration of DSRC and roadside sensors (Source: http://www.toyota-global.com)

The typical safety applications could be the following:

The efficiency applications can support the better utilization of the roads and intersections. These functions can operate locally at an intersections or a given road section, or in an optimal case on a large network, such as a busy downtown. It is important to note that the efficiency applications also have a beneficial effect on safety in most cases.

The following typical applications can enhance the traffic efficiency:

Figure 9.4. Dynamic traffic control supported by DSRC (Source: http://www.car-to-car.org/)

The number plate recognition serves as base for the payment applications, which is well-tried and reliable camera-based technology. The payment applications could be the following:

The information services can be typically the conventional variable traffic signs or temporary road signs supplemented with a DSRC beacon.

The main reason of the installation of a Fleet Management System is the expected decrease in the overall operating costs of the company. Though the establishment of such a system requires one-time costs of investment and its operation also imply costs, but these expenses are counterbalanced by the savings. The cost reductions arising from the more effective operation are typically the followings:

Reduction of operation costs thanks to the increase in vehicle utilization and optimal route planning.

The Fleet Management System provides the possibility of the maximal utilization of the vehicle parts’ lifetime by the complex monitoring of the vehicle. Simultaneously it warns for the necessity of the replacement and supports the logistics solutions also.

The system may improve the quality of the estimation of the fuel norm through the measurement of the real consumption.

The possibility of on-line vehicle tracking is an ideal tool for the optimization of the logistic processes. Not only the route of the goods can be determined, but the vehicle movements can be optimized. The improvement in logistics costs could be the followings:

Improve efficiency of freight transport

Decrease of extra charges caused by the delays.

Significant decrease in vehicle stop time can be reachable resulting in cost reduction with keeping the same transportation performance.

Monitoring helps forcing back the illegal utilization of the company’s vehicle.

Another requirement is to provide the safety of the freight through using Fleet Management System. The data acquired make possible to follow or retrace the vehicle events. With using on-line system it is possible to intervene in critical situations and to monitor the keeping the traffic regulations.

The following section presents the kind of functionality and the system architecture that can satisfy the above mentioned requirements.

Initially the Fleet Management Systems were introduced by the international transport companies with heavy commercial vehicles (large goods vehicle). These early systems have collected only the GPS positions and sent it by SMS to the central server. With the reduction of costs the FMS solutions have broken into the segment of the smaller commercial vehicles (light commercial vehicles). Nowadays the FMS is used almost in all vehicle segments, typically in the following ones:

large goods vehicle (commercial vehicles over 3.5 tons)

light commercial vehicles (commercial vehicles not more than 3.5 tons)

passenger vehicles

construction and agricultural machinery

Data acquisition and data handling give the basis of Fleet Management Systems. Primarily the on-board system should register existing vehicle signals, still in several cases the system needs new or more detailed parameters. Several possibilities are available for measuring the physical parameters and acquiring identification data (driver, trailer etc.). Nowadays all commercial vehicles possess multiple CAN interfaces providing various signals.

Data processing includes the sampling of various sensor sources either dynamically or fixed time based. Sampling strategy determines the overall amount of data to be handled, that may require temporary storage and pre-processing (e.g. histogram generation) to lower communication costs.

There are two categories of Fleet Management Systems, depending on the data location. If there is a recording unit in the vehicle and the recorded data are processed and evaluated afterwards, it is called off-line Fleet Management System. When all the vehicles are connected on-line to a computer server over mobile internet, and real-time information and data evaluation is available, is called the on-line Fleet Management System. In the recent years the on-line systems have come into general use as the result of the evolution of the wireless communication technologies. Several communication possibilities have become reachable for fleet management systems. The generally used techniques are based on GSM networks mainly on packet-switched services (like GPRS). In the future UMTS technologies will expand the possibilities and data bandwidth will be no longer a bottleneck for any function.

The identification of the motor vehicle is an essential task in Fleet Management Systems. This function can be achieved in several ways. The general solution is to use the “natural” identifier of the GSM unit called IMEI number which can be connected to the vehicle’s license plate or the vehicle identity number (VIN) in the central server’s relational database.

Identification increases system safety by connecting the vehicle data to the driver. Driver identification is very important from several aspects: work time registration, responsibility issues in case of special events etc. Nowadays a lot of different solutions can be reached on the market such as proximity (RFID) cards, Dallas keys, but recently the smart card based digital tachographs opened new possibilities on this field.

Another highly important task is trailer identification which is necessary for cargo tracking. It can be realized via the spiral cable between the tractor and the trailer or with a wireless identification system.

Event-triggered alerts enable the system to indicate abnormal operations to the driver and to the system centre also. These functions generally monitor specified signals. In case these signals reach unexpected or dangerous value an event will occur. For example these events can be the sudden decrease of the fuel level, speeding, tyre pressure etc.

Specific characteristic of the alerts is that they are real-time to enable quick intervention.

Determining the vehicle’s location is usually performed by GNSS systems (see Section 3.8). The principles of the satellite-based navigation systems (called GPS) were developed in the United Sates for military navigation purposes. The GPS is a widespread and reachable positioning solution which is capable of determining 3D position, chronometry and measurement of velocity. The system uses satellite signals for the determination of the position, thus it ensures continuous measurement possibility in 0-24 hours on the whole World. The Fleet Management Systems usually use GPS receivers or combined GPS/GLONASS receivers.

The central system of the fleet management systems is a complex hardware and software system, which handles data acquisition, storage and evaluation tasks. The incoming data from the vehicles must be handled in safe and reliable way for avoiding data loss and unauthorized access. The central system generates reports, charts, alerts and handles the geographic tasks.

Since the front office is the interface to the staff operating the system, it has to provide the following functionalities: primarily the system operator should gather vehicle, route and driver data through customizable reports. In addition for the tasks of on-line fleet control it has to possess geographical algorithms, and visualization. Since data security is highly important in such systems, the authentication and authorization in the whole system including the back office and also the front office must be managed and designed carefully.

In the building of the on-board unit (Figure 153) the important aspects are a heavy-duty design (EMC protection, shake protection, fluctuation of environmental temperature, etc.) and modularity. Therefore one should use a system that is built up of individual units. The connection of these by a series communication connection is worth realizing for the sake of simplicity and easy expansion.

Figure 14.2. General architecture of the on-board unit

The on-board unit is made up of the following main units:

GSM/GPS module,

central unit,

human interface device,

diagnostic adapter,

I/O module,

power supply unit and background batteries.

The basic on-board units are often referred to as AVL. The abbreviation of AVL stands for Automatic Vehicle Locator[138], most commonly called as vehicle tracking device. There are GPS and/or GLONASS based positioning systems integrated together with an industrial GSM/UMTS modem, which sends the positioning information on-line to a central computer server. AVL devices may be extended with vehicle technical information like fuel tank level, engine revolution, fuel used, and so on.

By the on-line fleet management systems the data have to be transferred with high reliability and integrity.

At present the fleet management systems use the public GSM network for data transmission. There are three possible technologies for this task:

SMS based,

circuit-switched and

packet-switched.

Nowadays the packet-switched communication is used most frequently. The most widespread is the GPRS (General Packet Radio Service), and EGPRS (Enhanced GPRS) which ensures larger bandwidth. In the 3G networks the UMTS and HSDPA can be used, but these techniques haven’t come into general use in fleet management systems because of the low covering and high prices of the devices.

The advantages of the packet switched technologies are the following:

continuous connection,

larger bandwidth,

data amount based costs,

low prices.

The SMS based data transmission can be a backup in case of GPRS service’s unavailability and it can be used for special purposes, for example sending alerts directly to mobile phones.

An example communication system can be built up by OSI model [143] as shown on Table 4.

There are several other possibilities elaborating the communication system. The first 3 layers are given when using GPRS networks. In the transport and session layers the UDP (User Datagram Protocol) or TCP (Transmission Control Protocol) can be used. UDP uses a simple, connectionless transmission model with a minimal protocol mechanism[144]. It has no handshaking process, thus any unreliability of the underlying network protocol devolves to the user's program. There is no guarantee of delivery, ordering, or duplicate protection. UDP provides checksums for data integrity, and port numbers for addressing different functions at the source and destination of the datagram. The TCP provides reliable transport layer above the IP, and also a session layer (TCP socket). The key features that set TCP [145] apart from User Datagram Protocol:

three-way handshake

Ordered data transfer,

Retransmission of lost packets,

Discarding duplicate packets,

Error-free data transfer,

Congestion/Flow control.

The presentation and application layers can be divided into two main groups:

simple byte-stream with a special packet structure,

markup language (e.g. XML) or other human-readable text format (e.g. JSON).

The first alternative is a modern, easy to handle solution which is primarily used in web-based communication systems. The advantages are the human-readability, the easy expandability and the off-the-shelf development tools. The main disadvantages are the verbosity and the unnecessary redundancy.

The byte-stream based protocols are widespread in Fleet Management Systems, because of the low-computing requirement and low amount of data. Nowadays the functions of the Fleet Management Systems are continuously expanding, thus the byte-stream based protocols become more difficult to handle by the developers and to integrate them into the modern web-based solutions.

The central system (Figure 154) based on a server system which consists of several databases and application servers. The on-board units connect to a communication server which implements the protocol. It has to deal with the following tasks:

data receiving,

data checks (syntactic, semantic, checksum),

data conversion and passing to the database,

acknowledging to the clients,

identification of the drivers,

sending the parameters of the OBU,

software update.

The communication server connects to a database system which contains three main databases:

transactional database,

data warehouse,

map database.

Figure 14.3. Architecture of the central system

The task of the transactional database is receiving the data from the communication server with high speed and reliability. The data is transferred to a data warehouse which executes several filtering and pre-processing procedures.

The users can access reports, charts and map data on a web-based user interface.

The user system consists of the user computers which are connected to the server system and the user software. It can be stand-alone software with a network database connection or web-application. Nowadays the web-based solutions are dominant (and can be called modern) because of the significant advantages (such as the easier software update and the minimal requirements on the client-side). It is important to note that in the recent years the web-based technologies are developing rapidly, thus made it possible to implement all of the necessary functions and ergonomic graphical interfaces for the effective work. A Fleet Management System can include many user functions [138]:

Vehicle maintenance

Vehicle tracking and diagnostic

Fuel management

Driver management

Tachograph management (Remote download)

Health and safety management

Basically the user system should display the geographical data of the vehicles on a map and the base vehicle data with easy filtering and ordering possibilities.

14.4.4.1. Data displaying, querying, reporting

Large amount of data in itself does not answer the questions of the fleet owner. Suitable business intelligence (BI) solutions are necessary for the data analysis, see [146]. Reporting and displaying system should be designed to support differently the employees with diverse roles considering the executive levels of the company. During the system design phase it is hard to determine all of the necessary and suitable reports. Thus it is important to give the opportunity of the flexible querying for future application.

The system should provide the following output formats:

diagram,

histogram,

statistics,

report.

As a result of the data analysis several outputs can be generated in the above mentioned formats. The generally used outputs are the following:

journey log,

vehicle parameter reports and histograms.

driver style classification,

fuel consumption,

event log,

alarm log,

These outputs can be queried for specific vehicles, drivers (or groups) in specific time periods. In the following figures several examples of the data displaying can be seen.

Figure 14.4. Diagram example

Figure 14.5. Example alarm log

Figure 14.6.  Vehicle parameters’ statistics example

Figure 14.7. Journey log example

14.4.4.2. Map display

For displaying the vehicles geographical data (actual position and route history) a digital map database and map display engine (collectively referred to as Geographical Information System – GIS) is needed.

The map database stores objects such as roads, lanes, elevation etc. and geocoding information to determine the country, the settlement and the street with the house number.

Basically there are two methods used to store mapping references in a GIS database and to display the map: raster images and vector. The latter technology used geometrical primitives such as points, lines, curves, and shapes or polygons based on mathematical expressions to represent the map objects and support the generation of the map image. Contrary to the raster (or bitmap) image it provides the ability of the clear magnification.

There are several companies offer GIS solutions, technologies and services such as ESRI, Autodesk, ERDAS, MapInfo, Bentley Systems, Intergraph etc.

Figure 14.8. Vector map example

There are several web mapping systems also available for the internet users and more of them provide an API to support the usage in map-based web services. In recent years these services began to gain ground in Fleet Management Systems, because they offers cost-effective licenses, free development platforms and continuously expanding feature list. The most popular services are Google Maps, OpenStreetMap and Bing Maps.

Figure 14.9. Google Maps based FMS example