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The bus sector is currently lagging behind when it comes to implementing autonomous systems for improved vehicle safety. However, in cities such as London, public transport strategies are changing, with requirements being made for advanced driver-assistance systems (ADAS) on buses. This study discusses the adoption of ADAS systems within the bus sector. A review of the on-road ADAS bus trials shows that passive forward collision warning (FCW) and intelligent speed assistance (ISA) systems have been successful in reducing the number of imminent pedestrian/vehicle collision events and improving speed limit compliance, respectively. Bus accident statistics for Great Britain have shown that pedestrians account for 82% of all fatalities, with three quarters occurring with frontal bus impacts. These statistics suggest that the bus forward collision warning system is a priority for inclusion in future vehicles to enhance the driver’s direct vision, and to increase reaction time for earlier brake application. Almost 80% of bus occupant casualties occurred in non-impact situations, mainly during acceleration/deceleration events. Therefore, care must be taken in implementing autonomous braking in buses, to ensure that it does not cause an increased number of deceleration events beyond the safe stability limits for passengers. Real on-road drive cycle data has shown that while instances of unsafe braking events do not occur regularly, there are instances of braking events that would present a hazard to both seated and standing passengers, therefore systems that would mitigate these issues would have real benefits to both passenger comfort and safety. During tests to simulate the use of the vehicle retarder for an autonomous braking system, deceleration rates largely remained safely within standee and seated passenger stability limits, whereas an emergency stop test showed a peak deceleration 3.5 times the limit of a standee supported by a vertical handrail, and 4 times the limit for a forward/backward facing seated passenger.

By exchanging basic traffic messages among vehicles for safety applications, a significantly higher level of safety can be achieved when vehicles and designated infrastructure-locations share their sensor data. While cameras installed in one vehicle can provide visual information for mitigating many avoidable accidents, a new safety paradigm is envisioned where visual data captured by multiple vehicles are shared and fused for significantly more optimized active safety and driver assistance systems. The sharing of visual data is motivated by the fact that some critical visual views captured by one vehicle or by an infrastructure-location are not visible or captured by other vehicles in the same environment. Sharing such data in real-time provides an invaluable new level of awareness that can significantly enhance a driver-assistance, connected vehicle, and/or autonomous vehicle's safety-system.

Machine vision based on automotive environment sensors is the enabling technology for advanced driver assistance systems and automated driving. Due to its important role, the reliability of environment sensing is highly safety relevant and has thus to be assessed and demonstrated during the development of the system. The main challenges associated with this task are low target error rates and the stochastic influence of different uncertain environmental conditions on the sensor performance. As a basis for the reliability assessment of environment sensors we introduce comprehensive performance metrics that allow a formal description of the uncertainties in a digital environmental model. Due to the influence of environmental conditions on the sensor performance, these metrics however are not constant but are random variables themselves. This leads to a hierarchical uncertainty structure including higher order uncertainties. To quantify the influence of the environmental conditions on the sensor reliability, we use a Bayesian hierarchical regression model. The utility of this method is demonstrated with a case study in which the influence of temperature on sensor reliability is examined. The results show that the proposed methodology is capable of identifying and quantifying the influence of the temperature on sensor performance. The introduced metrics and the proposed methodology are an important step towards a formalized reliability assessment of automotive environment sensing. In order to predict if the sensor reliability complies with the target error rates, the presented methodology has to be adapted and extended with additional stochastic methods.

The objective of this work is to test freely available system for active pedestrian protection. Tests are based on real fatal accidents that happened in the past with passenger cars that were not equipped with active safety systems. Tests have been conducted in order to evaluate what the real benefit of the active safety system is, and not to gain only a methodological prediction. The testing procedure was the first independent testing in the world which was based on real fatal pedestrian accidents. The aim of the tests is to evaluate the effectiveness of Volvo pedestrian detection system. An in-depth accident database contains about 300 fatal pedestrian traffic accidents in urban areas. Eighteen cases of pedestrians hit by the front end of the passenger vehicle were extracted from this database. Cases covering an average traffic scenario have been reconstructed to obtain detailed model situations for testing. Simulations of accidents have been made in PC Crash 10.0 using a multibody object and a mesh model of vehicles. Active safety testing scenario was built on the basis of reconstructed accidents with Volvo V40 cc and a new dummy simulating a pedestrian. Before the tests the dummy was evaluated in anechoic room to gain required radar reflection properties which would be the same as those of a human body. The movement of the dummy was driven by the autonomous ultraflat overrunable robot (UFO) for experimental advanced driver assistance systems (ADAS) testing and synchronized with Volvo motion by global positioning system (D-GPS) with high accuracy of motion.

The objective of this study is to develop a method that uses a combination of field data analysis, naturalistic driving data analysis, and computational simulations to explore the potential injury reduction capabilities of integrating passive and active safety systems in frontal impact conditions. For the purposes of this study, the active safety system is actually a driver assist (DA) feature that has the potential to reduce delta-V prior to a crash, in frontal or other crash scenarios. A field data analysis was first conducted to estimate the delta-V distribution change based on an assumption of 20% crash avoidance resulting from a pre-crash braking DA feature. Analysis of changes in driver head location during 470 hard braking events in a naturalistic driving study found that drivers' head positions were mostly in the center position before the braking onset, while the percentage of time drivers leaning forward or backward increased significantly after the braking onset. Parametric studies with a total of 4800 MADYMO simulations showed that both delta-V and occupant pre-crash posture had pronounced effects on occupant injury risks and on the optimal restraint designs. By combining the results for the delta-V and head position distribution changes, a weighted average of injury risk reduction of 17% and 48% was predicted by the 50th percentile Anthropomorphic Test Device (ATD) model and human body model, respectively, with the assumption that the restraint system can adapt to the specific delta-V and pre-crash posture. This study demonstrated the potential for further reducing occupant injury risk in frontal crashes by the integration of a passive safety system with a DA feature. Future analyses considering more vehicle models, various crash conditions, and variations of occupant characteristics, such as age, gender, weight, and height, are necessary to further investigate the potential capability of integrating passive and DA or active safety systems.

Traditionally, an in-vehicle map consists of only one type of data, tailored for a single user function. For example, the navigation maps contain spatial information about the roads. On the other hand, a map built for adaptive cruise control use consists of the detected vehicles and their properties. In autonomous vehicle research, the maps are often built up as an occupancy grid where areas are classified as passable or impassable. Using these kinds of maps separately, however, is not enough to support the traffic safety enhancing and advanced driver assistance systems of today and tomorrow.Instead of using separate systems to handle individual safety or planning tasks, information could be stored in one shared map containing several correlated layers of information. Map information can be collected by any number of different sensor devices, and fusion algorithms can be used to enhance the quality of the information. User functions that base their decisions on the multi-layered map can then retrieve any subset of the stored information making them scalable in terms of processor and memory use.The advantages of using a shared multi-layer spatial data storage are several:Sensors and user functions are decoupled. This can make it easier and more cost efficient to implement additional functions.Data quality is enhanced. Since fusion techniques can be used to generate estimates of physical properties from several sensors, the fused data is based on all available information.Using models that describe a certain entity, properties that are not even measured can be estimated by the system.This work describes an experimental semi-autonomous ground vehicle system, where on-line generated maps containing multiple layers of information are used for obstacle avoidance and planning of a suitable path between waypoints. The system is primarily simulated using physical vehicle models in a suitable environment, but limited real-world experiments with a subset of functions are also performed.

Future driver assistance systems will not only monitor the current traffic situation, but actively assist the driver in emergencies. Autonomous intervention in vehicle dynamics will increasingly help keep the vehicle under control, even in difficult operating situations. A rapid and intelligent braking system is one of the foundations for advancing the next generation of driver assistance systems. Siemens VDO sees its electronic wedge brake (EWB) brake-by-wire technology as the answer to future vehicle chassis safety, weight, reliability, and space requirements.

In this paper, we present a two-level driver model for the use in real-time vehicle dynamics applications. On the anticipation level of this model, nominal trajectories for the path and the speed profile of the vehicle along a given course are determined by reducing the driving task to a parametric optimal control problem and using an efficient direct collocation method for its solution. Typical optimality criteria and control-state constraints serve to depict driving properties of different driver types. On the stabilization level, a nonlinear position controller guides the full vehicle dynamics model along the prescribed trajectories in real-time. This synthetic driver model allows easy implementation of different driving strategies to simulate a wide range of driver types and vehicles. The expediency of the proposed model is shown by comparing simulation results with measured data from several drivers performing ISO double lane changes with a passenger car. INTRODUCTION Main tasks in the development of modern motor vehicles are the increase of driving safety and comfort as well as the relief of the driver by driver assistance systems. To guarantee reliability and robust design of the developed vehicle controllers, it is necessary to investigate the performance of the vehicle-controller system in major parts of the dynamic spectrum which is realized in practical driving situations. To meet the demands of all eligible drivers, handling studies must be carried out over a broad range of different driver types. Nowadays, software for the numerical simulation of the full car dynamics is indispensable for the development of vehicles and vehicle dynamics controllers. Besides virtual prototyping and conceptual design in the computer, vehicle dynamics simulation programs are employed for real-time applications in Hardware(HIL) und Software-in-the-Loop (SIL) environments. Realistic simulation results do not only require a comprehensive vehicle model and a detailed representation of the road conditions. For the investigation of the closed control loop of vehicle, driver and environment also a technical driver model is necessary which allows implementing driving maneuvers during normal operation and at the driving limits. In the following, we investigate an optimal control based model to depict the driver’s properties in vehicle guidance. Its application in real-time vehicle dynamics simulation is investigated and simulation results are compared to measurements from driving tests. FULL VEHICLE DYNAMICS SIMULATION For the computation of the full vehicle dynamics in real-time a simulation model is needed which realistically depicts the vehicle system behavior and requires little computational time. The vehicle model implemented in the vehicle dynamics program veDYNA [15] is a generic, fully parameterized multi-body system for the basic vehicle. The nonlinear elasto-kinematics of almost arbitrary axle types can be depicted either by kinematic look-up tables or by detailed geometric models, including the respective control arms, drag links, subframes and bushings. In addition, partial models are employed to account for intrinsic vehicle dynamics, such as of the drive train, the steering mechanism, and the tires (cf. Fig. 1). Figure 1: Multi-body model in veDYNA including steering system and geometrical axle model. Custom methods for treating multi-body systems use the descriptor form of the equations of motion and yield a system of differential-algebraic equations of index 3. The choice of suitable generalized coordinates in veDYNA, however, eliminates algebraic constraints and reduces the differential-algebraic system to ordinary differential equations. Due to their stiffness the numerical integration is performed with a semi-implicit Euler scheme which allows stable, real-time capable integration of the vehicle's equations of motion for step sizes up to several milliseconds [12]. For the representation of the vehicle environment the veDYNA Road model is used which allows depicting almost arbitrary road layouts with high accuracy [3, 16]. In this model, the horizontal course can be constructed synthetically according to a unit construction system or by specifying spatial road coordinates. The height profile allows segments with vanishing or constant slopes to be joined smoothly with arched pieces. For the road surface characteristic, geometric disturbances, such as potholes or rail tracks, may be depicted as well as different weather conditions and stochastic roughness. Figure 2: veDYNA Road layout for the Formula 1 course in Monza [3]. Besides openand closed-loop controls realistic driving maneuvers can be implemented with the veDYNA Driver [14, 16]. The versatile non-linear position controller which is described in the next section is able to guide the vehicle along arbitrary tracks and to handle the vehicle during demanding driving tasks. The driver controller shows to some extent natural human vehicle handling activities, but in a reproducible and adjustable way [8]. veDYNA allows the realistic simulation of the full vehicle dynamics in arbitrary driving situations. The program is employed among major car manufacturers and suppliers for rapid prototyping and parameter studies as well as for comfort and safety investigations on the PC. Applications in Hardwareand Software-in-the-Loop test benches include design, calibration and test of vehicle dynamics controllers, such as anti-lock braking and traction control systems, as well as reliability tests by endurance runs. OPTIMAL CONTROL BASED DRIVER MODEL According to Donges [4] human vehicular control can be separated into guidance and stabilization. Accordingly, a two-level driver model is suggested consisting of an anticipation level, where nominal trajectories for vehicle guidance are selected, and a stabilization level, where suitable control actions for adhering to the reference input variables are implemented. In the two-level driver model developed in the following, nominal trajectories for the path and the speed profile of the vehicle are determined by optimal control methods. This approach is motivated by the optimal control model of Baron, Kleinman and Levison [1] for the stabilization level. Accordingly, a “well-motivated, welltrained human operator behaves in a near-optimal manner subject to his inherent limitations and constraints, and his control task.” While common models for the driver anticipation level select the road center line and the maximum permissible vehicle speed as trivial reference variables, we formulate the driving task as a parametric optimal control problem. To depict the driver’s motivation, a combination of suitable optimality criteria, such as maximum traveled distance and minimum mean-square values of the vehicle’s deviation from the road center line and the lateral acceleration, is used. In this regard, the current work extends the driver model of Ehmann et al. [5, 17] for racing applications where time-optimal set variables are computed. The optimal control approach promises an easily parameterizable, synthetic driver model which is suitable to investigate the objective vehicle handling properties in the computer over a broad range of drivers, maneuvers, tracks and vehicles. On the stabilization level, a non-linear position control algorithm serves to guide the vehicle precisely along the prescribed trajectories for path and speed profile. Some controller parameters, such as preview, controller gain and steering delay, may be adjusted to represent human properties in vehicle guidance [8]. As compared to classic driver models from linear control theory, this approach provides vehicular control independent of the respective driving maneuver and offers a small number of meaningful driver parameters. For a detailed discussion of other driver controllers we refer to [2]. ANTICIPATION LEVEL The computation of nominal path and speed trajectories for vehicle guidance requires a vehicle dynamics model which can be treated with optimal control methods. Suitable for this application is the single track model [11] shown in Fig. 3. The latter is a planar vehicle model where front and rear wheels are condensed to one single wheel each and the center of gravity is situated on road height. Vertical dynamics, pitch and roll motions as well as displacements of the wheels relative to the vehicle body are neglected. Figure 3: Single track model [11]. y,r F x,r F β

Driver inattention and poor judgment are the major causes of motor vehicle accidents (MVA). Extensive research has shown that intelligent driver assistance systems can significantly reduce the number and severity of these accidents. The driver’s visual perception abilities are a key factor in the design of the driving environment. This makes image processing a natural candidate in any effort to impact MVAs. The vision system described here encompasses 3 major capabilities: (i) Lane Departure Warning (ii) Headway Monitoring and Warning (iii) Forward Collision Warning. This paper describes in detail the different warning features, the HMI (visual and acoustic) application design rules, and results of a study in which the system was installed in a commercial fleet and passenger vehicles.

The dual concepts of driver-vehicle cooperative operation and interaction were applied to a driver assistance device intended to lighten the operation load on the driver and reduce the occurrence of accidents. This addressed the concern that such devices would reduce driver motivation or cause other such problems. A full-scale driver assistance device capable of use throughout the expressway system was put into commercial production as the Honda Intelligent Driver Assistance System (HIDS) and installed on the Accord in October 2002. The lane keeping assistance employs the integration (cooperation) of driver operation and system support together with a new concept called the Human/Machine Interaction System (HMIS). The system has newly acquired approval from the Ministry of Land, Infrastructure and Transport. This article will report on the conceptual approach and substance of this system, and present the results of a survey on its effectiveness and influence on the driver.

Once the adaptive cruise control systems are already in the market in Japan and Europe, the evolution of these comfort systems is logically going towards implementing new additional functions and safety strategies in order to detect and actuate in case of emergency. This transition has to be done in clear and precise steps to assure an easy adaptation to each improvement. Driver assistance systems will play a major role in the future to minimise the risk and consequences of accidents and to increase the driving comfort level. The impact of such systems on traffic and society is briefly commented. This paper discusses the need of new driver assistance systems and a possible roadmap for them. After a short introduction of present Adaptive Cruise Control (ACC), and based on them, next possible functions are described. Afterwards the possible technical implementation of this new advanced functions is presented, discussing the different dangerous or ambiguous scenarios against the vehicle could be faced. The necessary inputs to recognise the situations unequivocally are identified in order to implement a satisfactory actuation of the vehicle. According to this requirements, the characteristics demanded to the system are extracted and a possible architecture is proposed. First tests carried out with a research prototype prepared by SEAT and the VW-Group Electronic Research Department are also reported. Finally, in the summary section, the main conclusions are highlighted.Once the adaptive cruise control systems are already in the market in Japan and Europe, the evolution of these comfort systems is logically going towards implementing new additional functions and safety strategies in order to detect and actuate in case of emergency. This transition has to be done in clear and precise steps to assure an easy adaptation to each improvement. Driver assistance systems will play a major role in the future to minimise the risk and consequences of accidents and to increase the driving comfort level. The impact of such systems on traffic and society is briefly commented. This paper discusses the need of new driver assistance systems and a possible roadmap for them. After a short introduction of present Adaptive Cruise Control (ACC), and based on them, next possible functions are described. Afterwards the possible technical implementation of this new advanced functions is presented, discussing the different dangerous or ambiguous scenarios against the vehicle could be faced. The necessary inputs to recognise the situations unequivocally are identified in order to implement a satisfactory actuation of the vehicle. According to this requirements, the characteristics demanded to the system are extracted and a possible architecture is proposed. First tests carried out with a research prototype prepared by SEAT and the VW-Group Electronic Research Department are also reported. Finally, in the summary section, the main conclusions are highlighted.

The development of electronic intelligence and the increasing knowledge of driving dynamics make intelligent vehicle systems available for series production, which substantially enhance the active safety of commercial vehicles. Through the implementation of advanced subsystems, which can be integrated into the basic electronic braking system, it will be possible to expand the possibilities of introducing assistance systems, which help and relieve the driver from stress in critical situations. The driver will be relieved of all duties which could divert his attention or cause severe stress. As a consequence, the active safety of commercial vehicles will be considerably increased. For the covering abstract see ITRD E119187.

Knowing the lane width, and vehicle position within it, are important parameters for the vehicle operator to be aware of at all times. Due to the fact that the driving environment is laid out for the human eyes, sensors have been developed which operate at or near the visible wave-length range that can play an important role in detecting the lane borders. Only systems able to cope autonomously with today's infrastructure promise to be successful in today's market. An extremely high market growth is predicted for such automonous driver assistance systems. Systems relying on infrastructure changes would never allow for a rapid growth rate. The majority of communities would not spend or do not have the funding necessary to equip the roads with, for example, telematic infrastructure. (A) For the covering abstract see ITRD E106371.

Existing driver information systems (DIS) include navigation, radio, telephone, television etc. The addition of computer functions like internet or word-processing turns DIS into car computing devices (CCD), often called CarPCs. Due to high mental and visual load of driving, solutions matching the human factors needs of the driving environment have to be developed. A usability study testing four different existing DIS was conducted. It is possible to transfer the results to future CCD. The visual channel should not be stressed by CCD. Applications running in car have to use acoustic and haptic channels for information transfer between driver and system. (A) For the covering abstract see ITRD E106371.

Developing a knowledge-based predictive driver-aid system requires a strategy that meets the needs and goals, as well as rigorous methods. This paper presents the strategy adopted and the first results obtained in the development of the Navigation Aided Intelligent Cruise Control (NAICC) system. Once the vehicle is located on the route, this new copilot can: (1) define the travel direction and the vehicle speed; (2) determine the distance to the next bend and its characteristics; (3) predict the optimal speed to negotiate the next bend considering the road profile. the constraints given by the driver and the information provided by the sensors mounted on the vehicle. Then, used in the warning mode ("Driver Alarm Mode"), NAICC informs the driver of the danger of the situation (excessive speed) or, in the intervention move ("Velocity Control Mode"), NAICC adapts the speed to the predicted reference. In this latter operating mode, NAICC can be considered as an advanced intelligent cruise control system. Real experiments were carried out with the laboratory test car to validate each module described. (A) For the covering abstract see ITRD E106371.

A method for detecting drivers’ intentions is essential to facilitate operating mode transitions between driver and driver assistance systems. We propose a driver behavior recognition method using Hidden Markov Models (HMMs) to characterize and detect driving maneuvers and place it in the framework of a cognitive model of human behavior. HMM-based steering behavior models for emergency and normal lane changes as well as for lane keeping were developed using a moving base driving simulator. Analysis of these models after training and recognition tests showed that driver behavior modeling and recognition of different types of lane changes is possible using HMMs.

This report examines the role that Global Positioning System (GPS)- based automobile navigation systems will have in driver assistance systems. It first gives an overview of global positioning systems. This is followed by a discussion of map databases and how they contribute information towards vehicle navigation. Several driver assistance applications that use GPS and map databases are then discussed.

This paper examines the requirements of adaptive cruise control (ACC) systems and the role of infrared detector technology in meeting the requirements of ACC systems. The first part of the paper looks at these requirements, while the second half describes advanced driver assistance systems that are equipped with infrared and supplementary technologies.

This paper first summarizes progress to date in truck safety-related technology. It then examines opportunities for advanced collision techniques, including electronic braking, object detection, and driver monitoring. The authors discuss the differences between active and passive safety systems. The paper concludes with a discussion on governmental roles and the need for consistent standards.

In this paper, the author analyzes automotive safety issues with respect to the application of electronic technology and presents several development targets for the future. The functions expected of the automobile have been changing steadily in Japan. Environmental concerns, for instance, have taken on increased importance and must be factored into vehicle design during the development process. Safety and security issues also impose rigorous requirements on vehicle design. Various efforts are under way to apply electronic technology to driver support systems and devices with the aim of enhancing safety, though numerous problems remain to be solved.

Reviews General Motors-inspired IVHS concepts for advanced driver information and vehicle control systems as well as the experimental Pathfinder and TravTek programs.