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Advancing active safety towards the protection of vulnerable road users: the PROSPECT project
(2017)
Accidents involving Vulnerable Road Users (VRU) are still a very significant issue for road safety. According to the World Health Organisation, pedestrian and cyclist deaths account for more than 25% of all road traffic deaths worldwide. Autonomous Emergency Braking Systems have the potential to improve safety for these VRU groups. The PROSPECT project (Proactive Safety for Pedestrians and Cyclists) aims to significantly improve the effectiveness of active VRU safety systems compared to those currently on the market by expanding the scope of scenarios addressed by the systems and improving the overall system performance. The project pursues an integrated approach: Newest available accident data combined with naturalistic observations and HMI guidelines represent key inputs for the system specifications, which form the basis for the system development. For system development, two main aspects are considered: advanced sensor processing with situation analysis, and intervention strategies including braking and steering. All these concepts are implemented in several vehicle prototypes. Special emphasis is put on balancing system performance in critical scenarios and avoiding undesired system activations. For system validation, testing in realistic scenarios will be done. Results will allow the performance assessment of the developed concepts and a cost-benefit analysis. The findings within the PROSPECT project will contribute to the generation of state -of-the-art knowledge, technical innovations, assessment methodologies and tools for advancing Advanced Driver Assistance Systems towards the protection of VRUs. The introduction of a new generation safety system in the market will enhance VRU road safety in 2020-2025, contributing to the "Vision Zero" objective of no fatalities or serious injuries in road traffic set out in the Transport White Paper. Furthermore, the test methodologies and tools developed within the project shall be considered for the New Car Assessment Programme (Euro NCAP) future roadmaps, supporting the European Commission goal of halving the road toll in the 2011-2020 timeframe.
Do learner gain sufficient braking capabilities at the end of education for collision avoidance?
(2013)
The paper describes a test design to evaluate the braking behaviour in the course of the driver education. The results show that the braking capabilities increased during the driver education and the learning effects are the same for males and females. The evaluation limit is set to 6 m/s-². At the beginning of education, 50% of the drivers do not reach this limit, although the driver education car is equipped with an emergency brake assist, which is regularly installed in all vehicles since 2009. After the education, 100% of the drivers can reach the limit. The results are mapped to a collision avoidance scenario.
The main focus of the benefit estimation of advanced safety systems with a warning interface by simulation is on the driver. The driver is the only link between the algorithm of the safety system and the vehicle, which makes the setup of a driver model for such simulations very important. This paper describes an approach for the use of a statistical driver model in simulation. It also gives an outlook on further work on this topic. The build-up process of the model suffices with a distribution of reaction times and a distribution of reaction intensities. Both were combined in different scenarios for every driver. Each scenario has then a specific probability to occur. To use the statistical driver model, every accident scene has to be simulated with each driver scenario (combinations of reaction times and intensities). The results of the simulations are then combined regarding the probabilities to occur, which leads to an overall estimated benefit of the specific system. The model works with one or more equipped participants and delivers a range for the benefit of advanced safety systems with warning interfaces.
The term driver assistance systems in the chapter title shall be understood to include vehicle automation. This chapter starts with a homogeneous and consistent classification and nomenclature of all kinds of driver assistance systems known and under discussion today (including vehicle automation). It thereby builds upon familiar classification schemes by the German Federal Highway Research Institute (BASt) and the standardization body SAE international. Detailed evaluation of the German legal situation for driver assistance systems and vehicle automation is provided in the following Sect. 2. In Sect. 3, an overview is given on the legal system in the US to reveal aspects relevant for vehicle automation. This is intended as initial information for those not acquainted to the US legal system which has been the first to regulate automation in several federal states. Finally, in Sect. 4, the current rating scheme of the European New Car Assessment Programme (EuroNCAP) is presented in comparison to legal instruments. The model of a consumer protection based approach proves to be a flexible instrument with great advantages in promoting new technologies. Technical vehicle regulations on the other hand rule minimum requirements. Both approaches are needed to achieve maximum vehicle safety.
The Swedish National Road Administration (SNRA), the Japanese Automobile Research Institute (JARI) and the Federal Highway Research Institute (BASt) are co-operating in the International Harmonized Research Activities on Intelligent Transportation Systems (IHRA-ITS). Under this umbrella a joint study was conducted. The overall objective of this study was to contribute to the definition and validation of a "battery of tools" which enables a prediction and an assessment of changes in driver workload due to the use of in-vehicle information systems (IVIS) while driving. In this sense \"validation\" means to produce empirical evidence from which it can be concluded that these methods reliably discriminate between IVIS which differ in terms of relevant features of the HMI-design. Additionally these methods should also be sensitive to the task demands imposed on the driver by the traffic situation and their interactions with HMI-design. To achieve these goals experimental validation studies (on-road and in the simulator) were performed in Sweden, Germany and Japan. As a common element these studies focused on the secondary task methodology as an approach to the study of driver workload. In a joint German-Swedish on-road study the Peripheral Detection Task (PDT) was assessed with respect to its sensitivity to the complexity of traffic situations and effects of different types of navigation systems. Results show that the PDT performance of both the German and the Swedish subjects reflects the task demands of the traffic situations better than those of the IVIS. However, alternative explanations are possible which will be examined by further analyses. Results of this study are supplemented by the Japanese study where informational demands induced by various traffic situations were analysed by using a simple arithmetic task as a secondary task. Results of this study show that relatively large task demands can be expected even from simple traffic situations.
Nowadays human-created systems are increasing in complexity due to the interaction of humans and technology. Especially road traffic systems are composed of multitudinous resources (e.g. personnel, vehicles, organizations, etc.), which make it even harder to anticipate the positive and negative effects on safety. One key in achieving a significant reduction of fatalities is seen in driver assistant systems counterbalancing the lack of drivers' capabilities. But the actual outcome of implementing these sophisticated technologies especially on influencing driver's capabilities are yet unknown. Latest research exemplifies an increase of reaction times of drivers in case of dysfunctional driver assistant systems. This research paper applies STAMP/STPA (STAMP = systems-theoretic accident model and processes; STPA = systems-theoretic process analysis) to the German automobile traffic system focusing on the effects of driver assistant systems on drivers. By doing so, the potential hazards caused by technology can be identified.
The strong prevalence of human error as a crash causation factor in motorcycle accidents calls for countermeasures that help tackling this issue. Advanced rider assistance systems pursue this goal, providing the riders with support and thus contributing to the prevention of crashes. However, the systems can only enhance riding safety if the riders use them. For this reason, acceptance is a decisive aspect to be considered in the development process of such systems. In order to be able to improve behavioural acceptance, the factors that influence the intention to use the system need to be identified. This paper examines the particularities of motorcycle riding and the characteristics of this user group that should be considered when predicting the acceptance of advanced rider assistance systems. Founded on theories predicting behavioural intention, the acceptance of technologies and the acceptance of driver support systems, a model on the acceptance of advanced rider assistance systems is proposed, including the perceived safety when riding without support, the interface design and the social norm as determinants of the usage intention. Since actual usage cannot be measured in the development stage of the systems, the willingness to have the system installed on the own motorcycle and the willingness to pay for the system are analyzed, constituting relevant conditions that allow for actual usage at a later stage. Its validation with the results from user tests on four advanced rider assistance systems allows confirming the social norm and the interface design as powerful predictors of the acceptance of ARAS, while the extent of perceived safety when riding without support did not have any predictive value in the present study.
Accident research 2.0: New methods for representative evaluation of integral safety in traffic
(2013)
BMW has developed a procedure for rating Advanced Driver Assistance Systems (ADAS) benefits that integrates two distinct tools. The tool "S.A.F.E.R." is designed to analyze the pre-crash phase. The aim of S.A.F.E.R. is to simulate all relevant processes in sufficient detail to obtain reproducible estimates of key indicators (effectiveness, false positives, etc.). The relevant processes include not only traffic and vehicle dynamics, but also environmental and most importantly human factors. Representative distributions of factors and parameters are obtained by taking the stochastic variation of all relevant parameters into account in the simulations. The second tool, known as "ICOS", has been designed to provide a high-resolution, high-fidelity description of crash phase dynamics. If one converts the outputs of stochastic simulation into inputs for crash dynamics, the result is a comprehensive description of exactly how a safety system can reduce injuries. Applications currently focus on high-fidelity simulation of individual crashes in order to enhance our understanding and optimization of connected safety systems. An integrated simulation process thus allows an exact prediction of the effectiveness in individual cases in terms of injury severity. The development and rating of integral safety need to reflect the true efficiency in the field. The integrated approach described here could provide a valid and reproducible basis for rating connected systems of active and passive safety. In particular, "virtual experiments" using a traffic-based approach and incorporating models of all relevant processes constitute an essential element of the approach.
The project UR:BAN "Cognitive assistance (KA)" aims at developing future assistance systems providing improved performance in complex city traffic. New state-of-the-art panoramic sensor technologies now allow comprehensive monitoring and evaluation of the vehicle environment. In order to improve protection of vulnerable road users such as pedestrians and cyclists, a particular objective of UR:BAN is the evaluation and prediction of their behaviour and actions. The objective of subproject "WER" is development support by providing quantitative estimates of traffic collisions at the very start and predict potential in terms of optimized accident avoidance and reduction of injury severity. For this purpose an integrated computer simulation toolkit is being devised based on real world accidents (GIDAS as well as video documented accidents), allowing the prediction of potential effectiveness and future benefit of assistance systems in this accident scenario. Subsequently, this toolkit may be used for optimizing the design of implemented assistance systems for improved effectiveness.
The evaluation of the expected benefit of active safety systems or even ideas of future systems is challenging because this has to be done prospectively. Beside acceptance, the predicted real-world benefit of active safety systems is one of the most important and interesting measures. Therefore, appropriate methods should be used that meet the requirements concerning representativeness, robustness and accuracy. The paper presents the development of a methodology for the assessment of current and future vehicle safety systems. The variety of systems requires several tools and methods and thus, a common tool box was created. This toolbox consists of different levels, regarding different aspects like data sources, scenarios, representativeness, measures like pre-crash-simulations, automated crash computation, single-case-analyses or driving simulator studies. Finally, the benefit of the system(s) is calculated, e.g. by using injury risk functions; giving the number of avoided/mitigated accidents, the reduction of injured or killed persons or the decrease of economic costs.