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PROSPECT (Proactive Safety for Pedestrians and Cyclists) is a collaborative research project involving most of the relevant partners from the automotive industry (including important active safety vehicle manufacturers and tier-1 suppliers) as well as academia and independent test labs, funded by the European Commission in the Horizon 2020 research program. PROSPECT's primary goal is the development of novel active safety functions, to be finally demonstrated to the public in three prototype vehicles. A sound benefit assessment of the prototype vehicle's functionality requires a broad testing methodology which goes beyond what has currently been used. Since PROSPECT functions are developed to prevent accidents in intersections, a key aspect of the test methodology is the reproduction of natural driving styles on the test track with driving robots. For this task, data from a real driving study with subjects in a suburb of Munich, Germany was used. Further data from Barcelona will be available soon. The data suggests that intersection crossing can be broken down into five phases, two phases with straight deceleration / acceleration, one phase with constant radius and speed turning, and two phases where the bend is imitated or ended. In these latter phases, drivers mostly combine lateral and longitudinal accelerations and drive what is called a clothoid, a curve with curvature proportional to distance travelled, in order to change lateral acceleration smoothly rather than abrupt. The data suggests that the main parameter of the clothoid, the ratio distance travelled to curvature, is mostly constant during the intersections. This parameter together with decelerations and speeds allows the generation of synthetic robot program files for a reproduction of natural driving styles using robots, allowing a much greater reproducibility than what is possible with human test drivers. First tests show that in principle it is possible to use the driving robots for vehicle control in that manner; a challenge currently is the control performance of the robot system in terms of speed control, but it is anticipated that this problem will be solved soon. Further elements of the PROSPECT test methodology are a standard intersection marking to be implemented on the test track which allows the efficient testing of all PROSPECT test cases, standard mobile and light obstruction elements for quick reproduction of obstructions of view, and a concept for tests in realistic surroundings. First tests using the PROSPECT test methodology will be conducted over the summer 2017, and final tests of the prototype vehicles developed within PROSPECT will be conducted in early 2018
The advent of active safety systems calls for the development of appropriate testing methods. These methods aim to assess the effectivity of active safety systems based on criteria such as their capability to avoid accidents or lower impact speeds and thus mitigate the injury severity. For prospective effectivity studies, simulation becomes an important tool that needs valid models not only to simulate driving dynamics and safety systems, but also to resolve the collision mechanics. This paper presents an impact model which is based on solving momentum conservation equations and uses it in an effectivity study of a generic collision mitigation system in reconstructed real accidents at junctions. The model assumes an infinitely short crash duration and computes output parameters such as post-crash velocities, delta-v, force directions, etc. and is applicable for all impact collision configurations such as oblique, excentric collisions. Requiring only very little computational effort, the model is especially useful for effectivity studies where large numbers of simulations are necessary. Validation of the model is done by comparison with results from the widely used reconstruction software PC-Crash. Vehicles involved in the accidents are virtually equipped with a collision mitigation system for junctions using the software X-RATE, and the simulations (referred to as system simulations) are started sufficiently early before the collision occurred. In order to assess the effectivity, the real accident (referred to as baseline) is compared with the system simulations by computing the reduction of the impact speeds and delta-v.
For more than a decade, ADAC accident researchers have analysed road accidents with severe injuries, recording some 20,000 accidents. An important task in accident research is to determine the causative factors of road accidents. Apart from vehicle engineering and human factors, accident research also focuses on infrastructural and environmental aspects. To find out what accident scenarios are the most common in ADAC accident research and what driver assistance systems can prevent them, our first task was to conduct a detailed accident analysis. Using CarMaker, we performed a realistic simulation of accident scenarios, including crashes, with varying parameters. To begin with, we made an initial selection of driver assistance systems in order to determine those with the greatest accident prevention potential. One important finding of this study is that the safety potential of the individual driver assistance systems can actually be examined. It also turned out that active safety offers even much more potential for development and innovation than passive safety. At the same time, testing becomes more demanding, too, as new systems keep entering the market, many of them differing in functional details. ADAC will continue to test all driver assistance systems as realistically as possible so as to be able to provide advice to car buyers. Therefore, it will be essential to develop and improve test conditions and criteria.
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.
For the estimation of the benefit and effect of innovative Driver Assistance Systems (DAS) on the collision positions and by association on the accident severity, together with the economic benefit, it becomes necessary to simulate and evaluate a variety of virtual accidents with different start values (e.g. initial speed). Taken into account the effort necessary for a manual reconstruction, only an automated crash computation can be considered for this task. This paper explains the development of an automated crash computation based on GIDAS. The focus will be on the design of the virtual vehicle models, the method of the crash computation as well as exemplary applications of the automated crash computation. For the first time an automated crash computation of passenger car accidents has been realized. Using the automated crash computation different tasks within the field of vehicle safety can be elaborated. This includes, for example, the calculation of specific accident parameters (such as EES or delta-V) for various accident constellations and the estimation of the economic benefit of DAS using IRFs (Injury Risk Functions).
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.
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 presentation deals with the simulation tool rateEFFECT which intends to answer the following questions: Which active safety systems should be developed to maximize safety benefit in real traffic accidents? What is the effectiveness of a specific active safety system in the real world? How many casualties could be avoided by such a system? It is shown that a lot of information is required to simulate existing accidents in order to estimate ADAS effects. This particularly includes numerical values for the pre-crash and in-crash phase. The database GIDAS provides a required minimum number of these parameters for a statistically significant sample.
The utilisation of secondary-safety systems to protect occupants has attained a very high level over the past decades. Further improvements are still possible, but increasingly minor progress is only to be had with a high degree of effort. Thus, a key aspect must be the impact to overall safety in an accident. If reliable information is available on an imminent crash, measures already taken in the pre-crash phase can result in a significantly great influence on the outcomes of the crash. With this background preventive measures are the key to a sustainable further reduction of the figures of crash victims on our roads. This paper aims to show a preventive approach that can contribute to lessening the consequences of a crash by creating an optimum interaction of measures in the fields of primary and secondary safety. To further enhance vehicle safety, driver assistant systems are already available that warn the driver of an imminent front-to-rear-end crash. The next step is to support him in his reactions or if he fails to react sufficiently, to even initiate an automatic braking when the crash becomes unavoidable. Automatic pre-crash braking can, in an ideal situation, fully prevent a crash or can significantly reduce the impact speed and thus the impact energy (and the severity of the accident). If a vehicle is being braked in the pre-crash phase, the occupants are already being pre-stressed by the deceleration. The information available about the imminent crash can be used to activate the belt tensioners and likewise other secondary safety systems in the vehicle right before the impact. The pre-crash deceleration also causes the front of the vehicle to dip. Conventional crash tests do not take this specific impact situation into consideration. This is why, for example, the influences of the pre-crash displacements of the occupants are not recorded in the test results. Furthermore, a reproducible representation of the benefit of the vehicle safety systems which prepare the occupants for the imminent impact is not possible. In order to demonstrate the functions of automated pre-crash braking and to investigate the differences during the impact as a consequence of the altered occupant positions as well as the initiation of force and deformations of the vehicle front, DEKRA teamed up with BMW to carry out a joint crash test with the latest BMW 5 series vehicle. It involved the vehicle braking automatically from a starting test speed of 64 km/h (corresponding to the impact speed set by Euro NCAP) down to 40 km/h. The test was still run by the intelligent drive system of the crash test facility. This required several modifications to be made to the test facility as well as to the vehicle. The paper will describe and discuss some relevant results of the crash test. In addition, the possible benefits of such systems will also be considered. The test supplemented the work of the vFSS working group (vFSS stands advanced Forward-looking Safety Systems).