Sonstige
Over the last decades the number of traffic accident fatalities on German roads decreased by 77% down to 4968 in the year 2007. This positive development is due to optimisations of vehicle safety, roads and infrastructure and medical rescue issues. Up to now mostly the optimisations of secondary safety measures lead to this effect on vehicle safety. Since some years more and more driver assistance systems are available and lead to a further reduction of all accidents. These new systems are often comfort systems and have not primarily been developed to increase vehicle safety. In contrast to secondary safety systems primary safety systems are able to mitigate and avoid accidents. So in the future it is important to estimate the benefit of these systems in reducing accident numbers as well. Current benefit estimation methods mostly focus on a single system only and not on the combination of systems. In this paper a new method for a multivariate benefit estimation based on real accident data is developed. The paper describes the basic method to estimate the benefit of primary and secondary safety systems in combination. With the presented method the benefit will not be overestimated as it would be by a simple addition of the benefits of single systems. The model will be validated by a multivariate prospective benefit estimation of different vehicle safety systems in comparison to single benefit estimations of the same systems. For this the German In-Depth Accident Database is used. The results show the importance to implement the interactions of safety systems in the estimation process and rate the overestimation by a simple addition of the single system benefits. The validation includes primary and secondary safety systems in combination. The validation is done using more than 3500 real accidents which were initiated by cars. This sample out of the GIDAS database is representative for the current accident situation in Germany. The paper shows the necessity of a multivariate estimation of the benefit for existing and future safety systems.
The sequence of accident events can be classified by three essential phases, the pre-crash-sequence, the crash-sequence and the post-crash-sequence. The level of reliability of the information in the GIDAS-database (German In Depth Accident Study) is provided predominantly on the passive side. The period to evaluate active safety systems begins already in the pre-crash-sequence. The assessment of the potential of sensor- or communication-based active safety systems can only be accomplished by a detailed analysis of the pre-crash-phase. Hence the necessity to analyze the early period of the accident event in detail arises. This is possible with the help of the digital sketches of the accident site and the simulation of the accident by a simulation method of the VUFO GmbH. After simulating the pre-crash scenario it is possible to generate additional and standardized data to describe the pre-crash-sequences of an accident in a very high detail. These data are documented in a second database called the GIDAS Pre-Crash-Matrix (PCM). The PCM contains various tables with all relevant data to reproduce the pre-crash-sequence of traffic accidents from the GIDAS database until 5 seconds before the first collision. This includes parameters to describe the environment data, participant data and motion or dynamic data. This paper explains the creation of the PCM, the simulation itself and the contents and structure of the PCM. With this information of the pre-crash-sequence for various accident scenarios an improved benefit estimation and development of active safety systems can be made possible.
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 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.