Abstract
Accident analysis and reconstruction classically based on tracks recorded during the scene investigations of an accident. In a case ideal for the post reconstruction of the accident, these traces can greatly help reconstruction of the accident process. However, in most cases, it is not possible to record all necessary traces, so it is not possible to use these data later. There are tools from the 1950s - with continuously evolving features, - that could partially respond this problem, for example by continuous monitoring certain vehicle dynamics parameters, and by recording these parameters at a time of an accident occurring, useful information can be gained about the vehicle’s state of motion. The data provided by accident data recorders can thus provide useful information on accidents involving traditional vehicles, but their significance is growing especially with the spread of highly automated and autonomous vehicles, where the control of the vehicle can, in certain circumstances, be transferred to the vehicle. In this article we will present the data that can be used for modern accident analysis, then we will investigate accident data recorded during crash tests, which were collected from the vehicles own control units with EDR functions, and by the post-fitted event data recorders during collision attempts. On the basis of our experiences and conclusions, we will make suggestions on the range of optimal data from the point of view of accident analysis, on the recording of these data, paying particular attention to autonomous vehicle specific solutions.
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Acknowledgement
The project has been supported by the European Union, co-financed by the European Social Fund. EFOP-3.6.2-16-2017-00002.
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Pinter, K., Szalay, Z. (2018). Comparison of Data Required for Accident Reconstruction Based on Crash Test. In: Jármai, K., Bolló, B. (eds) Vehicle and Automotive Engineering 2. VAE 2018. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-75677-6_41
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DOI: https://doi.org/10.1007/978-3-319-75677-6_41
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