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ATZ worldwide
Published in: ATZ worldwide 5/2017

01-05-2017 | Research

Artificial intelligence for developing an accident severity prediction function

Authors: Dr.-Ing. Andreas Meier, Prof. Dr.-Ing. Mark Gonter, Prof. Dr. habil. Rudolf Kruse

Published in: ATZ worldwide | Issue 5/2017

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Excerpt

The continuous improvement of safety is one the most complex tasks in vehicle development. Especially adaptive integrated safety systems may offer a high potential due to their accident severity dependent firing strategies. However, the precise prediction of accident severity is mostly a challenging algorithmic problem. At Volkswagen Corporate Research, this problem was solved in a dissertation with the help of artificial intelligence for the first time. The resulting solutions have been awarded with the Hermann Appel Prize of IAV in 2016. …
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Literature
[1]
Meier, A.: Verfahren zur vorkollisionären Prognose der zu erwartenden Unfallschwere von Fahrzeugfrontalkollisionen. Magdeburg, Otto-von-Guericke-University, PhD thesis, 2015
[2]
Kruse, R.; Borgelt, C.; Braune, C.; Mostaghim, S.; Steinbrecher, M.: Computational Intelligence. London: Springer-Verlag, 2016CrossRefMATH
[3]
Meier, A.; Gonter, M.; Kruse, R.: Approximationsverfahren Meier, A.; Gonter, M.; Kruse, R.für kollisionsbedingte Geschwindigkeitskurven. In: Proceedings of 23. Workshop Computational Intelligence (2013), Vol. 46, pp. 1–20
[4]
Worm, T.; Chiu, K.: Prioritized grammar enumeration: symbolic regression by dynamic programming. In: Gecco 13 Proceedings of the 15th annual conference on Genetic and evolutionary computation, pp. 1021-1028
[5]
Meier, A.; Gonter, M.; Kruse, R.: Symbolic Regression for Precrash Accident Severity Prediction. In: Hybrid Artificial Intelligence Systems (2014), pp. 133–144CrossRef
Metadata
Title
Artificial intelligence for developing an accident severity prediction function
Authors
Dr.-Ing. Andreas Meier
Prof. Dr.-Ing. Mark Gonter
Prof. Dr. habil. Rudolf Kruse
Publication date
01-05-2017
Publisher
Springer Fachmedien Wiesbaden
Published in
ATZ worldwide / Issue 5/2017
Electronic ISSN: 2192-9076
DOI
https://doi.org/10.1007/s38311-017-0026-z

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