A statistical learning approach for estimating the reliability of crash severity predictions

Marcus Müller, Parthasarathy Nadarajan, Michael Botsch, Wolfgang Utschick, Dennis Böhmländer, Stefan Katzenbogen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

Abstract

Ahead of an unavoidable collision, the actual crash constellation and along with it, the crash severity can significantly change based on the driver actions. To justify the use of safety measures like airbags, prior to an accident, the severity of the predicted crash must be high enough and the crash severity prediction itself must be reliable. In this work, a machine learning driven reliability estimator for crash severity predictions is presented. The reliability estimate is obtained by simulating various driver hypotheses and analyzing the corresponding crash severity distribution. A simulation framework is introduced, utilizing a two-Track dynamics model and a mass-spring model, to simulate the pre-, in-And postcrash phases and automatically generate a large amount of crash data. The data are used to train a Random Forest regression model, capable of estimating the reliability of one crash severity prediction in real-Time and thereby, around 105 times faster than with simulations, and with a correlation coefficient of true and predicted reliability values of 0:92.

Original languageEnglish
Title of host publication2016 IEEE 19th International Conference on Intelligent Transportation Systems, ITSC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2199-2206
Number of pages8
ISBN (Electronic)9781509018895
DOIs
StatePublished - 22 Dec 2016
Event19th IEEE International Conference on Intelligent Transportation Systems, ITSC 2016 - Rio de Janeiro, Brazil
Duration: 1 Nov 20164 Nov 2016

Publication series

NameIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

Conference

Conference19th IEEE International Conference on Intelligent Transportation Systems, ITSC 2016
Country/TerritoryBrazil
CityRio de Janeiro
Period1/11/164/11/16

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