TY - GEN
T1 - A statistical learning approach for estimating the reliability of crash severity predictions
AU - Müller, Marcus
AU - Nadarajan, Parthasarathy
AU - Botsch, Michael
AU - Utschick, Wolfgang
AU - Böhmländer, Dennis
AU - Katzenbogen, Stefan
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/12/22
Y1 - 2016/12/22
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85010060611&partnerID=8YFLogxK
U2 - 10.1109/ITSC.2016.7795911
DO - 10.1109/ITSC.2016.7795911
M3 - Conference contribution
AN - SCOPUS:85010060611
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 2199
EP - 2206
BT - 2016 IEEE 19th International Conference on Intelligent Transportation Systems, ITSC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Conference on Intelligent Transportation Systems, ITSC 2016
Y2 - 1 November 2016 through 4 November 2016
ER -