Abstract—In road traffic, critical situations pass by as quickly as they appear. Within the blink of an eye, one has to come to a decision, which can make the difference between a low severity, high severity or fatal crash. Because time is important, a machine learning driven Crash Severity Predictor (CSP) is presented which provides the estimated crash severity distribution of an imminent crash in less than 0.2ms. This is 63⋅ 10³ times faster compared to predicting the same distribution through computationally expensive numerical simulations. With the proposed method, even very complex crash data, like the results of Finite Element Method (FEM) simulations, can be made available ahead of a collision. Knowledge, which can be used to prepare occupants and vehicle to an imminent crash, activate and adjust safety measures like airbags or belt tensioners before of a collision or let self-driving vehicles go for the maneuver with the lowest crash severity. Using a real-world crash test it is shown that significant safety potential is left unused if instead of the CSP-proposed driving maneuver, no or the wrong actions are taken.
 

Index Terms—crash severity, vehicle safety, reliable prediction, machine learning

Cite: Marcus Müller, Michael Botsch, Dennis Böhmländer, and Wolfgang Utschick, "Machine Learning Based Prediction of Crash Severity Distributions for Mitigation Strategies," Vol. 9, No. 1, pp. 15-24, February 2018. doi: 10.12720/jait.9.1.15-24