Symbolic Regression for Precrash Accident Severity Prediction

New advanced safety systems like accident-adaptive restraint systems have the potential to improve vehicle safety. However, these systems may require a function predicting the crash severity prior to a collision. This means that only with accident parameters gathered by precrash car sensors the severity of the upcoming collision has to be predicted. In this work, we present the first known approach based on symbolic regression that finds a solution for this challenging problem automatically. For that, we process crash simulation data and apply Prioritized Grammar Enumeration (PGE) for the first time in a real-world application. In the evaluation, we show that the found model is fast, compact and interpretable yet achieving a good prediction performance. We conclude this paper with a discussion and research questions, which may lead to an application of this approach for future, safer vehicles.