Predicting Vehicle Impact Severity With Deep Neural Network for Reinforcement Learning Based Autonomous Vehicle Simulators

Road accidents leads to a significant number of deaths in the world, with human error being attributed as one of the roots of the problem. One of the possible alternatives to address the issue is the development of autonomous vehi-cles. Of the many technological advancements in the field of autonomous vehicles, Reinforcement Learning (RL) has shown progress because its methodology is not reliant on human data. However, one of its challenges is the definition of the reward function, which is critical to RL algorithms. In this context, determining the severity of a crash allows for an improvement in the reward function, especially in simulators. In this study, we present a Deep Neural Network (DNN) based model that predicts the crash impact severity in terms of vehicle deformation during the event, along with the de-formation we create a reward strategy for RL- based vehicle simulators. The model is developed based on data generated from finite element simulations replicating a full-frontal vehicle impact against a rigid barrier. We compare the trained model with a variety of NN methods, and different input data. The Neural Network model performs well, supporting the development of a reward system to improve RL-based autonomous vehicle simulators.