Self-learning drift control of automated vehicles beyond handling limit after rear-end collision

Abstract Vehicles involved in traffic accidents generally experience divergent vehicle motion, which causes severe damage. This paper presents a self-learning drift-control method for the purpose of stabilizing a vehicle's yaw motions after a high-speed rear-end collision. The struck vehicle generally experiences substantial drifting and/or spinning after the collision, which is beyond the handling limit and difficult to control. Drift control of the struck vehicle along the original lane was investigated. The rear-end collision was treated as a set of impact forces, and the three-dimensional non-linear dynamic responses of the vehicle were considered in the drift control. A multi-layer perception neural network was trained as a deterministic control policy using the actor-critic reinforcement learning framework. The control policy was iteratively updated, initiating from a random parameterized policy. The results show that the self-learning controller gained the ability to eliminate unstable vehicle motion after data-driven training of about 60,000 iterations. The controlled struck vehicle was also able to drift back to its original lane in a variety of rear-end collision scenarios, which could significantly reduce the risk of a second collision in traffic.