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

Yuming Yin, Shengbo Eben Li, Keqiang Li, Jue Yang, Fei Ma
2020 Transportation Safety and Environment  
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
more » ... d. 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.
doi:10.1093/tse/tdaa009 fatcat:vzgxqhqtofga7mjtnjg6jjfwsm