TY  - JOUR
PY  - 2020//
TI  - Self-learning drift control of automated vehicles beyond handling limit after rear-end collision
JO  - Transportation safety and environment
A1  - Yin, Yuming
A1  - Li, Shengbo Eben
A1  - Li, Keqiang
A1  - Yang, Jue
A1  - Ma, Fei
SP  - 97
EP  - 105
VL  - 2
IS  - 2
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 2631-4428
UR  - http://dx.doi.org/10.1093/tse/tdaa009
ID  - ref1
ER  -