@article{ref1,
title="Self-learning drift control of automated vehicles beyond handling limit after rear-end collision",
journal="Transportation safety and environment",
year="2020",
author="Yin, Yuming and Li, Shengbo Eben and Li, Keqiang and Yang, Jue and Ma, Fei",
volume="2",
number="2",
pages="97-105",
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.<p /> <p>Language: en</p>",
language="en",
issn="2631-4428",
doi="10.1093/tse/tdaa009",
url="http://dx.doi.org/10.1093/tse/tdaa009"
}