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Safe Hybrid-Action Reinforcement Learning-Based Decision and Control for Discretionary Lane Change
March 4, 2024, 5:42 a.m. | Ruichen Xu, Xiao Liu, Jinming Xu, Yuan Lin
cs.LG updates on arXiv.org arxiv.org
Abstract: Autonomous lane-change, a key feature of advanced driver-assistance systems, can enhance traffic efficiency and reduce the incidence of accidents. However, safe driving of autonomous vehicles remains challenging in complex environments. How to perform safe and appropriate lane change is a popular topic of research in the field of autonomous driving. Currently, few papers consider the safety of reinforcement learning in autonomous lane-change scenarios. We introduce safe hybrid-action reinforcement learning into discretionary lane change for the …
abstract accidents advanced arxiv autonomous autonomous vehicles change control cs.lg cs.ro decision driver driving efficiency environments feature hybrid key popular reduce reinforcement reinforcement learning research systems traffic type vehicles
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