作者
Zhong Cao, Diange Yang, Shaobing Xu, Huei Peng, Boqi Li, Shuo Feng, Ding Zhao
发表日期
2020/1/6
期刊
IEEE Transactions on Intelligent Transportation Systems
卷号
22
期号
2
页码范围
990-1000
出版商
IEEE
简介
Exiting from highways in crowded dynamic traffic is an important path planning task for autonomous vehicles (AVs). This task can be challenging because of the uncertain motion of surrounding vehicles and limited sensing/observing window. Conventional path planning methods usually compute a mandatory lane change (MLC) command, but the lane change behavior (e.g., vehicle speed and gap acceptance) should also adapt to traffic conditions and the urgency for exiting. In this paper, we propose a reinforcement learning-enhanced highway-exit planner. The learning-based strategy learns from past failures and adjusts the vehicle motion when the AV fails to exit. The reinforcement learning is based on the Monte Carlo tree search (MCTS) approach. The proposed learning-enhanced highway-exit planner is tested 6000 times in stochastic simulations. The results indicate that the proposed planner achieves a …
学术搜索中的文章
Z Cao, D Yang, S Xu, H Peng, B Li, S Feng, D Zhao - IEEE Transactions on Intelligent Transportation …, 2020