论文标题
自动驾驶汽车未信号交叉点的决策：左转动作，深入增强学习
Decision-making at Unsignalized Intersection for Autonomous Vehicles: Left-turn Maneuver with Deep Reinforcement Learning
论文作者
论文摘要
决策模块使自动驾驶汽车能够在复杂的城市环境中，尤其是交叉路口的情况下进行适当的操作。这项工作建议在自动驾驶汽车的未信号交叉点上进行深入的增强学习（DRL）的左转决策框架。研究的自动化车辆的目的是在四向未信号的交叉路口进行有效且安全的左转操作。利用的DRL方法包括深Q学习（DQL）和双DQL。仿真结果表明，提出的决策策略可以有效地降低碰撞率并提高运输效率。这项工作还表明，构造的左转控制结构具有实时应用的巨大潜力。
Decision-making module enables autonomous vehicles to reach appropriate maneuvers in the complex urban environments, especially the intersection situations. This work proposes a deep reinforcement learning (DRL) based left-turn decision-making framework at unsignalized intersection for autonomous vehicles. The objective of the studied automated vehicle is to make an efficient and safe left-turn maneuver at a four-way unsignalized intersection. The exploited DRL methods include deep Q-learning (DQL) and double DQL. Simulation results indicate that the presented decision-making strategy could efficaciously reduce the collision rate and improve transport efficiency. This work also reveals that the constructed left-turn control structure has a great potential to be applied in real-time.
