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.
翻译:决策模块使自主车辆能够在复杂的城市环境中,特别是交叉情况下,达到适当的操控,这项工作提议在自主车辆的无信号十字路口建立以强化型左转决策框架,研究的自动车辆的目标是在四路无信号十字路口采取高效和安全的左转机动,开发的DRL方法包括深空学习和双空DQL。模拟结果表明,提出的决策战略可以有效地降低碰撞率,提高运输效率。这项工作还表明,建造的左转控制结构在实时应用方面具有巨大潜力。