This paper presents a Predictive Maneuver Planning with Deep Reinforcement Learning (PMP-DRL) model for maneuver planning. Traditional rule-based maneuver planning approaches often have to improve their abilities to handle the variabilities of real-world driving scenarios. By learning from its experience, a Reinforcement Learning (RL)-based driving agent can adapt to changing driving conditions and improve its performance over time. Our proposed approach combines a predictive model and an RL agent to plan for comfortable and safe maneuvers. The predictive model is trained using historical driving data to predict the future positions of other surrounding vehicles. The surrounding vehicles' past and predicted future positions are embedded in context-aware grid maps. At the same time, the RL agent learns to make maneuvers based on this spatio-temporal context information. Performance evaluation of PMP-DRL has been carried out using simulated environments generated from publicly available NGSIM US101 and I80 datasets. The training sequence shows the continuous improvement in the driving experiences. It shows that proposed PMP-DRL can learn the trade-off between safety and comfortability. The decisions generated by the recent imitation learning-based model are compared with the proposed PMP-DRL for unseen scenarios. The results clearly show that PMP-DRL can handle complex real-world scenarios and make better comfortable and safe maneuver decisions than rule-based and imitative models.

翻译：本文提出了一种基于深度强化学习的预测性机动规划（PMP-DRL）模型，用于机动规划。传统的基于规则的机动规划方法往往难以充分应对真实驾驶场景的多样性。通过从经验中学习，基于强化学习（RL）的驾驶智能体能够适应变化的驾驶条件，并随时间推移提升其性能。我们提出的方法结合了预测模型与强化学习智能体，以规划舒适且安全的机动行为。预测模型利用历史驾驶数据进行训练，用于预测周围其他车辆的未来位置。周围车辆的过去与预测未来位置被嵌入上下文感知网格图中。同时，强化学习智能体基于这一时空上下文信息学习如何做出机动决策。使用公开的NGSIM US101与I80数据集生成的仿真环境，对PMP-DRL进行了性能评估。训练序列显示驾驶经验持续改进，表明所提出的PMP-DRL能够学习安全性与舒适性之间的权衡。将近期基于模仿学习的模型在未见场景中生成的决策与提出的PMP-DRL进行了比较。结果清晰表明，PMP-DRL能够处理复杂的真实世界场景，并做出比基于规则模型与模仿模型更舒适且安全的机动决策。