从驾驶任务视角总结强化学习在自动驾驶路径规划中的设计经验
A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective
- 基于具体驾驶场景分析强化学习的设计思路
- 提炼出多类驾驶任务的共性设计规律
- 适合研究自动驾驶决策算法的开发者参考
强化学习（RL）凭借其在复杂动态决策任务中探索与优化策略的能力，已成为解决自动驾驶（AD）中路径规划（MoP）挑战的有前景方法。尽管RL与AD领域进展迅速，但针对多样化驾驶任务的系统性RL设计描述与解读仍不充分。本文全面综述了基于强化学习的自动驾驶路径规划，聚焦于任务特定视角的启示。首先梳理了强化学习方法的基础，随后调查其在路径规划中的应用，分析不同场景特征与任务需求对RL设计选择的影响。基于此，总结关键设计经验，提取各类驾驶任务应用中的洞见，为未来实现提供指导。此外，还探讨了当前前沿挑战，回顾近期应对措施，并提出解决未决问题的策略。
原文摘要 · Abstract (English)
Reinforcement learning (RL), with its ability to explore and optimize policies in complex, dynamic decision-making tasks, has emerged as a promising approach to addressing motion planning (MoP) challenges in autonomous driving (AD). Despite rapid advancements in RL and AD, a systematic description and interpretation of the RL design process tailored to diverse driving tasks remains underdeveloped. This survey provides a comprehensive review of RL-based MoP for AD, focusing on lessons from task-specific perspectives. We first outline the fundamentals of RL methodologies, and then survey their applications in MoP, analyzing scenario-specific features and task requirements to shed light on their influence on RL design choices. Building on this analysis, we summarize key design experiences, extract insights from various driving task applications, and provide guidance for future implementations. Additionally, we examine the frontier challenges in RL-based MoP, review recent efforts to addresse these challenges, and propose strategies for overcoming unresolved issues.
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