提出数据驱动最优控制框架，融合学习与控制优势。
A Review of Learning-Based Motion Planning: Toward a Data-Driven Optimal Control Approach
- 构建数据驱动最优控制范式，整合机器学习与控制理论
- 提出三维度实现路径：定制化、动力学自适应、自调优
- 面向可信赖自动驾驶，适合研究智能决策与安全控制的学者
自动驾驶运动规划面临关键权衡：传统基于规则的方法虽具可验证的安全性与可解释性，但在复杂场景中泛化能力差；新兴学习方法（如模仿学习、强化学习、生成式AI）适应性强，却存在透明度低和安全风险。现有综述多孤立分析这些AI方法，忽视其与严格控制框架的融合潜力。本文首次系统回顾数据驱动最优控制（DDOC）范式，明确探讨如何将最优控制的理论保证与现代机器学习的自适应能力相结合。基于此框架，提出首个基于DDOC的运动规划路线图，将其实施分为三个关键维度：定制化、动力学自适应与自调优。最后，为弥合现实差距，识别四个未来研究方向，推动可信且类人自动驾驶的实现。
原文摘要 · Abstract (English)
Motion planning for autonomous driving (AD) faces a critical trade-off. While traditional rule-based pipelines offer verifiable safety and interpretability, they often fail to generalize in complex scenarios. Conversely, emerging learning-based methods-including imitation learning (IL), reinforcement learning (RL), and generative AI-offer greater adaptability but are often constrained by opacity and safety risks. Existing surveys typically analyze these AI methods in isolation, overlooking the potential of integrating them with rigorous control frameworks. To bridge this gap, this paper presents the first systematic review of the Data-Driven Optimal Control (DDOC) paradigm, explicitly examining how it synergizes the theoretical guarantees of optimal control with the adaptive capabilities of modern machine learning. Building on this framework, we propose the first roadmap for DDOC-based motion planning, structuring its implementation into three critical dimensions: customization, dynamics adaptation, and self-tuning. Finally, to close the remaining reality gap, we identify four future research directions, thereby accelerating the transition to trustworthy and human-like autonomous driving.
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