Motion planning for high-level autonomous driving is constrained by a fundamental trade-off between the transparent, yet brittle, nature of pipeline methods and the adaptive, yet opaque, "black-box" characteristics of modern learning-based systems. This paper critically synthesizes the evolution of the field -- from pipeline methods through imitation learning, reinforcement learning, and generative AI -- to demonstrate how this persistent dilemma has hindered the development of truly trustworthy systems. To resolve this impasse, we conduct a comprehensive review of learning-based motion planning methods. Based on this review, we outline a data-driven optimal control paradigm as a unifying framework that synergistically integrates the verifiable structure of classical control with the adaptive capacity of machine learning, leveraging real-world data to continuously refine key components such as system dynamics, cost functions, and safety constraints. We explore this framework's potential to enable three critical next-generation capabilities: "Human-Centric" customization, "Platform-Adaptive" dynamics adaptation, and "System Self-Optimization" via self-tuning. We conclude by proposing future research directions based on this paradigm, aimed at developing intelligent transportation systems that are simultaneously safe, interpretable, and capable of human-like autonomy.

翻译：高级自动驾驶的运动规划面临一个根本性的权衡：一方面，流水线方法具有透明性但脆弱；另一方面，现代基于学习的系统具有适应性但呈现不透明的“黑箱”特性。本文批判性地综合了该领域的发展历程——从流水线方法到模仿学习、强化学习及生成式人工智能——论证这一持续存在的困境如何阻碍了真正可信系统的开发。为突破这一僵局，我们对基于学习的运动规划方法进行了全面综述。基于此综述，我们提出数据驱动的优化控制范式作为统一框架，该框架将经典控制的可验证结构与机器学习的自适应能力协同整合，利用真实世界数据持续优化系统动力学、成本函数及安全约束等关键组件。我们探讨了该框架在实现三项关键下一代能力方面的潜力：“以人为中心”的定制化、“平台自适应”的动力学适配，以及通过自调优实现的“系统自优化”。最后，我们基于该范式提出未来研究方向，旨在开发兼具安全性、可解释性及类人自主能力的智能交通系统。