A Review of Learning-Based Motion Planning: Toward a Data-Driven Optimal Control Approach

• URL: http://arxiv.org/abs/2512.11944v1
• Date: Fri, 12 Dec 2025 14:01:24 GMT
• Title: A Review of Learning-Based Motion Planning: Toward a Data-Driven Optimal Control Approach
• Authors: Jia Hu, Yang Chang, Haoran Wang,
• Abstract summary: Motion planning for high-level autonomous driving is constrained by a trade-off between the transparent, yet brittle, nature of pipeline methods and the adaptive, yet opaque, "black-box" characteristics of modern learning-based systems.<n>We propose a data-driven optimal control paradigm as a unifying framework that integrates classical control with the adaptive capacity of machine learning.<n>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.
• Score: 9.141034088788233
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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