将物理规则融入端到端占位预测，提升自动驾驶规划的安全性与效率。
A Physics-informed End-to-End Occupancy Framework for Motion Planning of Autonomous Vehicles
- 用人工势场作为物理约束指导网络训练，确保预测结果符合物理规律。
- 在多种驾驶场景中，任务完成率和安全裕度显著提升，规划效率更高。
- 适合追求高安全性和可解释性的自动驾驶系统研发人员。
准确且可解释的运动规划对自动驾驶车辆在复杂不确定环境中的导航至关重要。尽管近期端到端占位预测方法提升了环境理解能力，但通常缺乏显式物理约束，限制了安全性与泛化性能。本文提出一种统一的端到端框架，将可验证的物理规则嵌入占位学习过程。具体地，在网络训练中引入人工势场（APF）作为物理引导，确保预测的占位图既数据高效又物理合理。该架构结合卷积与循环神经网络，以捕捉空间与时间依赖关系，同时保持模型灵活性。实验表明，该方法在多样驾驶场景中均提升了任务完成率、安全裕度与规划效率，验证了其在真实自动驾驶系统中可靠部署的潜力。
原文摘要 · Abstract (English)
Accurate and interpretable motion planning is essential for autonomous vehicles (AVs) navigating complex and uncertain environments. While recent end-to-end occupancy prediction methods have improved environmental understanding, they typically lack explicit physical constraints, limiting safety and generalization. In this paper, we propose a unified end-to-end framework that integrates verifiable physical rules into the occupancy learning process. Specifically, we embed artificial potential fields (APF) as physics-informed guidance during network training to ensure that predicted occupancy maps are both data-efficient and physically plausible. Our architecture combines convolutional and recurrent neural networks to capture spatial and temporal dependencies while preserving model flexibility. Experimental results demonstrate that our method improves task completion rate, safety margins, and planning efficiency across diverse driving scenarios, confirming its potential for reliable deployment in real-world AV systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。