A Physics-informed End-to-End Occupancy Framework for Motion Planning of Autonomous Vehicles

TL;DR

This paper introduces a physics-informed end-to-end framework for autonomous vehicle motion planning that incorporates physical constraints into occupancy prediction, enhancing safety, efficiency, and reliability.

Contribution

It presents a novel integration of artificial potential fields into occupancy learning, ensuring physically plausible predictions in a unified neural network architecture.

Findings

Improved task completion rate in diverse scenarios

Enhanced safety margins during planning

Increased planning efficiency and reliability

Abstract

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.