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

2025-05-08

AI Summary

This paper addresses the safety and generalization limitations of end-to-end occupancy forecasting methods arising from the absence of explicit physical constraints. To this end, we propose a lightweight spatiotemporal joint occupancy prediction framework guided by physics. Our key contribution is the first integration of Artificial Potential Fields (APF) as a verifiable physical prior into neural network training, thereby unifying data-driven learning with physically grounded reasoning. We further design a hybrid convolutional-recurrent architecture that balances modeling efficiency and representational capacity. Extensive evaluation across diverse, complex driving scenarios demonstrates significant improvements: +12.3% in task completion rate, +18.7% in safe inter-vehicle distance, and enhanced planning latency—while maintaining high reliability and deployment feasibility.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Ensures AV motion planning is accurate and interpretable
• Integrates physical constraints into occupancy prediction
• Improves safety and generalization in diverse scenarios

Innovation

• Integrates verifiable physical rules into occupancy learning
• Uses artificial potential fields as physics-informed guidance
• Combines convolutional and recurrent neural networks