Quantum-topological meta-learning for tire-road contact stability and multi-modal road prediction in autonomous driving.

Abstract

This paper addresses the critical challenge of tire-road contact dynamics in intelligent transportation systems, particularly for Level 4 autonomous driving. Traditional empirical models fail to accurately predict tire behavior on unstructured road surfaces, especially under low-adhesion conditions, leading to control delays and safety risks. To address these issues, we propose a novel dual-drive architecture that integrates Quantum Topological Field Theory with meta-learning techniques. A differential homeomorphism model is developed for tire contact stability, using Seiberg-Witten instanton decomposition to create a quantized representation of the contact stress field. Additionally, a multi-modal road prediction system is introduced, combining CBAM-LSTM quantum feature extraction with MAML meta-learning to generalize acceleration signals across different road conditions. Experimental validation on a hardware-in-the-loop platform demonstrates that the system reduces braking distance on ice to 32.1 meters, 38.7% shorter than traditional ABS, and achieves a slip rate control error of 1.8%. The quantum feature extraction accuracy reaches 98.5%, with a Wilson loop reconstruction error under 0.15%. This architecture overcomes key engineering challenges, providing a robust solution for L4 autonomous driving, with potential applications in tire health monitoring and intelligent road networks, enhancing safety and performance in real-world conditions.