A Survey and Comprehensive Taxonomy of Tire-Road Adhesion Coefficient Estimation for Intelligent Vehicles

Within autonomous driving research, the intricate variability of the road surface is frequently overlooked, while the tire-road interactions critically impact vehicle stability. This paper comprehensively reviews traditional and emerging tire-road adhesion coefficient (TRAC) estimation methods for intelligent vehicles. We initially categorize traditional methods into cause-based and effect-based approaches, which are founded on vehicle responses and road surface characteristics, respectively. Then, we classify emerging methods into learning-based approaches and hybrid models combining physical principles with data-driven strategies. We eventually point out areas for improvement and future research directions. The proposed systematic taxonomy summarizes the independent and collaborative operations of dynamics analysis and learning methods in TRAC estimation, offering insights for further research.