High-Slip-Ratio Control for Peak Tire-Road Friction Estimation Using Automated Vehicles
This addresses the need for safer vehicle control under adverse road conditions, offering a scalable and cost-effective solution for roadway friction screening, though it is incremental as it builds on existing tire models and control strategies.
The paper tackled the problem of accurately estimating the tire-road friction coefficient (TRFC) by developing a high-slip-ratio control framework for automated vehicles, which actively excites peak friction during operations and demonstrated accuracy and safety in simulations and real-vehicle experiments.
Accurate estimation of the tire-road friction coefficient (TRFC) is critical for ensuring safe vehicle control, especially under adverse road conditions. However, most existing methods rely on naturalistic driving data from regular vehicles, which typically operate under mild acceleration and braking. As a result, the data provide insufficient slip excitation and offer limited observability of the peak TRFC. This paper presents a high-slip-ratio control framework that enables automated vehicles (AVs) to actively excite the peak friction region during empty-haul operations while maintaining operational safety. A simplified Magic Formula tire model is adopted to represent nonlinear slip-force dynamics and is locally fitted using repeated high-slip measurements. To support safe execution in car-following scenarios, we formulate a constrained optimal control strategy that balances slip excitation, trajectory tracking, and collision avoidance. In parallel, a binning-based statistical projection method is introduced to robustly estimate peak TRFC under noise and local sparsity. The framework is validated through both closed-loop simulations and real-vehicle experiments, demonstrating its accuracy, safety, and feasibility for scalable, cost-effective roadway friction screening.