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This paper introduces a high-slip-ratio control framework for automated vehicles (AVs) to actively excite the peak tire-road friction region, enabling more accurate estimation of the tire-road friction coefficient (TRFC). The framework uses a simplified Magic Formula tire model, locally fitted with high-slip measurements, and a constrained optimal control strategy to balance slip excitation, trajectory tracking, and collision avoidance. A binning-based statistical projection method is then used to robustly estimate peak TRFC from the collected data. The framework's accuracy, safety, and feasibility are validated through closed-loop simulations and real-vehicle experiments.
Automated vehicles can be turned into cost-effective mobile labs for roadway friction screening by actively inducing high-slip maneuvers, even while safely car-following.
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.
