High-Slip-Ratio Control for Peak Tire-Road Friction Estimation Using Automated Vehicles

TL;DR

This paper introduces a control framework for automated vehicles to actively induce high slip ratios, enabling more accurate and safe estimation of peak tire-road friction coefficients during operation.

Contribution

It presents a novel high-slip-ratio control strategy combined with a statistical estimation method for improved peak TRFC detection in AVs.

Findings

Validated through simulations and real-vehicle tests.

Achieved accurate peak TRFC estimation under noisy conditions.

Demonstrated safety and feasibility in operational scenarios.

Abstract

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.