High-Slip-Ratio Control for Peak Tire-Road Friction Estimation Using Automated Vehicles
🤖 AI Summary
This study addresses the challenge of accurately estimating the peak tire–road friction coefficient, which existing methods struggle to achieve due to their reliance on routine driving data that lacks sufficient slip excitation. To overcome this limitation, the authors propose an active high-slip-ratio excitation control framework that safely induces operation near the peak friction region during unoccupied autonomous vehicle runs. The approach integrates trajectory tracking and collision-avoidance constraints to enable efficient friction identification while ensuring safety. Innovatively enhancing observability of peak friction under strict safety guarantees, the method also supports large-scale road friction mapping. Built upon a simplified Magic Formula tire model, constrained optimal control, and binning-based statistical projection, the framework demonstrates superior performance in estimation accuracy, safety, and cost-effectiveness, as validated through closed-loop simulations and real-world vehicle experiments.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
tire-road friction coefficient
peak friction estimation
slip ratio
automated vehicles
friction observability
Innovation
Methods, ideas, or system contributions that make the work stand out.
high-slip-ratio control
tire-road friction estimation
automated vehicles
constrained optimal control
statistical projection
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