让自动驾驶车主动激发轮胎打滑，精准测出路面最大摩擦力。
High-Slip-Ratio Control for Peak Tire-Road Friction Estimation Using Automated Vehicles
- 通过高滑移率控制主动刺激轮胎达到峰值摩擦区。
- 实验证明可准确估计峰值摩擦系数，误差低于5%。
- 适合自动驾驶车队做低成本路面摩擦筛查。
精确估计轮胎-路面摩擦系数（TRFC）对保障车辆安全控制至关重要，尤其在恶劣路况下。然而，现有方法多依赖常规车辆的自然驾驶数据，通常处于轻度加速或制动状态，导致滑移激励不足，难以观测峰值TRFC。本文提出一种高滑移率控制框架，使自动驾驶车辆在空载运行时主动激发峰值摩擦区域，同时确保操作安全。采用简化版魔幻公式轮胎模型表征非线性滑移-力动态，并通过多次高滑移测量进行局部拟合。为支持跟车场景下的安全执行，设计了一种约束最优控制策略，平衡滑移激励、轨迹跟踪与防碰撞需求。此外，引入基于分箱的统计投影方法，提升在噪声和局部稀疏情况下的峰值TRFC估计鲁棒性。该框架经闭环仿真与实车实验验证，展现出高精度、安全性与可扩展性，适用于低成本道路摩擦筛查。
原文摘要 · Abstract (English)
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.
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