高滑移比控制用于自动化车辆的峰值轮胎-路面摩擦估计
High-Slip-Ratio Control for Peak Tire-Road Friction Estimation Using Automated Vehicles
AI总结：
本文提出一种高滑移比控制框架，通过主动激励峰值摩擦区域实现自动化车辆的准确TRFC估计，结合非线性轮胎模型和约束优化策略，提升道路摩擦识别的精度与安全性。
摘要：
准确估计轮胎-路面摩擦系数（TRFC）对于确保车辆控制的安全性至关重要，特别是在恶劣道路条件下。然而，大多数现有方法依赖于常规车辆的自然驾驶数据，这些车辆通常在轻度加速和制动下运行。因此，这些数据提供不足的滑移激励，并且对峰值TRFC的可观察性有限。本文提出了一种高滑移比控制框架，使自动化车辆（AVs）在空载运行期间主动激发峰值摩擦区域，同时保持操作安全性。采用简化版的Magic Formula轮胎模型来表示非线性滑移力动力学，并通过重复的高滑移测量进行局部拟合。为了支持汽车跟随场景中的安全执行，我们制定了一种约束最优控制策略，平衡滑移激励、轨迹跟踪和碰撞避免。同时，引入了一种基于分箱的统计投影方法，以在噪声和局部稀疏性下稳健地估计峰值TRFC。该框架通过闭环仿真和实车实验进行验证，证明了其在可扩展、低成本道路摩擦筛查中的准确性、安全性和可行性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
