Accurate knowledge of the tire-road friction coefficient (TRFC) is essential for vehicle safety, stability, and performance, especially in autonomous racing, where vehicles often operate at the friction limit. However, TRFC cannot be directly measured with standard sensors, and existing estimation methods either depend on vehicle or tire models with uncertain parameters or require large training datasets. In this paper, we present a lightweight approach for online slip detection and TRFC estimation. Our approach relies solely on IMU and LiDAR measurements and the control actions, without special dynamical or tire models, parameter identification, or training data. Slip events are detected in real time by comparing commanded and measured motions, and the TRFC is then estimated directly from observed accelerations under no-slip conditions. Experiments with a 1:10-scale autonomous racing car across different friction levels demonstrate that the proposed approach achieves accurate and consistent slip detections and friction coefficients, with results closely matching ground-truth measurements. These findings highlight the potential of our simple, deployable, and computationally efficient approach for real-time slip monitoring and friction coefficient estimation in autonomous driving.

翻译：轮胎-路面摩擦系数（TRFC）的精确获取对于车辆安全性、稳定性及性能至关重要，在自主赛车领域尤其如此，因为车辆常在摩擦极限下运行。然而，TRFC无法通过标准传感器直接测量，现有估计方法要么依赖于参数不确定的车辆或轮胎模型，要么需要大量训练数据集。本文提出一种用于在线滑移检测与TRFC估计的轻量化方法。该方法仅依赖IMU与LiDAR测量数据及控制指令，无需特定的动力学或轮胎模型、参数辨识或训练数据。通过对比指令运动与实测运动，实时检测滑移事件，并在无滑移条件下直接根据观测加速度估计TRFC。通过1:10比例自主赛车在不同摩擦水平路面上的实验表明，所提方法能实现准确且一致的滑移检测与摩擦系数估计，其结果与真实测量值高度吻合。这些发现凸显了这种简洁、可部署且计算高效的方法在自动驾驶实时滑移监测与摩擦系数估计中的潜力。