不依赖模型和数据，用传感器实时检测打滑并估算摩擦系数。
Online Slip Detection and Friction Coefficient Estimation for Autonomous Racing
- 通过对比控制指令与实际运动判断打滑，无需车辆动力学模型。
- 在无滑状态下直接从加速度估计摩擦系数，精度接近真实值。
- 仅需IMU和激光雷达，适合部署在资源受限的自动驾驶赛车上。
准确获取轮胎-路面摩擦系数（TRFC）对车辆安全、稳定性和性能至关重要，尤其在自动驾驶赛车中，车辆常运行在摩擦极限附近。然而，TRFC无法通过标准传感器直接测量，现有方法或依赖参数不确定的车辆/轮胎模型，或需大量训练数据。本文提出一种轻量级在线打滑检测与TRFC估计算法，仅使用惯性测量单元（IMU）和激光雷达（LiDAR）数据及控制动作，无需特殊动力学或轮胎模型、参数辨识或训练数据。打滑事件通过比较指令运动与实测运动实时检测，摩擦系数则在无滑状态下直接由观测加速度推导。在1:10比例自动驾驶赛车上，于不同摩擦水平下的实验表明，该方法实现高精度且一致的打滑检测与摩擦系数估计，结果与真实值高度吻合。这凸显了该方法简单、可部署、计算高效，在自动驾驶实时打滑监控与摩擦系数估计中的潜力。
原文摘要 · Abstract (English)
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.
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