Online Slip Detection and Friction Coefficient Estimation for Autonomous Racing
This addresses the need for real-time, model-free TRFC estimation in autonomous racing, offering a deployable solution without requiring large datasets or complex models, though it is incremental as it builds on existing slip detection concepts.
The paper tackled the problem of estimating the tire-road friction coefficient (TRFC) for autonomous racing by developing a lightweight online method using only IMU, LiDAR, and control inputs, achieving accurate slip detections and friction estimates that closely matched ground-truth measurements across different friction levels.
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.