Online Slip Detection and Friction Coefficient Estimation for Autonomous Racing

2025-09-18

AI Summary

To address the challenge of real-time, accurate tire-road friction coefficient (TRFC) estimation for autonomous racing vehicles operating at the friction limit, this paper proposes a model-free, lightweight online slip detection and TRFC estimation algorithm. The method relies solely on IMU measurements, LiDAR data, and control inputs: slip is detected in real time by monitoring discrepancies between commanded and measured vehicle motion; under nonslip conditions, TRFC is analytically estimated directly from IMU-measured longitudinal and lateral accelerations—requiring no vehicle or tire dynamic models, parameter identification, or training data. Experimental validation on a 1:10-scale physical test platform demonstrates that the algorithm achieves TRFC estimation errors below 0.05 across diverse road surfaces, with latency under 20 ms. It thus delivers high accuracy, low latency, and strong robustness, providing a reliable perception foundation for autonomous decision-making under extreme driving conditions.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• Estimating tire-road friction coefficient without direct measurement
• Detecting slip events in real-time for autonomous racing
• Using only IMU and LiDAR data without complex models

Innovation

• Uses IMU and LiDAR measurements only
• Detects slip via commanded versus measured motion
• Estimates friction from no-slip condition accelerations