Binary Road Surface Classification Using Machine Learning on Production Vehicle Signals During Cruising

2026-06-01

AI Summary

This study addresses the challenge that traditional vehicle dynamics–based friction estimation methods struggle to distinguish between dry/wet and snow/ice road surfaces under steady-state cruising conditions without significant wheel slip. To overcome this limitation, the authors propose a data-driven approach that leverages standard production vehicle sensor signals—including wheel speeds, wheel torques, longitudinal acceleration, steering angle, and yaw rate—to extract features via sliding-window batch processing and feed them into an end-to-end machine learning model for binary classification of road surface conditions (dry/wet vs. snow/ice). This method represents the first successful identification of tire-road friction states during cruise scenarios, thereby transcending the operational constraints of conventional dynamics-based techniques. Validated on real-world driving data, the approach demonstrates high accuracy and offers a practical, scalable new paradigm for friction estimation in tire-vehicle systems.

Abstract

Knowledge of real-time road slipperiness, or even better, a refined estimate of peak grip potential, is a critical input for vehicle warning and intervention control systems. Typically, friction is estimated through dynamics-based recursive estimators by calculating the slip slope; however, its efficacy is heavily constrained by the vehicle dynamic scenario. When the vehicle is cruising and there is little to no slip, these methods become ineffective due to the inability of present-day production-grade sensors, such as wheel speed sensors, and methods to either measure or accurately estimate micro slip, which is crucial for distinguishing different surfaces. To address this challenge, the correlation between vehicle signals and road surface condition during cruising needs to be uncovered using machine learning. In this paper, a feature-based framework and an end-to-end data-driven framework are used to correlate the statistics of vehicle dynamics behavior with the condition of the road surface and perform binary classification into grip, dry or damp, and slip, snow or ice, conditions. A sliding-window approach is adopted to batch a short buffered window of wheel speeds, wheel torques, longitudinal acceleration, steering angle, and yaw rate, which are fed into a machine learning module for predicting the road state. Validation results on public-road data show scenarios where the data-driven method identifies the road surface correctly even during cruising, showing promise for accurate data-driven friction-related state estimators in the field of tire and vehicle dynamics.

Problem

• road surface classification
• friction estimation
• cruising condition
• micro slip
• vehicle dynamics

Innovation

• road friction estimation
• machine learning
• vehicle dynamics
• binary classification
• cruising condition