Friction and Road Condition Estimation by Combining Cause- and Effect-Based Methods using Bayesian Networks

TL;DR

This paper proposes a Bayesian network-based method to robustly estimate maximum tire-road friction by fusing multiple sensor data, improving stability and safety assessments under varying environmental conditions.

Contribution

It introduces a novel fusion approach combining cause- and effect-based methods using Bayesian networks for more reliable friction estimation.

Findings

Bayesian network effectively models relationships between sensors and friction.

The method outperforms individual sensor-based estimates in tests.

Robustness under different road and environmental conditions is demonstrated.

Abstract

Knowledge about the maximum tire-road friction potential is an important factor to ensure the driving stability and traffic safety of the vehicle. Many authors proposed systems that either measure friction related parameters or estimate the friction coefficient directly via a mathematical model. However these systems can be negatively impacted by environmental factors or require a sufficient level of excitation in the form of tire slip, which is often too low under practical conditions. Therefore, this work investigates, if a more robust estimation can be achieved by fusing the information of multiple systems using a Bayesian network, which models the statistical relationship between the sensors and the maximum friction coefficient. First, the Bayesian network is evaluated over its entire domain to compare the inference process to all possible road conditions. After that, the algorithm is applied to data from a test vehicle to demonstrate the performance under real conditions.