Fusion of Heterogeneous Friction Estimates for Traction Adaptive Motion Planning and Control

TL;DR

This paper proposes a fusion method combining local friction estimation and road surface classification to improve real-time traction estimation for autonomous vehicle motion planning, demonstrating near-optimal behavior in simulations.

Contribution

It introduces a novel fusion approach using heteroscedastic Gaussian process regression to enhance friction estimation accuracy for traction adaptive control.

Findings

Fusion yields better friction estimates than individual methods.

Simulation results show improved vehicle behavior with the fusion approach.

The method provides sufficient foresight for adaptive motion planning.

Abstract

Traction adaptive motion planning and control has potential to improve an an automated vehicle's ability to avoid accident in a critical situation. However, such functionality require an accurate friction estimate for the road ahead of the vehicle that is updated in real time. Current state of the art friction estimation techniques include high accuracy local friction estimation in the presence of tire slip, as well as rough classification of the road surface ahead of the vehicle, based on forward looking camera. In this paper we show that neither of these techniques in isolation yield satisfactory behavior when deployed with traction adaptive motion planning and control functionality. However, fusion of the two provides sufficient accuracy, availability and foresight to yield near optimal behavior. To this end, we propose a fusion method based on heteroscedastic gaussian process regression, and present initial simulation based results.