Experimental investigation of a maneuver selection algorithm for vehicles in low adhesion conditions

TL;DR

This paper demonstrates an experimental approach using a scaled vehicle platform to estimate ground conditions and select optimal maneuvers in low adhesion winter conditions, enhancing vehicle safety.

Contribution

It introduces a model-based estimator combined with a data-driven predictor for real-time maneuver selection in low adhesion scenarios, validated on a scaled vehicle platform.

Findings

Real-time ground parameter estimation achieved

Optimal maneuver prediction demonstrated

Effective maneuver selection in low adhesion conditions

Abstract

Winter conditions, characterized by the presence of ice and snow on the ground, are more likely to lead to road accidents. This paper presents an experimental proof of concept, with a 1/5th scale car platform, of a maneuver selection scheme for low adhesion conditions. In the proposed approach, a model-based estimator first processes the high-dimensional sensors data of the IMU, LIDAR and encoders to estimate physically relevant vehicle and ground conditions parameters such as the inertial velocity of the vehicle $v$, the friction coefficient $\mu$, the cohesion $c$ and the internal shear angle $\phi$. Then, a data-driven predictor is trained to predict the optimal maneuver to perform in the situation characterized by the estimated parameters. Experimental results show that it is possible to 1) produce a real-time estimate of the relevant ground parameters, and 2) determine an optimal maneuver based on the estimated parameters between a limited set of maneuvers.