Wheel alignment effects on autonomous vehicle control vs human driver in simulation

Abstract

In a human operated vehicle, the alignment of tires aims to strike a balance between ease of steering and a minimization of tire wear. The replacement of the human driver in an autonomous vehicle with low latency computer control of path tracking means that tire alignment can be performed with less emphasis on handling characteristics which contribute to ease of steering and directed towards improvement in tire life. This study uses MATLABs Vehicle Dynamics Blockset and Predictive Driver block to compare the path tracking capability of a passenger vehicle performing a double lane change maneuver under the control of the pure pursuit autonomous path following algorithm as well as a simulated human driver. Validation of the Predictive Driver block is performed by tracking a panel of human drivers performing the double lane change maneuver using GPS for localization in a subcompact electric vehicle. The vehicle model is characterized based on measurements from the test vehicle and sent through the same double lane change in simulation to compare behaviors. Tire alignment parameters are altered to demonstrate their effects on vehicle handling under both types of vehicle control. In the simulation environment, the pure pursuit algorithm tracks the desired path consistently across all parameter variations while the simulated human driver varies in its path tracking capabilities.