Improving Simulation to Hardware Correlation of Autonomous Vehicles Operating on Low Friction Surfaces

Abstract

Abstract Simulation is a critical step in the development of autonomous driving technologies, allowing engineers to test control algorithms, path planners, and other dynamic vehicle behaviors in a risk-free, low-cost environment. Under normal driving conditions representative of paved, most vehicle models are suitable for accurate prediction of vehicle motion. On low friction surfaces, basic vehicle models do not display the variability in vehicle response to steering input that is observed on real-world low friction surfaces such as clear ice. This work presents distribution parameters for a stochastic friction grid map for use in simulating vehicle behavior on icy surfaces. Simulation data from rapid double lane changes are compared with vehicle response to the same paths on an ice rink test course. Strong correlation between the simulation and test vehicle is achieved with validation performed using previously developed control and path planning methods.