Identification of Vehicle Dynamics Parameters Using Simulation-based Inference

TL;DR

This paper introduces a novel simulation-based inference method for accurately identifying complex, nonlinear vehicle dynamics parameters, enhancing control and planning algorithms for autonomous vehicles.

Contribution

It applies simulation-based inference, a modern Bayesian approach, to vehicle parameter identification, demonstrating improved accuracy over traditional methods.

Findings

Successfully identifies nonlinear vehicle parameters.

Provides accurate estimates for vehicle dynamics equations.

Demonstrates effectiveness in complex simulation scenarios.

Abstract

Identifying tire and vehicle parameters is an essential step in designing control and planning algorithms for autonomous vehicles. This paper proposes a new method: Simulation-Based Inference (SBI), a modern interpretation of Approximate Bayesian Computation methods (ABC) for parameter identification. The simulation-based inference is an emerging method in the machine learning literature and has proven to yield accurate results for many parameter sets in complex problems. We demonstrate in this paper that it can handle the identification of highly nonlinear vehicle dynamics parameters and gives accurate estimates of the parameters for the governing equations.