Identification of Vehicle Dynamics Parameters Using Simulation-based Inference
Robotics
2021-08-30 v1 Machine Learning
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.
Cite
@article{arxiv.2108.12114,
title = {Identification of Vehicle Dynamics Parameters Using Simulation-based Inference},
author = {Ali Boyali and Simon Thompson and David Robert Wong},
journal= {arXiv preprint arXiv:2108.12114},
year = {2021}
}
Comments
Presented at the Autoware Workshop of IEEE Intelligent Vehicle Symposium IV2021