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Computer Science > Robotics

arXiv:2108.12114 (cs)
[Submitted on 27 Aug 2021]

Title:Identification of Vehicle Dynamics Parameters Using Simulation-based Inference

Authors:Ali Boyali, Simon Thompson, David Robert Wong
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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.
Comments: Presented at the Autoware Workshop of IEEE Intelligent Vehicle Symposium IV2021
Subjects: Robotics (cs.RO); Machine Learning (cs.LG)
Cite as: arXiv:2108.12114 [cs.RO]
  (or arXiv:2108.12114v1 [cs.RO] for this version)
  https://doi.org/10.48550/arXiv.2108.12114

Submission history

From: Ali Boyali [view email]
[v1] Fri, 27 Aug 2021 04:28:14 UTC (3,866 KB)
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