シミュレーションベース推論による車両ダイナミクスパラメータの同定
Identification of Vehicle Dynamics Parameters Using Simulation-based Inference
近似ベイズ計算の現代版であるシミュレーションベース推論(SBI)を用いて、車両ダイナミクスの非線形パラメータを高精度に同定できることを示した論文。
著者: Ali Boyali, Simon Thompson, David Robert Wong
分類: cs.RO, cs.LG
原文アブストラクト
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.
