マルチフィデリティベイズ最適化による車両コントローラパラメータの効率的学習：シミュレーションから実験まで
Efficient Learning of Vehicle Controller Parameters via Multi-Fidelity Bayesian Optimization: From Simulation to Experiment
自動回帰型マルチフィデリティガウス過程をベイズ最適化に統合し、低忠実度シミュレーションと少数の実世界実験から車両コントローラの最適パラメータを効率的に学習する手法を提案した。
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著者: Yongpeng Zhao, Maik Pfefferkorn, Maximilian Templer, Rolf Findeisen
分類: eess.SY, cs.RO, cs.SY
原文アブストラクト
Parameter tuning for vehicle controllers remains a costly and time-intensive challenge in automotive development. Traditional approaches rely on extensive real-world testing, making the process inefficient. We propose a multi-fidelity Bayesian optimization approach that efficiently learns optimal controller parameters by leveraging both low-fidelity simulation data and a very limited number of real-world experiments. Our approach significantly reduces the need for manual tuning and expensive field testing while maintaining the standard two-stage development workflow used in industry. The core contribution is the integration of an auto-regressive multi-fidelity Gaussian process model into Bayesian optimization, enabling knowledge transfer between different fidelity levels without requiring additional low-fidelity evaluations during real-world testing. We validate our approach through both simulation studies and realworld experiments. The results demonstrate that our method achieves high-quality controller performance with only very few real-world experiments, highlighting its potential as a practical and scalable solution for intelligent vehicle control tuning in industrial applications.
