人間のレースドライバー行動を模倣する確率的フレームワーク
A Probabilistic Framework for Imitating Human Race Driver Behavior
ドライバー行動モデリングを複数モジュールに分割し、確率的運動プリミティブとクロソイド曲線、ニューラルネットワークを組み合わせて人間の運転スタイルを模倣する手法を提案し、シミュレーションで高い模倣精度と頑健性を示した。
著者: Stefan Löckel, Jan Peters, Peter van Vliet
分類: cs.RO, cs.LG, stat.ML
原文アブストラクト
Understanding and modeling human driver behavior is crucial for advanced vehicle development. However, unique driving styles, inconsistent behavior, and complex decision processes render it a challenging task, and existing approaches often lack variability or robustness. To approach this problem, we propose Probabilistic Modeling of Driver behavior (ProMoD), a modular framework which splits the task of driver behavior modeling into multiple modules. A global target trajectory distribution is learned with Probabilistic Movement Primitives, clothoids are utilized for local path generation, and the corresponding choice of actions is performed by a neural network. Experiments in a simulated car racing setting show considerable advantages in imitation accuracy and robustness compared to other imitation learning algorithms. The modular architecture of the proposed framework facilitates straightforward extensibility in driving line adaptation and sequencing of multiple movement primitives for future research.
