自動運転のための拡散ポリシーにおける離散対照学習
Discrete Contrastive Learning for Diffusion Policies in Autonomous Driving
人間の運転データから対照学習で運転スタイルの辞書を抽出し、量子化して条件付き拡散ポリシーを学習することで、より安全で人間らしい運転行動を生成する手法を提案した。
著者: Kalle Kujanpää, Daulet Baimukashev, Farzeen Munir, Shoaib Azam, Tomasz Piotr Kucner, Joni Pajarinen, Ville Kyrki
分類: cs.RO, cs.AI
原文アブストラクト
Learning to perform accurate and rich simulations of human driving behaviors from data for autonomous vehicle testing remains challenging due to human driving styles' high diversity and variance. We address this challenge by proposing a novel approach that leverages contrastive learning to extract a dictionary of driving styles from pre-existing human driving data. We discretize these styles with quantization, and the styles are used to learn a conditional diffusion policy for simulating human drivers. Our empirical evaluation confirms that the behaviors generated by our approach are both safer and more human-like than those of the machine-learning-based baseline methods. We believe this has the potential to enable higher realism and more effective techniques for evaluating and improving the performance of autonomous vehicles.
