選好に応じて運転スタイルを動的に適応させる多目的強化学習による自動運転
Multi-Objective Reinforcement Learning for Adaptable Personalized Autonomous Driving
多目的強化学習を用いて、効率・快適性・速度・攻撃性などの運転スタイル選好を連続的な重みベクトルで表現し、再学習なしに実行時に適応できる単一ポリシーの自動運転エージェントを提案した。CARLAシミュレータでの評価により、衝突回避や経路完走性能を維持しつつ選好に応じて動的に運転行動を変化させることを示した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Hendrik Surmann, Jorge de Heuvel, Maren Bennewitz
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
Human drivers exhibit individual preferences regarding driving style. Adapting autonomous vehicles to these preferences is essential for user trust and satisfaction. However, existing end-to-end driving approaches often rely on predefined driving styles or require continuous user feedback for adaptation, limiting their ability to support dynamic, context-dependent preferences. We propose a novel approach using multi-objective reinforcement learning (MORL) with preference-driven optimization for end-to-end autonomous driving that enables runtime adaptation to driving style preferences. Preferences are encoded as continuous weight vectors to modulate behavior along interpretable style objectives$\unicode{x2013}$including efficiency, comfort, speed, and aggressiveness$\unicode{x2013}$without requiring policy retraining. Our single-policy agent integrates vision-based perception in complex mixed-traffic scenarios and is evaluated in diverse urban environments using the CARLA simulator. Experimental results demonstrate that the agent dynamically adapts its driving behavior according to changing preferences while maintaining performance in terms of collision avoidance and route completion.
