対話型自動運転におけるシナリオベース意思決定の合理的基準による評価：サーベイ
Evaluating Scenario-based Decision-making for Interactive Autonomous Driving Using Rational Criteria: A Survey
自動運転の対話型意思決定に用いられる深層強化学習アルゴリズムを、高速道路・合流・ラウンドアバウト・無信号交差点などのシナリオ別に整理し、複数の観点から評価したサーベイ。
著者: Zhen Tian, Zhihao Lin, Dezong Zhao, Wenjing Zhao, David Flynn, Shuja Ansari, Chongfeng Wei
分類: cs.RO, cs.AI, cs.SY, eess.SY
原文アブストラクト
Autonomous vehicles (AVs) can significantly promote the advances in road transport mobility in terms of safety, reliability, and decarbonization. However, ensuring safety and efficiency in interactive during within dynamic and diverse environments is still a primary barrier to large-scale AV adoption. In recent years, deep reinforcement learning (DRL) has emerged as an advanced AI-based approach, enabling AVs to learn decision-making strategies adaptively from data and interactions. DRL strategies are better suited than traditional rule-based methods for handling complex, dynamic, and unpredictable driving environments due to their adaptivity. However, varying driving scenarios present distinct challenges, such as avoiding obstacles on highways and reaching specific exits at intersections, requiring different scenario-specific decision-making algorithms. Many DRL algorithms have been proposed in interactive decision-making. However, a rationale review of these DRL algorithms across various scenarios is lacking. Therefore, a comprehensive evaluation is essential to assess these algorithms from multiple perspectives, including those of vehicle users and vehicle manufacturers. This survey reviews the application of DRL algorithms in autonomous driving across typical scenarios, summarizing road features and recent advancements. The scenarios include highways, on-ramp merging, roundabouts, and unsignalized intersections. Furthermore, DRL-based algorithms are evaluated based on five rationale criteria: driving safety, driving efficiency, training efficiency, unselfishness, and interpretability (DDTUI). Each criterion of DDTUI is specifically analyzed in relation to the reviewed algorithms. Finally, the challenges for future DRL-based decision-making algorithms are summarized.
