Title: Evaluating Scenario-based Decision-making for Interactive Autonomous Driving Using Rational Criteria: A Survey Show Chinese title

Title: 基于场景的交互式自动驾驶理性准则决策评估：综述

Abstract: 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. Show Chinese abstract

Abstract: 自动驾驶车辆（AVs）在安全性、可靠性和减碳方面可以显著促进道路运输的进展。 然而，在动态和多样的环境中确保安全和效率仍然是大规模采用自动驾驶车辆的主要障碍。 近年来，深度强化学习（DRL）已成为一种先进的基于人工智能的方法，使自动驾驶车辆能够从数据和交互中自适应地学习决策策略。 由于其适应性，DRL策略比传统的基于规则的方法更适合处理复杂、动态和不可预测的驾驶环境。 然而，不同的驾驶场景会带来不同的挑战，例如在高速公路上避开障碍物和在交叉口到达特定出口，这需要不同的场景特定决策算法。 许多DRL算法已被提出用于交互式决策。 然而，缺乏对这些DRL算法在各种场景中的合理综述。 因此，从多个角度（包括车辆用户和车辆制造商的角度）评估这些算法是至关重要的。 本综述文章回顾了DRL算法在典型场景下的自动驾驶应用，总结了道路特征和最新进展。 这些场景包括高速公路、匝道合流、环形交叉路口和无信号交叉路口。 此外，基于五个合理标准对基于DRL的算法进行了评估：驾驶安全、驾驶效率、训练效率、无私性和可解释性（DDTUI）。 DDTUI的每个标准都针对所回顾的算法进行了具体分析。 最后，总结了未来基于DRL的决策算法面临的挑战。