基于博弈论的交互式自动驾驶场景决策：综述
Scenario-based Decision-making Using Game Theory for Interactive Autonomous Driving: A Survey
AI总结：
本综述系统评估了基于博弈论的交互式自动驾驶决策方法，按场景总结最新进展，批判性分析算法对标准博弈模型的改编及机制，并讨论局限与未来方向。
摘要：
基于博弈的交互式驾驶模拟已成为推进道路交通移动性决策算法的多功能平台。尽管这些环境为测试驾驶策略提供了安全、可扩展且引人入胜的设置，但在动态和多样化场景中确保真实性和稳健性能仍然是一个重大挑战。近年来，博弈技术与先进学习框架的整合促进了自适应决策模型的发展，这些模型有效管理了各种驾驶条件下固有的复杂性。这些模型优于传统模拟方法，尤其是在应对特定场景挑战时，从高速公路上的避障、匝道合流期间的精确操控，到环岛、无信号交叉口的导航，甚至自动驾驶赛车的高速需求。尽管基于博弈的交互式驾驶取得了众多创新，但跨不同场景比较这些方法的系统性综述仍然缺失。本综述通过总结各场景的最新进展和固有道路特征，对基于博弈的交互式驾驶方法进行了全面评估。此外，根据所评算法对标准博弈模型的改编及其具体机制的分析，对其进行了批判性评估，以理解它们对决策性能的影响。最后，综述讨论了当前方法的局限性，并概述了未来研究的有前景方向。
英文摘要：
Game-based interactive driving simulations have emerged as versatile platforms for advancing decision-making algorithms in road transport mobility. While these environments offer safe, scalable, and engaging settings for testing driving strategies, ensuring both realism and robust performance amid dynamic and diverse scenarios remains a significant challenge. Recently, the integration of game-based techniques with advanced learning frameworks has enabled the development of adaptive decision-making models that effectively manage the complexities inherent in varied driving conditions. These models outperform traditional simulation methods, especially when addressing scenario-specific challenges, ranging from obstacle avoidance on highways and precise maneuvering during on-ramp merging to navigation in roundabouts, unsignalized intersections, and even the high-speed demands of autonomous racing. Despite numerous innovations in game-based interactive driving, a systematic review comparing these approaches across different scenarios is still missing. This survey provides a comprehensive evaluation of game-based interactive driving methods by summarizing recent advancements and inherent roadway features in each scenario. Furthermore, the reviewed algorithms are critically assessed based on their adaptation of the standard game model and an analysis of their specific mechanisms to understand their impact on decision-making performance. Finally, the survey discusses the limitations of current approaches and outlines promising directions for future research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
