The integration of Autonomous Vehicles (AVs) into existing human-driven traffic systems poses considerable challenges, especially within environments where human and machine interactions are frequent and complex, such as at unsignalized intersections. To deal with these challenges, we introduce a novel framework predicated on dynamic and socially-aware decision-making game theory to augment the social decision-making prowess of AVs in mixed driving environments. This comprehensive framework is delineated into three primary modules: Interaction Orientation Identification, Mixed-Strategy Game Modeling, and Expert Mode Learning. We introduce 'Interaction Orientation' as a metric to evaluate the social decision-making tendencies of various agents, incorporating both environmental factors and trajectory characteristics. The mixed-strategy game model developed as part of this framework considers the evolution of future traffic scenarios and includes a utility function that balances safety, operational efficiency, and the unpredictability of environmental conditions. To adapt to real-world driving complexities, our framework utilizes a dynamic optimization framework for assimilating and learning from expert human driving strategies. These strategies are compiled into a comprehensive strategy library, serving as a reference for future decision-making processes. The proposed approach is validated through extensive driving datasets and human-in-loop driving experiments, and the results demonstrate marked enhancements in decision timing and precision.

翻译：将自动驾驶车辆（AVs）融入现有的人类驾驶交通系统面临巨大挑战，尤其是在无信号交叉口等人机交互频繁且复杂的场景中。为此，我们提出一种基于动态社会感知决策博弈理论的新型框架，以提升自动驾驶车辆在混合驾驶环境中的社会性决策能力。该框架由三个核心模块构成：交互取向识别模块、混合策略博弈建模模块以及专家模式学习模块。我们首次提出"交互取向"这一指标，通过融合环境因素与轨迹特征来评估不同代理体的社会性决策倾向。所建立的混合策略博弈模型既考虑了未来交通场景的演化过程，又包含能够平衡安全性、运行效率与环境不确定性的效用函数。为适应真实驾驶环境的复杂性，我们采用动态优化框架吸收学习人类驾驶专家的策略，并将这些策略汇编成完整的策略库供后续决策参考。通过大规模行驶数据集与人在环驾驶实验验证，该方法在决策时机与精确度方面均取得显著提升。