标题： 基于信任的多智能体强化学习框架用于异构交通流中的合作式匝道汇入控制 显示英文标题

标题： Trust-MARL: Trust-Based Multi-Agent Reinforcement Learning Framework for Cooperative On-Ramp Merging Control in Heterogeneous Traffic Flow

摘要： 智能交通系统需要连接式和自动化车辆（CAVs）在复杂的现实交通环境中与人类驾驶车辆（HVs）进行安全高效的协作。然而，人类行为的固有不可预测性，尤其是在高速公路匝道汇入等瓶颈区域，往往会导致交通流量中断并降低系统性能。为了解决异构交通环境中匝道汇入协作的挑战，本研究提出了一种基于信任的多智能体强化学习（Trust-MARL）框架。在宏观层面，Trust-MARL 通过利用智能体间的信任来提升整体交通效率，改善瓶颈处的通行能力，并通过群体级协调减轻交通冲击波的影响。在微观层面，设计了一种动态信任机制，使 CAV 能够根据实时行为和与 HV 和其他 CAV 的历史交互情况调整其合作策略。此外，还集成了一个基于信任触发的博弈论决策模块，以指导每个 CAV 在满足安全性、舒适性和效率约束下调整其合作因素并执行情境感知的车道变更决策。大量的消融研究和对比实验验证了所提出的 Trust-MARL 方法的有效性，展示了在不同 CAV 渗透率和交通密度下显著提高的安全性、效率、舒适性和适应性。 显示英文摘要

摘要： Intelligent transportation systems require connected and automated vehicles (CAVs) to conduct safe and efficient cooperation with human-driven vehicles (HVs) in complex real-world traffic environments. However, the inherent unpredictability of human behaviour, especially at bottlenecks such as highway on-ramp merging areas, often disrupts traffic flow and compromises system performance. To address the challenge of cooperative on-ramp merging in heterogeneous traffic environments, this study proposes a trust-based multi-agent reinforcement learning (Trust-MARL) framework. At the macro level, Trust-MARL enhances global traffic efficiency by leveraging inter-agent trust to improve bottleneck throughput and mitigate traffic shockwave through emergent group-level coordination. At the micro level, a dynamic trust mechanism is designed to enable CAVs to adjust their cooperative strategies in response to real-time behaviors and historical interactions with both HVs and other CAVs. Furthermore, a trust-triggered game-theoretic decision-making module is integrated to guide each CAV in adapting its cooperation factor and executing context-aware lane-changing decisions under safety, comfort, and efficiency constraints. An extensive set of ablation studies and comparative experiments validates the effectiveness of the proposed Trust-MARL approach, demonstrating significant improvements in safety, efficiency, comfort, and adaptability across varying CAV penetration rates and traffic densities.