让自动驾驶车根据人类司机信任度动态调整变道策略，提升人车协同效率。
TGLD: A Trust-Aware Game-Theoretic Lane-Changing Decision Framework for Automated Vehicles in Heterogeneous Traffic
- 构建多方合作博弈模型，融合自动驾驶与人类驾驶的协作程度。
- 在线评估人类司机信任水平，指导自动驾驶选择合适变道动作。
- 实验证明该框架显著提升变道效率与安全性，适合复杂交通场景应用。
自动驾驶车辆在混合交通环境中亟需具备社会兼容行为并有效与人类驾驶车辆（HVs）协作。然而，现有变道决策框架普遍忽视了人类驾驶员动态信任水平，限制了对人类驾驶行为的准确预测。为此，本文提出一种信任感知的博弈论变道决策框架（TGLD）。首先，构建多车联盟博弈模型，融合自动驾驶车辆间的完全协作与人类驾驶车辆基于实时信任评估的部分协作。其次，设计在线信任评估方法，动态估算变道交互中人类驾驶员的信任水平，引导自动驾驶车辆选择适配情境的协作行为。最后，通过最小化对周围车辆的干扰并提升自动驾驶行为可预测性，实现社会兼容性目标，确保人机友好且情境自适应的变道策略。在高速公路匝道汇入场景开展的人机协同实验验证了TGLD的有效性。结果表明，自动驾驶车辆能根据不同人类驾驶员的信任水平和驾驶风格动态调整策略；引入信任机制显著提升了变道效率，保障安全，并促进透明、自适应的车车交互。
原文摘要 · Abstract (English)
Automated vehicles (AVs) face a critical need to adopt socially compatible behaviors and cooperate effectively with human-driven vehicles (HVs) in heterogeneous traffic environment. However, most existing lane-changing frameworks overlook HVs' dynamic trust levels, limiting their ability to accurately predict human driver behaviors. To address this gap, this study proposes a trust-aware game-theoretic lane-changing decision (TGLD) framework. First, we formulate a multi-vehicle coalition game, incorporating fully cooperative interactions among AVs and partially cooperative behaviors from HVs informed by real-time trust evaluations. Second, we develop an online trust evaluation method to dynamically estimate HVs' trust levels during lane-changing interactions, guiding AVs to select context-appropriate cooperative maneuvers. Lastly, social compatibility objectives are considered by minimizing disruption to surrounding vehicles and enhancing the predictability of AV behaviors, thereby ensuring human-friendly and context-adaptive lane-changing strategies. A human-in-the-loop experiment conducted in a highway on-ramp merging scenario validates our TGLD approach. Results show that AVs can effectively adjust strategies according to different HVs' trust levels and driving styles. Moreover, incorporating a trust mechanism significantly improves lane-changing efficiency, maintains safety, and contributes to transparent and adaptive AV-HV interactions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。