TGLD: A trust-aware game-theoretic lane-changing decision framework for automated vehicles in heterogeneous traffic

Automated vehicles (AVs) face a critical need to adopt socially compatible behaviors and cooperate with human-driven vehicles (HVs) in heterogeneous traffic environments. However, existing lane-changing decision frameworks for AVs rarely account for the dynamic trust levels of HVs, thereby impeding the accurate prediction of HV behaviors due to inherent uncertainties. This study proposes a trust-aware, game-theoretic lane-changing decision framework for AVs. First, a trust-aware multi-vehicle coalition game for heterogeneous traffic environment is developed, which encompasses a fully cooperative game among AVs and a partially cooperative model for HVs based on each HV's real-time trust level. Second, an online evaluation method to dynamically estimate HV's trust during lane-change interactions is designed, guiding AVs to select appropriate cooperative maneuvers. Finally, social compatibility objectives are considered by minimizing disruption to surrounding vehicles and enhancing the predictability of AV behavior, ensuring the overall lane-changing strategy remains both human-friendly and context-adaptive. To test the proposed model, a human-in-the-loop experiment was conducted in a highway on-ramp scenario. Results showed that the AV adjusted its lane-changing strategy accordingly for human drivers of varying driving styles and trust levels. Moreover, the proposed model facilitated safe, efficient, and comfortable merging behavior for the AV. Ablation studies revealed that incorporating the trust mechanism yields higher average lane-change speeds than models without it, demonstrating improved efficiency while maintaining safety in heterogeneous traffic. This research contributes to enhance the interpretability of HV-AV interaction and promotes the design of more transparent and adaptive lane-changing strategies in automated driving systems.