TGLD: A Trust-Aware Game-Theoretic Lane-Changing Decision Framework for Automated Vehicles in Heterogeneous Traffic

2025-07-14

AI Summary

In mixed traffic, autonomous vehicles (AVs) and human-driven vehicles (HVs) often exhibit misaligned lane-changing behaviors due to inaccurate HV behavior prediction and incompatible interaction—stemming from the neglect of dynamic human trust in AV decision-making. To address this, we propose a multi-vehicle coalition game framework integrated with online trust estimation. We model HV’s real-time trust level as an estimable latent state embedded within a non-cooperative game formulation, and introduce a social compatibility objective to enable AVs to adaptively refine cooperative strategies. Validated through human-in-the-loop experiments and high-fidelity simulations, our approach significantly improves lane-changing success rate (+18.3%) and safety (32.7% reduction in conflict incidence), while enhancing AV behavioral predictability and interaction transparency. The framework provides an interpretable, generalizable theoretical foundation and practical methodology for socially aware autonomous decision-making.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Adversarial Agents
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

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.

Problem

• Automated vehicles need cooperative behaviors with human-driven vehicles
• Existing frameworks ignore dynamic trust levels of human drivers
• Propose trust-aware game-theoretic lane-changing decision framework

Innovation

• Game-theoretic framework for AV lane-changing decisions
• Dynamic trust evaluation for human-driven vehicles
• Social compatibility in heterogeneous traffic environments