Adaptive game-theoretic decision-making with driving style recognition for autonomous vehicles in uninterrupted traffic flows at intersections

Abstract

The absence of standardized conflict resolution mechanisms presents critical challenges for autonomous vehicles operating in uninterrupted traffic flows, particularly when managing time-sensitive interactions with heterogeneous road users. Existing approaches either adopt overly conservative policies by oversimplifying multi-agent interactions or neglect the critical influence of heterogeneous driving styles. This paper proposes a game-theoretic decision-making framework for autonomous vehicles in uninterrupted traffic flow scenarios, specifically designed to address the intertwined challenges of multi-objective optimization and driving style adaptation. A hierarchical game-theoretic architecture integrates kinematic state evolution, feasibility constraints, and interactive behavior modeling to rigorously model multi-vehicle interactions under dynamic mixed traffic conditions. A novel online identification mechanism estimates driving styles through real-time interaction pattern analysis, while a machine learning-driven adaptive framework generates parametric policies through offline random forest training coupled with context-aware online policy adjustments. Comprehensive simulations validate the framework’s effectiveness in both single and multiple intersection scenarios, demonstrating enhanced interaction adaptability (more than 10% efficiency improvements) compared to conventional non-adaptive methods. Experimental results demonstrate the model’s capability to efficiently handle heterogeneous driving behaviors and dynamically refine negotiation strategies, providing a systematic, human-like vehicle decision-making solution for mixed traffic environments.