Toward Cooperative Driving in Mixed Traffic: An Adaptive Potential Game-Based Approach with Field Test Verification

2026-04-22

AI Summary

This study addresses the challenge of coordinating human-driven and connected autonomous vehicles in mixed traffic, where existing approaches struggle to balance individual heterogeneity with system-wide performance. The authors propose a cooperative driving framework grounded in adaptive potential games, unifying individual and system utility functions. By integrating Shapley value allocation to quantify each vehicle’s marginal contribution, online estimation of human driver preferences, and model predictive control, the method dynamically optimizes coordination strategies. This integrated approach significantly improves coordination success rates and outperforms state-of-the-art methods in both safety and traffic efficiency. Real-world experiments further validate its practical feasibility and robustness in realistic driving scenarios.

Technology Category

• Game Theory and Economic Paradigms: Coordination and Collaboration
• Multiagent Systems: Coordination and Collaboration
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Connected autonomous vehicles (CAVs), which represent a significant advancement in autonomous driving technology, have the potential to greatly increase traffic safety and efficiency through cooperative decision-making. However, existing methods often overlook the individual needs and heterogeneity of cooperative participants, making it difficult to transfer them to environments where they coexist with human-driven vehicles (HDVs).To address this challenge, this paper proposes an adaptive potential game (APG) cooperative driving framework. First, the system utility function is established on the basis of a general form of individual utility and its monotonic relationship, allowing for the simultaneous optimization of both individual and system objectives. Second, the Shapley value is introduced to compute each vehicle's marginal utility within the system, allowing its varying impact to be quantified. Finally, the HDV preference estimation is dynamically refined by continuously comparing the observed HDV behavior with the APG's estimated actions, leading to improvements in overall system safety and efficiency. Ablation studies demonstrate that adaptively updating Shapley values and HDV preference estimation significantly improve cooperation success rates in mixed traffic. Comparative experiments further highlight the APG's advantages in terms of safety and efficiency over other cooperative methods. Moreover, the applicability of the approach to real-world scenarios was validated through field tests.

Problem

• cooperative driving
• mixed traffic
• human-driven vehicles
• autonomous vehicles
• heterogeneity

Innovation

• adaptive potential game
• Shapley value
• mixed traffic
• cooperative driving
• preference estimation