A Game-Theoretic Framework of Interaction and Cooperative Driving for CAVs at Mixed Unsignalized Intersections

During the ongoing development and proliferation of autonomous driving, human-driven vehicles (HDVs) and connected automated vehicles (CAVs) will coexist in mixed traffic environments for the foreseeable future. However, current autonomous driving systems (ADSs) often face challenges in ensuring optimal safety and efficiency, particularly in complex conflict scenarios. To address these shortcomings and improve cooperation in mixed traffic environments, this article presents a game-theoretic decision-making method. The proposed framework accounts for both CAV–CAV cooperation and CAV–HDV interaction in mixed traffic at unsignalized intersections. It introduces a parameter updating mechanism based on twin games to dynamically adjust HDVs’ parameters to better predict and respond to variable human driving behaviors. To validate the effectiveness of the proposed cooperative driving framework, the comparative analysis of its safety and efficiency with other established methods is conducted. The results demonstrate that our method successfully ensures both safety and efficiency in mixed traffic environments. Compared with reinforcement learning (RL) approaches such as I-PPO, it achieves a 35%–55% improvement in success rate while maintaining decision stability and traffic efficiency. In contrast to methods that enforce strict safety guarantees, our approach improves the average vehicle speed by 0.1–0.6 m/s and the average CAV speed by 0.7–1.7 m/s, without compromising safety. Additionally, several validation experiments are conducted using a hardware-in-the-loop and human-in-the-loop experimental platform, confirming the practical applicability of the method.