Collaborative Overtaking Strategy for Enhancing Overall Effectiveness of Mixed Connected and Connectionless Vehicles

Intelligent Transportation Systems (ITS) aim to enhance traffic management by improving connectivity and data sharing among vehicles and with road infrastructure. In a Mixed Connected and Connectionless Vehicles (MCCV) scenario consisting of connected vehicles equipped with On-Board Units (OBUs) and non-connected vehicles lacking OBUs, the inability for direct communication between two types of vehicles is due to disparities in communication capabilities. However, this discrepancy results in critical lane-changing overtaking driving decisions based on fully connected scenarios struggling to adapt to the dynamic interactions among these different types of vehicles. Considering the diversity in decision-making ways and capabilities of non-connected vehicles in MCCV scenarios, ensuring the coordinated execution of safe and efficient lane-changing overtaking maneuvers by multiple connected vehicles is crucial for enhancing traffic efficiency. Therefore, we propose a collaborative strategy to facilitate safer and more efficient lane-changing overtaking maneuvers for connected vehicles in the MCCV scenario. First, we design a multi-criteria priority detection, and a dynamic event-triggered mechanism based on confidence intervals to foster efficient collaboration among connected vehicles, optimizing decision-making and reducing conflicts. Second, to accommodate diverse driving styles of autonomous and human-driven vehicles, we introduce an Improved Dynamic Precise Fuzzy C-Means (IDP-FCM) algorithm to dynamically identify and adapt to different driving styles, thereby improving safety. Finally, tackling the challenge of multiple connected vehicles performing lane-changing overtaking involving hybrid action space, our proposed Multi-agent Contrastive Parameterized Dueling Deep Q-Network (MCPDDQN) algorithm incorporates contrastive learning to improve strategy stability in complex driving scenarios. Our experimental results demonstrate the effectiveness of our strategy in improving road safety and traffic efficiency of the MCCV scenario.