Unsignalized intersection vehicle platoon formation scheduling method based on mixed traffic.

Area of Science:

• Intelligent Transportation Systems
• Traffic Engineering
• Automated Driving

Background:

• Unsignalized intersection management is challenging in mixed traffic environments with both human-driven and connected automated vehicles (CAVs).
• Existing virtual queuing methods for automated driving are not suitable for mixed traffic, potentially causing delays.
• A new approach is needed to integrate CAVs and human-driven vehicles for efficient intersection passage.

Purpose of the Study:

• To propose a hierarchical framework for managing unsignalized intersections in mixed traffic.
• To develop the Mixed Platoon Scheduling Model (MPSM) for optimizing vehicle passing order.
• To enhance traffic safety and efficiency at unsignalized intersections.

Main Methods:

• A hierarchical control framework with lower-level distributed control and upper-level collaborative control.
• Forming vehicle platoons with CAVs leading in mixed traffic.
• Transforming the scheduling problem into a graphical optimization using a Mixed Conflict-Directed Graph (MCDG).
• Optimizing spanning tree depth and width, and reordering nodes to minimize delay.

Main Results:

• The proposed MPSM demonstrated superior delay performance compared to Adaptive Signal Control Method (Adaptive-SIM), First-Come, First-Served (FCFS), and Deep First Search Tree (DFST) algorithms.
• The framework effectively manages mixed traffic, improving safety and efficiency at unsignalized intersections.
• Experimental evaluation confirmed the model's effectiveness across different traffic scenarios.

Conclusions:

• The hierarchical framework and MPSM offer a viable solution for unsignalized intersection management in mixed traffic environments.
• MPSM provides an optimal vehicle passing order, enhancing traffic flow and reducing delays.
• This approach is applicable to various traffic conditions, paving the way for safer and more efficient intersections.