Intersection management for autonomous vehicles with vehicle-to-infrastructure communication

Area of Science:

• Intelligent Transportation Systems
• Autonomous Vehicle Navigation
• Traffic Engineering

Background:

• Traditional intersection management relies on fixed or adaptive traffic lights, which can be inefficient.
• Autonomous vehicles offer new possibilities for optimizing traffic flow.
• Vehicle-to-infrastructure communication is crucial for advanced traffic management.

Purpose of the Study:

• To propose a novel intersection management strategy for fully autonomous vehicles using vehicle-to-infrastructure communication.
• To develop a decentralized management approach that minimizes computational load.
• To enhance traffic efficiency and fairness at intersections.

Main Methods:

• A static conflict matrix and a dynamic information list determine intersection passage priority.
• The intersection management unit acts as a database, reducing computational requirements.
• Simulations were performed using Simulation of Urban MObility (SUMO).

Main Results:

• The proposed strategy significantly reduces average intersection time delay.
• A notable decrease in the variance of time delay was observed, indicating improved fairness.
• The strategy demonstrated superior performance compared to fixed and adaptive traffic light methods.

Conclusions:

• The proposed database-centric intersection management strategy is efficient and fair for autonomous vehicles.
• This approach is well-suited for real-time applications, especially in heavy traffic conditions.
• The findings support the adoption of V2I communication for optimized autonomous intersection management.