A Cyber-Physical Framework for Optimal Coordination of Connected and Automated Vehicles on Multi-Lane Freeways

Area of Science:

• Intelligent Transportation Systems
• Control Engineering
• Traffic Engineering

Background:

• Uncoordinated driving behavior significantly contributes to freeway congestion and bottlenecks.
• Connected and Automated Vehicles (CAVs) offer potential solutions for improving traffic flow.
• Existing traffic management systems often struggle with real-time optimization for complex scenarios like merging and lane changing.

Purpose of the Study:

• To develop and evaluate a novel cyber-physical framework for optimal coordination of CAVs on multi-lane freeways.
• To enhance traffic flow efficiency, safety, and fuel economy through coordinated vehicle movements.
• To minimize speed deviation and acceleration while ensuring safe gaps for lane changes and merging.

Main Methods:

• A cloud-based traffic coordination system optimizes target trajectories for individual vehicles using a receding horizon control (RHC) approach.
• Vehicles are grouped into platoons for successive trajectory optimization.
• Individual vehicles utilize local controllers to follow optimized trajectories while maintaining safe distances.

Main Results:

• The proposed framework demonstrated significant improvements in fuel economy, average velocity, and travel time compared to traditional human-driven systems.
• Microscopic traffic simulations validated the effectiveness of the coordination system across various traffic volumes.
• The RHC approach ensures fast optimization, enabling real-time application.

Conclusions:

• The developed cyber-physical framework effectively coordinates CAVs to mitigate freeway bottlenecks.
• The system offers a promising approach to enhance traffic efficiency and safety in mixed or fully automated traffic environments.
• Real-time optimization capabilities make the framework suitable for practical implementation in intelligent transportation systems.