Unlocking Mutual Gains-An Experimental Study on Collaborative Autonomous Driving in Urban Environment

Area of Science:

• Transportation Engineering
• Autonomous Systems
• Game Theory

Background:

• Convoy driving offers societal benefits like reduced congestion and emissions.
• Individual vehicle participation in convoys is crucial but often overlooked.
• Existing research prioritizes collective benefits over individual incentives.

Purpose of the Study:

• To model societal benefits of convoy driving (traffic flow, emissions).
• To design and model individual-level incentives for convoy participation.
• To bridge the gap between societal goals and individual vehicle owner motivations.

Main Methods:

• Modeling mixed traffic flow using a fundamental diagram.
• Incorporating Connected Autonomous Vehicles (CAVs) penetration rates, coalition intensity, and size.
• Applying coalitional game theory to model collaborative convoy driving.
• Developing a novel utility function with incentives (insurance, fines, tolls).

Main Results:

• Mixed traffic flow dynamics analyzed based on CAV penetration, coalition intensity, and size.
• Identified the necessity of balancing CAV penetration, coalition factors, and speed for optimal benefits.
• Demonstrated the effectiveness of proposed incentives in encouraging convoy formation.

Conclusions:

• Convoy driving requires aligning road authority sustainability goals with individual vehicle owner benefits.
• A balanced approach to CAV integration and incentive design is key.
• This research promotes a mutually beneficial future for convoy driving.