Energy-Efficient Cooperative Decision-Making for CAVs: A Traffic Flow Optimization Approach

The deployment of Connected and Automated Vehicles (CAVs) has the potential to significantly improve traffic flow and decrease both congestion and fuel consumption. This study examines how speed management strategies employed by CAVs influence fuel consumption. We introduce a cooperative decision-making framework for CAVs navigating in unsignalized intersections for traffic flow optimization, utilizing the Predicted Inter-Distance Profile (PIDP). Building upon this framework, we have developed a fuel consumption model to establish a balanced optimization approach for a strategy that is both time- and energy-efficient, while guaranteeing the safety of the vehicles. The obtained results indicate that the proposed approach not only effectively reduces crossing time, but also significantly lowers fuel consumption. Through simulations of diverse random scenarios, we validate the proposed method's reliability and energy efficiency.