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

He, Shan; Adouane, Lounis · 2025 · Crossref

DOI: 10.1109/sose66311.2025.11083785

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Summary

This paper addresses the challenge of optimizing traffic flow and reducing fuel consumption for Connected and Automated Vehicles (CAVs) at unsignalized intersections. Motivated by the environmental impact of transportation and the potential of CAVs to alleviate congestion, the authors propose an Energy-Efficient Cooperative Decision-Making (EECDM) framework. The study aims to balance time efficiency and energy efficiency while ensuring vehicle safety, building upon existing Predicted Inter-Distance Profile (PIDP) methods. The methodology integrates a PIDP-based Multi-Risk Management Cooperative Optimization (MRMCO-PIDP) approach with a novel negotiation mechanism and a specific fuel consumption model. The intersection is modeled with four zones: core, interaction, optimizing, and buffer. The PIDP metric predicts inter-vehicle distances over a future horizon to assess collision risks, defined by a safety distance threshold. To adapt to dynamic environments, a four-part negotiation mechanism (input, optimization, communication, and action) allows CAVs to share state information and iteratively update strategies. The core innovation is an objective function that combines a time-efficiency term (minimizing crossing time and collision risk penalties) with an energy-efficiency term. This fuel model accounts for aerodynamic drag, rolling resistance, drivetrain efficiency, and acceleration/deceleration dynamics. The optimization process seeks a weighted balance between these two components to determine optimal speed profiles. Simulations were conducted in MATLAB across stochastic scenarios with traffic flows of 2000, 3000, and 4000 vehicles per hour, comparing the proposed EECDM method against the baseline MRMCO-PIDP method. Results demonstrate that EECDM significantly reduces both power and energy consumption. At 2000 veh/h, power consumption decreased by 5.62% and energy per kilometer by 4.97%. As traffic density increased to 3000 and 4000 veh/h, power consumption reductions rose to 10.4% and 10.6%, respectively, while energy per kilometer reductions remained around 5%. The study finds that improvements are more pronounced in high-density traffic because the baseline method prioritizes safety at the expense of energy efficiency, whereas EECDM effectively balances both objectives. The significance of this work lies in its contribution to sustainable intelligent transportation systems. By demonstrating that cooperative decision-making can simultaneously enhance traffic throughput and reduce fuel usage, the approach supports long-term goals for emission reduction and fuel economy. The authors conclude that the method is particularly valuable in urban scenarios with frequent congestion and note that future work will extend the framework from single intersections to broader urban traffic networks.

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• v2x connected vehicle