Hierarchical cooperative eco-driving control for connected autonomous vehicle platoon at signalized intersections

Vehicles in the platoon can sufficiently incorporate the information via V2X communication to plan ecological speed trajectories and pass the intersection smoothly. Most existing eco-driving studies mainly focus on the optimal control of a single vehicle at an individual signalized intersection, while rarely involving the cooperative optimization of a group of vehicles at successive signalized intersections. In this study, a hierarchical cooperative eco-driving control for a connected autonomous vehicle (CAV) platoon is proposed to enhance traffic mobility and energy efficiency, wherein the velocity trajectory of the leading vehicle at each isolated signalized intersection is planned using the pseudo-spectral method, and then the cooperative optimization of following vehicles in the platoon is conducted via rolling optimization, with the aim of improving driving comfort, safety and energy economy for the platoon. The simulation results highlight that the proposed hierarchical cooperative eco-driving strategy can lead to preferable vehicle-following behaviours and platoon driving performance, and the overall energy consumption and trip time of vehicle platoon are respectively reduced by 26.10% and 2.83%, compared with that under manual driving. Furthermore, the overall energy economy is promoted by 4.95% and 4.60%, compared with cooperative adaptive cruise control and intelligent driver model-based platoon control strategies.

Automated summary

In this study, a hierarchical cooperative eco-driving control strategy is proposed, enabling vehicles in a platoon to plan ecological speed trajectories and pass intersections smoothly via V2X communication. Unlike existing eco-driving studies, this research focuses on the cooperative optimization of a group of vehicles at consecutive signalized intersections. Simulation results show that the proposed strategy can improve vehicle-following behaviors and platoon performance, with a 26.10% reduction in overall energy consumption and a 2.83% reduction in trip time compared to manual driving.