Fine-tuning a real-time speed planner for eco-driving of connected and automated vehicles

Abstract

A speed planner uses available information to enable automated vehicles to 'eco-drive, ' which includes eco-approach and departure at signalized intersections and leads to significant energy savings. Here we summarize the design of a proposed speed planner that generates multiple modes based on its analysis of optimal solutions, and we showcase the role of the parameters in the trajectory generation of each mode in the speed planner. For greater energy savings compared to human-driven vehicles, without sacrificing travel time, we optimally tune parameters through a global optimization method in a high-fidelity simulation framework. Finally, we analyze how the fine-tuning process makes driving more efficient and show its energy impacts. © 2020 IEEE.