2022/06/16 by Ozgenur Kavas-Torris, Levent Guvenc, Levent Güvenç +2
Computer Science · Engineering · Mathematics · #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Multiagent Systems (cs.MA) #Numerical Analysis (math.NA) #Systems and Control (eess.SY) #Traffic control and management #Vehicle emissions and performance #cs.MA #cs.NA #cs.SY #eess.SY #electronic engineering #information engineering #math.NA
paper · pdf · doi:10.48550/arxiv.2206.08306
arxiv created 2022/06/16 · openalex publication_date 2022/06/16 · arxiv updated 2022/06/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, a comprehensive Eco-Driving strategy for CAVs is presented. In this setup, multiple driving modes calculate speed profiles ideal for their own set of constraints simultaneously to save fuel as much as possible, while a High Level (HL) controller ensures smooth transitions between the driving modes for Eco-Driving. This Eco-Driving deterministic controller for an ego CAV was equipped with Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) algorithms. Simulation results are used to show that the HL controller ensures significant fuel economy improvement as compared to baseline driving modes with no collisions between the ego CAV and traffic vehicles while the driving mode of the ego CAV was set correctly under changing constraints.