research report

Energy-aware Trajectory Optimization of Connected and Automated Vehicle Platoons through a Signalized Intersection

Areas of Expertise

Intelligent Transportation Systems, Emerging Technologies, & Big Data

Abstract

Traffic signals, while serving an important function in coordinating vehicle movements through intersections, also cause frequent stops and delays, particularly when they are not properly timed. Such stops and delays contribute to a significant amount of fuel consumption and greenhouse gas emissions. The recent development of connected and automated vehicle (CAV) technology provides new opportunities to enable better control of vehicles and intersections, which in turn reduces fuel consumption and emissions. In this paper, the research team proposes platoon trajectory optimization (PTO) to minimize the total fuel consumption of a CAV platoon through a signalized intersection. In this approach, all CAVs in one platoon are considered as a whole, that is, all other CAVs follow the trajectory of the leading one with a time delay and minimum safety gap, which is enabled by vehicle-to-vehicle communication. Moreover, the leading CAV in the platoon learns of the signal timing plan just after it enters the approach segment through vehicle-to-infrastructure communication. The team compares the platoon trajectory optimization control with the other two controls, in which the leading vehicle adopts the optimal trajectory (LTO) or drives with maximum speed (AT), respectively, and the other vehicles follow the leading vehicle with a simplified Gipps’ car-following model. Furthermore, the research paper extends the controls to multiple platoons by considering the interactions between the two platoons. The numerical results demonstrate that platoon trajectory optimization has better performance than optimal trajectory and maximum speed, particularly when CAVs have enough space and travel time to smooth their trajectories. The reduction of travel time and fuel consumption can be as high as 40% and 30% on average, respectively, in the studied cases, which shows the great potential of CAV technology in reducing congestion and the negative environmental impact of automobile transportation.