Acedo Aguilar, Jose Carlos (University Of Texas at El Paso), Wang, Shian (The University of Texas at El Paso)

A Simple and Efficient Speed Control Method for Autonomous Vehicles to Improve Traffic Performance

Scheduled for presentation during the Poster Session "Road Traffic Control I" (WeBT15), Wednesday, September 25, 2024, 14:30−16:30, Foyer

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 3, 2024

Abstract

It has been previously shown that human driving behavior is the culprit of traffic instability, causing stop-and-go waves even in the absence of traffic bottlenecks. It is expected that in the future with technological advancements, intelligently controlled autonomous vehicles (AVs) will be capable of smoothing traffic, reducing energy consumption, and improving urban mobility. Leveraging car-following principles, the first generation of AVs, i.e., adaptive cruise control (ACC) vehicles, provide low levels of automation and the opportunity to utilize vehicles as mobile actuators in traffic, making it possible to strategically influence human driver behaviors. In this paper, we develop a car-following controller for AVs that imitates the trajectory of the immediate preceding vehicle while considering the safety of its user. The proposed AV controller is easy to synthesize and implement since it requires only local traffic information that can be readily obtained via onboard sensors of an AV. To demonstrate the viability of this strategy and its impact on traffic flow, we simulate two distinct scenarios with varying degrees of market penetration rate (MPR) of AVs. The first scenario considers a synthetic lead vehicle trajectory, while the second employs a real speed profile gathered from a section of Highway 55 in Minnesota. The results show that the AV controller is effective in improving traffic smoothness and energy economy of a vehicle platoon, even at low MPRs, across distinct traffic conditions.