Dissertation Defense – Manuel Aurelio Rodriguez


Title: INTELLIGENT INTERSECTION MANAGEMENT THROUGH GRADIENT-BASED MULTI-AGENT COORDINATION OF TRAFFIC LIGHTS AND VEHICLES

Author: Manuel Aurelio Rodriguez

Date/Time: July 7, 2021 1pm-3pm

Zoom Meeting: https://umd.zoom.us/j/5586796034

List of committee members
Dr. Hosam Fathy (Chair)
Dr. Yancy Diaz-Mercado
Dr. Shapour Azarm
Dr. Jin-Oh Hahn
Dr. Derek Paley (Dean’s Representative)

Abstract
This dissertation examines the problem of coordinating both traffic lights plus connected and autonomous (CAVs) vehicles in urban traffic. We propose a coordination strategy for CAVs and smart traffic lights based on combining ideas from gradient-based multi-agent control, trajectory planning and control barrier functions.

The work is motivated by an extensive previous literature showing that traffic network synchronization has the potential to alleviate congestion while reducing fuel consumption and delay. The literature presents many algorithms for coordinating the traversal of intersections by connected and automated vehicles (CAVs), as well as the synchronization of traffic lights. However, the integrated solution of these two synchronization problems remains relatively unexplored. One of the main challenges of any algorithm proposed in this area consists of managing the trade-off between computational efficiency, communication requirements, and performance.

In this dissertation, the overall proposed control framework consists of coordinating the timing of the agents through decentralized gradient-based control, using novel potential energy functions that encode the desired interactions between the timing of connected agents. The negotiated timing is then achieved through acceleration minimizing trajectory planning. Finally, feasibility and safety constraints are handled by a regulator that uses control barrier functions. The strategy is validated in simulation for different types of intersections and traffic scenarios. We show potential savings in fuel and time of up 15% and 70% respectively.