Toward Smarter Transportation Systems with Autonomous Driving
FRIDAY, APRIL 10, 2026
1 PM – 2 PM
ALKEK TEACHING THEATER
Reception with speaker in attendance follows the presentation
Open and accessible to the general public
Abstract:
Self-driving cars could reshape transportation in ways we are only beginning to imagine. This talk explores two everyday problems they may help solve: traffic jams at intersections and limited parking spaces. First, I will describe a system that helps autonomous vehicles move through intersections with far fewer stops, potentially reducing congestion in busy cities. Second, I will discuss a new approach to parking lots in which self-driving cars can temporarily move out of one another’s way, allowing more cars to fit i n the same space. Along the way, I will explain the key ideas behind these systems and show how mathematics makes them possible.
High-Density Parking for Autonomous Vehicles. Many cities suffer from a shortage of parking spaces. Research in high-density parking (HDP) focuses on increasing parking lot capacity by allowing vehicles to block each other while temporarily yielding to other vehicles when autonomously requested. We propose the design of autonomous parking lots that enable the deployment of different parking strategies across regions within a parking lot while avoiding gridlock. Our simulation shows that autonomous parking lots can hold 60% more vehicles given the same amount of space.
Autonomous Traffic Management. Looking ahead to the time when autonomous cars will be common, we study how to utilize the capacity of autonomous vehicles to make transportation systems much more efficient. Dresner and Stone proposed an intersection control protocol for autonomous vehicle traffic called Autonomous Intersection Management (AIM), which is more efficient than traffic signals and stop signs. In this project, we expand the scope of AIM to autonomous traffic management in road networks and aim to find out the best transportation infrastructure for traffic that consists of a mix of autonomous vehicles and non-autonomous vehicles
For more about previous presentations and to view the event photos, please visit MSAM 2025
Speaker: Dr. Tsz-Chiu Au
Dr. Tsz-Chiu Au is an Associate Professor in Computer Science at Texas State University. He obtained his PhD in Computer Science from the University of Maryland, College Park, and he was a postdoctoral fellow at the University of Texas at Austin.
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His research interests in AI and Robotics include multiagent systems, multirobot systems, task and motion planning, and intelligent transportation systems. The topics of his funded projects are 1) high-density parking lots for autonomous vehicles, 2) traffic speed and congestion prediction for broadcast stations, 3) high-performance deep learning systems for disaster management, 4) telepresence security robot teams, and 5) AI companions for the elderly. He has extensive teaching experience in computer science courses, including AI, machine learning, and autonomous robots. He is an associate editor of IEEE Robotics and Automation Letters.
Sponsored by: Department of Mathematics and Texas Mathworks.
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