Welcome to the home page for the Autonomous Intersection Management project. We are in the Learning Agents Research Group, which is part of the AI Laboratory in the Department of Computer Sciences at the University of Texas at Austin.

Project Description

This project "AIM"s to create a scalable, safe, and efficient multiagent framework for managing autonomous vehicles at intersections.

Intelligent vehicle technology is progressing very rapidly and recent advances suggest that autonomous vehicle navigation will be possible in the near future. At modern-day intersections, traffic lights and stop signs assist human drivers in conducting their vehicles safely through the cross traffic. However, in the future, with computers "behind the wheel", will it make sense to have intersection control mechanisms that were designed with today's human drivers in mind? With all the advantages computerized drivers offer - more precise control, better sensors, and quicker reaction times - we believe automobile travel can be made not only safer and easier, but much more efficient.

AIM is designed for the time when all (or most) vehicles are fully autonomous and connected. Experts, however, anticipate a long transition period during which human and autonomously operated vehicles will coexist. For this reason, we have developed the Hybrid AIM protocol that is suitable for such a transition period.

Project Members


Past Members

Past Collaborators

Resources for Visitors

The AIM4 Simulator

The Hybrid-AIM Simulator

Updated on November-2016

AIM4 patch for collision avoidance (by Diana Toader)

Updated on March-2017

This patch prevents collisions on the intersection boundries that may occur if setting high speed limit or acceleration parameters.
For more details please contact Diana Toader at dianaelenatoader@gmail.com

Selected Project Publications

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