Enforcing Liveness in Autonomous Traffic Management


  • Tsz-Chiu Au The University of Texas at Austin
  • Neda Shahidi The University of Texas at Austin
  • Peter Stone The University of Texas at Austin


Looking ahead to the time when autonomous cars will be common, Dresner and Stone proposed a multiagent systems-based intersection control protocol called Autonomous Intersection Management (AIM). They showed that by leveraging the capacities of autonomous vehicles it is possible to dramatically reduce the time wasted in traffic, and therefore also fuel consumption and air pollution. The proposed protocol, however, handles reservation requests one at a time and does not prioritize reservations according to their relative priorities and waiting times, causing potentially large inequalities in granting reservations. For example, at an intersection between a main street and an alley, vehicles from the alley can take an excessively long time to get reservations to enter the intersection, causing a waste of time and fuel. The same is true in a network of intersections, in which gridlock may occur and cause traffic congestion. In this paper, we introduce the batch processing of reservations in AIM to enforce liveness properties in intersections and analyze the conditions under which no vehicle will get stuck in traffic. Our experimental results show that our prioritizing schemes outperform previous intersection control protocols in unbalanced traffic.




How to Cite

Au, T.-C., Shahidi, N., & Stone, P. (2011). Enforcing Liveness in Autonomous Traffic Management. Proceedings of the AAAI Conference on Artificial Intelligence, 25(1), 1317-1322. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/7807



Special Track on Computational Sustainability and AI