Evasion Planning for Autonomous Vehicles at Intersections (2012)
Tsz-Chiu Au, Chien-Liang Fok, Sriram Vishwanath, Christine Julien, and Peter Stone
Autonomous intersection management (AIM) is a new intersection control protocol that exploits the capabilities of autonomous vehicles to control traffic at intersections in a way better than traffic signals and stop signs. A key assumption of this protocol is that vehicles can always follow their trajectories. But mechanical failures can occur in real life, causing vehicles to deviate from their trajectories. A previous approach for handling mechanical failure was to prevent vehicles from entering the intersection after the failure. However, this approach cannot prevent collisions among vehicles already in the intersection or too close to stop because (1) the lack of coordination among vehicles can cause collisions during the execution of evasive actions; and (2) the intersection may not have enough room for evasive actions. In this paper, we propose a preemptive approach that pre-computes evasion plans for several common types of mechanical failures before vehicles enter an intersection. This preemptive approach is necessary because there are situations in which vehicles cannot evade without pre-allocation of space for evasion. We present a modified AIM protocol and demonstrate the effectiveness of evasion plan execution on a miniature autonomous intersection testbed.
In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, October 2012.

Tsz-Chiu Au Postdoctoral Alumni chiu [at] cs utexas edu
Peter Stone Faculty pstone [at] cs utexas edu