Title
Evasion planning for autonomous vehicles at intersections
Abstract
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.
Year
DOI
Venue
2012
10.1109/IROS.2012.6385936
IROS
Keywords
Field
DocType
aim,protocols,intersection control protocol,road vehicles,traffic signals,preemptive approach,mobile robots,failure analysis,traffic control,autonomous intersection management,autonomous vehicle,stop signs,evasion planning,road traffic,mechanical failure handling,collision avoidance,mathematical model,trajectory
Computer security,Computer science,Testbed,Road traffic,Control engineering,Exploit,Mechanical failure,Trajectory,Mobile robot,Distributed computing
Conference
ISSN
ISBN
Citations 
2153-0858
978-1-4673-1737-5
10
PageRank 
References 
Authors
0.64
7
5
Name
Order
Citations
PageRank
Tsz-Chiu Au154838.63
Chien-Liang Fok266339.24
Sriram Vishwanath34185445.45
Christine Julien467765.29
Peter Stone56878688.60