Paper Info
Title
Traffic light mapping, localization, and state detection for autonomous vehicles
Abstract
Detection of traffic light state is essential for autonomous driving in cities. Currently, the only reliable systems for determining traffic light state information are non-passive proofs of concept, requiring explicit communication between a traffic signal and vehicle. Here, we present a passive camera based pipeline for traffic light state detection, using (imperfect) vehicle localization and assuming prior knowledge of traffic light location. First, we introduce a convenient technique for mapping traffic light locations from recorded video data using tracking, back-projection, and triangulation. In order to achieve robust real-time detection results in a variety of lighting conditions, we combine several probabilistic stages that explicitly account for the corresponding sources of sensor and data uncertainty. In addition, our approach is the first to account for multiple lights per intersection, which yields superior results by probabilistically combining evidence from all available lights. To evaluate the performance of our method, we present several results across a variety of lighting conditions in a real-world environment. The techniques described here have for the first time enabled our autonomous research vehicle to successfully navigate through traffic-light-controlled intersections in real traffic.
Year
DOI
Venue
2011
10.1109/ICRA.2011.5979714
Robotics and Automation
Keywords
DocType
ISSN
cameras,object detection,traffic engineering computing,autonomous driving,autonomous vehicles,backprojection,lighting condition,passive camera,robust real-time detection,tracking,traffic light mapping,traffic light state detection,traffic signal,triangulation,vehicle localization
Conference
1050-4729
ISBN
Citations 
PageRank 
978-1-61284-386-5
31
1.38
References 
Authors
8
4
Name
Order
Citations
PageRank
Jesse Levinson127116.24
Jake Askeland2311.38
Jennifer Dolson327114.03
Sebastian Thrun4203472302.56