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Traffic Light Status Detection Using Movement Patterns of Vehicles

Abstract Traditional methods for detecting the status of traffic lights used in autonomous vehicles may be susceptible to errors, which is troublesome in a safety-critical environment. In the case of vision-based recognition methods, failures may arise due to disturbances in the environment such as occluded views or poor lighting conditions. Some methods also depend on high-precision meta-data which is not always available. This thesis proposes a complementary detection approach based on an entirely new source of information: the movement patterns of other nearby vehicles. This approach is robust to traditional sources of error, and may serve as a viable supplemental detection method. Several different classification models are presented for inferri... (more)
Created Date 2016
Contributor Campbell, Joseph (Author) / Fainekos, Georgios (Advisor) / Ben Amor, Heni (Committee member) / Artemiadis, Panagiotis (Committee member) / Arizona State University (Publisher)
Subject Computer science / Computer engineering / Intelligent vehicles / Perception / Situation awareness
Type Masters Thesis
Extent 45 pages
Language English
Reuse Permissions All Rights Reserved
Note Masters Thesis Computer Science 2016
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis