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154964 https://hdl.handle.net/2286/R.I.40319 http://rightsstatements.org/vocab/InC/1.0/ All Rights Reserved 2016 v, 39 pages : color illustrations Masters Thesis Academic theses Text eng Campbell, Joseph Fainekos, Georgios Ben Amor, Heni Artemiadis, Panagiotis Arizona State University Partial requirement for: M.S., Arizona State University, 2016 Includes bibliographical references (pages 37-39) Field of study: Computer science 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 inferring traffic light status based on these patterns. Their performance is evaluated over real-world and simulation data sets, resulting in up to 97% accuracy in each set. Computer Science Computer Engineering Intelligent vehicles Perception Situational awareness Traffic signs and signals Pattern perception Intelligent transportation systems Automated vehicles--Technological innovations. Automated Vehicles Traffic light status detection using movement patterns of vehicles