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 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. / Dissertation/Thesis / Masters Thesis Computer Science 2016
| Identifer | oai:union.ndltd.org:asu.edu/item:40319 |
| Date | January 2016 |
| Contributors | Campbell, Joseph (Author), Fainekos, Georgios (Advisor), Ben Amor, Heni (Committee member), Artemiadis, Panagiotis (Committee member), Arizona State University (Publisher) |
| Source Sets | Arizona State University |
| Language | English |
| Detected Language | English |
| Type | Masters Thesis |
| Format | 45 pages |
| Rights | http://rightsstatements.org/vocab/InC/1.0/, All Rights Reserved |
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