Traffic Light Status Detection Using Movement Patterns of Vehicles

• Other Authors: Campbell, Joseph (Author)
• Format: Dissertation
• Language: English
• Published: 2016
• Subjects: Computer science; Computer engineering; Intelligent vehicles; Perception; Situation awareness
• Online Access: http://hdl.handle.net/2286/R.I.40319

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