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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

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.

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    Date Created
    • 2016
    Resource Type
  • Text
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    Note
    • Partial requirement for: M.S., Arizona State University, 2016
      Note type
      thesis
    • Includes bibliographical references (pages 37-39)
      Note type
      bibliography
    • Field of study: Computer science

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    by Joseph Campbell

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