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Presentation
Using Visual Lane Detection to Control Steering in a Self-Driving Vehicle
Proceedings fromthe 2015 European Alliance for Innovation’s International Conference on E-mobility and EmergingVehicular Technology (2015)
  • Kevin McFall, Kennesaw State University
Abstract
An effective lane detection algorithm employing the Hough transform and inverse perspective mapping to estimate distances in real space is utilized to send steering control commands to a self-driving vehicle. The vehicle is capable of autonomously traversing long stretches of straight road in a wide variety of conditions with the same set of algorithm design parameters. Better performance is hampered by slowly updating inputs to the steering control system. The 5 frames per second (FPS) using a Raspberry Pi 2 for image capture and processing can be improved to 23 FPS with an Odroid XU3. Even at 5 FPS, the vehicle is capable of navigating structured and unstructured roads at slow speed.
Keywords
  • Self-driving vehicle,
  • Hough transform,
  • dynamic threshold,
  • inverse perspective transform,
  • temporal integration,
  • angle control
Disciplines
Publication Date
October, 2015
Citation Information
Kevin McFall. "Using Visual Lane Detection to Control Steering in a Self-Driving Vehicle" Proceedings fromthe 2015 European Alliance for Innovation’s International Conference on E-mobility and EmergingVehicular Technology (2015)
Available at: http://works.bepress.com/kevin-mcfall/14/