Research Article
Using Visual Lane Detection to Control Steering in a Self-driving Vehicle
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@INPROCEEDINGS{10.1007/978-3-319-33681-7_77, author={Kevin McFall}, title={Using Visual Lane Detection to Control Steering in a Self-driving Vehicle}, proceedings={Smart City 360°. First EAI International Summit, Smart City 360°, Bratislava, Slovakia and Toronto, Canada, October 13-16, 2015. Revised Selected Papers}, proceedings_a={SMARTCITY360}, year={2016}, month={6}, keywords={Self-driving vehicle Hough transform Dynamic threshold Inverse perspective transform Temporal integration Angle control}, doi={10.1007/978-3-319-33681-7_77} }- Kevin McFall
Year: 2016
Using Visual Lane Detection to Control Steering in a Self-driving Vehicle
SMARTCITY360
Springer
DOI: 10.1007/978-3-319-33681-7_77
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.
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