A Comprehensive Lane Detection and Following System in Autonomous Vehicles

Hannah Grace Mason (Electrical and Computer Engineering, Lipscomb University)
Joe MacInnes (Computer Science, The College of Wooster)
Landon Bentley (Computer Science, The University of Alabama)

Lane detection and following are important aspects for the future of autonomous vehicles. Their project's goal was to explore a comprehensive implementation for lane detection and following using hardware-in-the-loop testing on a hybrid autonomous vehicle. Hue, lightness, and saturation are used with a perspective transform to detect lanes from an image and produce a bird’s eye view. A modification to existing path extraction algorithms is proposed that improves upon the shortcomings of current algorithms. The resulting algorithm generates waypoints from extracted lane markings with higher accuracy in curvy lanes with discontiguous markings. The resulting waypoints are sent to a Hoffmann steering controller which performs real-time trajectory correction. The project details the implementation of these subcomponents and a comparison to the results of competing approaches.

CAT Vehicle 2019

Brandon Dominique (New Jersey Institute of Technology)

Daniel Fishbein (Missouri State University)
Christopher
 Kreienkamp (University of Notre Dame)

Alex Day (Clarion University of Pennsylvania)
Sam Hum (Colorado College)
Riley Wagner
 (the University of Arizona)

Eric Av (Gonzaga University)
Hoang Huynh (Georgia State University-Perimeter College)
John Nguyen (University of Minnesota, Twin Cities)

Brandon Dominique's experience: A brief overview of the work that I did at the University of Arizona for their student-led self driving car project, the CAT Vehicle.

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