M.S. (Master of Science)
Department of Electrical Engineering
In this thesis, detection of solid lines and dashed lines of lanes using augmented sliding window technique, clustering technique and combination of both augmented sliding window and clustering is discussed. The lane points are extracted using image processing techniques. The performance of lane detection using these techniques is analyzed. Lanes are simulated in a laboratory for the input data set. The input data set consists of curved lines of dashed and solid lines which split and merge. Further, partially obscured lanes are also tested with the algorithm. The center of the lanes from vehicle to horizon is found based on the lane width. The percentage of successful detection of lanes using various techniques is calculated and their performance is compared. The average detection accuracy of clustering and augmented sliding window is nearly 98% for the considered input set of video frames. Missing lane markings of obscured lanes are also estimated using the relative position of adjacent lanes.
Bhupathi, Keerti Chand, "Lane Detection of Autonomous Vehicles Using Clustering and Augmented Sliding Windows Techniques" (2020). Graduate Research Theses & Dissertations. 6860.
Northern Illinois University
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