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Accurate Lane Detection for Self-Driving Cars: An Approach Based on Color Filter Adjustment and K-Means Clustering Filter

Dongfang Liu

Computer and Information Technology, Purdue University, 610 Purdue Mall, West Lafayette, IN 47906, USA

E-mail Address: liu2538@purdue.edu

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Yaqin Wang

Computer and Information Technology, Purdue University, 610 Purdue Mall, West Lafayette, IN 47906, USA

E-mail Address: wang4070@purdue.edu

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Tian Chen

Department of Mathematics and Statistics, University of Toledo, 610 Purdue Mall, 2801 Bancroft Street, Toledo, OH 43606, USA

E-mail Address: tian.chen@utoledo.edu

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, and

Eric T. Matson

Computer and Information Technology, Purdue University, 610 Purdue Mall, West Lafayette, IN 47906, USA

E-mail Address: ematson@purdue.edu

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https://doi.org/10.1142/S1793351X20500038Cited by:6 (Source: Crossref)

Abstract

Lane detection is a crucial factor for self-driving cars to achieve a fully autonomous mode. Due to its importance, lane detection has drawn wide attention in recent years for autonomous driving. One challenge for accurate lane detection is to deal with noise appearing in the input image, such as object shadows, brake marks, breaking lane lines. To address this challenge, we propose an effective road detection algorithm. We leverage the strength of color filters to find a rough localization of the lane marks and employ a K-means clustering filter to screen out the embedded noises. We use an extensive experiment to verify the effectiveness of our method. The result indicates that our approach is robust to process noises appearing in input image, which improves the accuracy in lane detection.

Keywords:

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