Lane detection technique based on perspective transformation and histogram analysis for self-driving cars. (July 2020)

• Record Type: Journal Article
• Authors: Muthalagu, Raja; Bolimera, Anudeepsekhar; Kalaichelvi, V.
• Is Part Of: Computers & electrical engineering. Volume 85(2020)
• Publication Date: 2020-07
• Subjects: Contemporary computer vision techniques -- Perception algorithm -- Self-driving cars -- Lane detection -- Perspective transformation; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2020.106653
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: In this study, we present a perception algorithm that is based purely on vision or camera data. We focus on demonstrating a powerful end-to-end lane detection method using contemporary computer vision techniques for self-driving cars. We first present a minimalistic approach based on edge detection and polynomial regression which is the baseline approach for detecting only the straight lane lines. We then propose an improved lane detection technique based on perspective transformations and histogram analysis. In this latter technique, both straight and curved lane lines can be detected. To demonstrate the superiority of the proposed lane detection approach over the conventional approach, simulation results in different environments are presented.