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Sensors 2018, 18(12), 4274;

Robust Lane-Detection Method for Low-Speed Environments

State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China
College of Civil Engineering, Shenzhen University, Shenzhen 518060, China
School of Electronic Information, Wuhan University, Wuhan 430072, China
Author to whom correspondence should be addressed.
Received: 7 November 2018 / Revised: 28 November 2018 / Accepted: 2 December 2018 / Published: 4 December 2018
(This article belongs to the Special Issue Perception Sensors for Road Applications)
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Vision-based lane-detection methods provide low-cost density information about roads for autonomous vehicles. In this paper, we propose a robust and efficient method to expand the application of these methods to cover low-speed environments. First, the reliable region near the vehicle is initialized and a series of rectangular detection regions are dynamically constructed along the road. Then, an improved symmetrical local threshold edge extraction is introduced to extract the edge points of the lane markings based on accurate marking width limitations. In order to meet real-time requirements, a novel Bresenham line voting space is proposed to improve the process of line segment detection. Combined with straight lines, polylines, and curves, the proposed geometric fitting method has the ability to adapt to various road shapes. Finally, different status vectors and Kalman filter transfer matrices are used to track the key points of the linear and nonlinear parts of the lane. The proposed method was tested on a public database and our autonomous platform. The experimental results show that the method is robust and efficient and can meet the real-time requirements of autonomous vehicles. View Full-Text
Keywords: lane detection; symmetrical local threshold (SLT); Bresenham line voting space (BLVS); Kalman filter lane detection; symmetrical local threshold (SLT); Bresenham line voting space (BLVS); Kalman filter

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Li, Q.; Zhou, J.; Li, B.; Guo, Y.; Xiao, J. Robust Lane-Detection Method for Low-Speed Environments. Sensors 2018, 18, 4274.

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