Next Article in Journal
Research on Path Planning Model Based on Short-Term Traffic Flow Prediction in Intelligent Transportation System
Previous Article in Journal
Data Compression Based on Stacked RBM-AE Model for Wireless Sensor Networks
Previous Article in Special Issue
Multi-Target Detection Method Based on Variable Carrier Frequency Chirp Sequence
Article Menu

Export Article

Open AccessArticle
Sensors 2018, 18(12), 4274; https://doi.org/10.3390/s18124274

Robust Lane-Detection Method for Low-Speed Environments

1
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China
2
College of Civil Engineering, Shenzhen University, Shenzhen 518060, China
3
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)
Full-Text   |   PDF [8019 KB, uploaded 4 December 2018]   |  

Abstract

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

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

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top