Lane Detection of Curving Road for Structural Highway With Straight-Curve Model on Vision

Curve is the traffic accident-prone area in the traffic system of the structural road. How to effectively detect the lane-line and timely give the traffic information ahead for drivers is a difficult point for the assisted safe driving. The traditional lane detection technology is not very applicable in the curved road conditions. Thus, a curve detection algorithm which is based on straight-curve model is proposed in this paper and this method has good applicability for most curve road conditions. First, the method divides the road image into the region of interest and the road background region by analyzing the basic characteristics of the road image. The region of interest is further divided into the straight region and the curve region. At the same time, the straight-curve mathematical model is established. The mathematical equation of the straight model is obtained by using the improved Hough transform. The polynomial curve model is established according to the continuity of the road lane-line and the tangent relationship between the straight model and the curve model. Then, the parameters of the curve model equation are solved by the curve fitting method. Finally, the detection and identification of the straight and the curve are realized respectively and the road lane-line is reconstructed. Experiments show that this method can accurately identify the curve lane-line, provide effective traffic information, make early warning, and it also has certain universality.

[1]  Yan Song,et al.  Lane marking detection based on adaptive threshold segmentation and road classification , 2014, 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014).

[2]  Chang-Hong Lin,et al.  An Efficient Method for Lane-Mark Extraction in Complex Conditions , 2012, 2012 9th International Conference on Ubiquitous Intelligence and Computing and 9th International Conference on Autonomic and Trusted Computing.

[3]  Van-Dung Hoang,et al.  Vanishing Point Based Image Segmentation and Clustering for Omnidirectional Image , 2013, ICIC.

[4]  Shutao Li,et al.  An Effective Lane Detection Algorithm for Structured Road in Urban , 2012, IScIDE.

[5]  Cláudio Rosito Jung,et al.  Real-Time Detection and Classification of Road Lane Markings , 2013, 2013 XXVI Conference on Graphics, Patterns and Images.

[6]  Jean Ponce,et al.  Vanishing point detection for road detection , 2009, CVPR.

[7]  Peng Hon.  Lane detection algorithm based on extended Kalman filter , 2015 .

[8]  Paul Schonfeld,et al.  Multi-objective highway alignment optimization incorporating preference information , 2014 .

[9]  Wenjing Zhou,et al.  Lane Detection Based on Improved Canny Detector and Least Square Fitting , 2013 .

[10]  Minho Lee,et al.  Robust Lane Detection Based On Convolutional Neural Network and Random Sample Consensus , 2014, ICONIP.

[11]  Kwanghoon Sohn,et al.  Real-time illumination invariant lane detection for lane departure warning system , 2015, Expert Syst. Appl..

[12]  Dajun Ding,et al.  An adaptive road ROI determination algorithm for lane detection , 2013, 2013 IEEE International Conference of IEEE Region 10 (TENCON 2013).

[13]  Rama Chellappa,et al.  A Learning Approach Towards Detection and Tracking of Lane Markings , 2012, IEEE Transactions on Intelligent Transportation Systems.

[14]  Cláudio Rosito Jung,et al.  Lane following and lane departure using a linear-parabolic model , 2005, Image Vis. Comput..

[15]  Kah Phooi Seng,et al.  Lane Detection and Kalman-Based Linear-Parabolic Lane Tracking , 2009, 2009 International Conference on Intelligent Human-Machine Systems and Cybernetics.

[16]  Sheroz Khan,et al.  A Hyperbola-Pair Based Lane Detection System for Vehicle Guidance , 2010 .

[17]  Ping Wang,et al.  Straight-Line Based Image Registration in Hough Parameter Space , 2011, 2011 International Workshop on Multi-Platform/Multi-Sensor Remote Sensing and Mapping.

[18]  Anirban Laha,et al.  An Empirical Evaluation of various Deep Learning Architectures for Bi-Sequence Classification Tasks , 2016, COLING.

[19]  Leibo Liu,et al.  Efficient lane detection system based on monocular camera , 2015, 2015 IEEE International Conference on Consumer Electronics (ICCE).

[20]  Weifeng Liu,et al.  Road Detection by Using a Generalized Hough Transform , 2017, Remote. Sens..

[21]  Kwanghoon Sohn,et al.  Gradient-Enhancing Conversion for Illumination-Robust Lane Detection , 2013, IEEE Transactions on Intelligent Transportation Systems.

[22]  Youngjoon Han,et al.  A real-time system of lane detection and tracking based on optimized RANSAC B-spline fitting , 2013, RACS.

[23]  Jianmin Xu,et al.  Lane Detection Algorithm for Night-time Digital Image Based on Distribution Feature of Boundary Pixels , 2013 .

[24]  Chang-Hong Lin,et al.  Lane-mark extraction for automobiles under complex conditions , 2014, Pattern Recognit..

[25]  Azzedine Boukerche,et al.  A real-time lane marking localization, tracking and communication system , 2016, Comput. Commun..

[26]  Azzedine Boukerche,et al.  Lane detection and tracking system based on the MSER algorithm, hough transform and kalman filter , 2014, MSWiM '14.

[27]  Ling Mao,et al.  Lane detection using Heuristic Search Methods based on color clustering , 2010, 2010 International Conference on Communications, Circuits and Systems (ICCCAS).

[28]  Christoph Stiller,et al.  Kalman Particle Filter for lane recognition on rural roads , 2009, 2009 IEEE Intelligent Vehicles Symposium.

[29]  Umar Ozgunalp,et al.  Lane detection by estimating and using restricted search space in Hough domain , 2017 .

[30]  Fumihiko Saitoh,et al.  Binarization of the Image due to Dynamic Convex Quadrilateral Region Segmentation using GA , 2014 .

[31]  Qiang Chen,et al.  A Real-time Lane Detection Algorithm Based on a Hyperbola-Pair Model , 2006, 2006 IEEE Intelligent Vehicles Symposium.

[32]  Charles Zegeer,et al.  Modeling the impact of spatial relationships on horizontal curve safety. , 2012, Accident; analysis and prevention.

[33]  Ying Wang,et al.  A lane marking detection and tracking algorithm based on sub-regions , 2014, Proceedings 2014 International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS).

[34]  余曦.  Lane departure warning system and method based on machine vision , 2012 .