A novel lane changing algorithm with efficient method of lane detection

This paper presents an efficient method of lane detection and apply the detection results in vehicle auto lane departure. The inverse perspective mapping image is taken for line detection. During image preprocessing, PPHT method and a third degree Bezier spline are applied for line fitting step. Meanwhile, linear clustering is taken to reduce the excessive lines. Not only the lines in the current driving lane can be detected, but also lines of neighboring lanes when vehicle is changing lanes can be detected. Besides, an algorithm of trajectory planning is introduced as a prerequisite for the implementation of lane changing. Fields results have shown the effectiveness of the proposed system.

[1]  Constantine Kotropoulos,et al.  Color image histogram equalization by absolute discounting back-off , 2007, Comput. Vis. Image Underst..

[2]  Gang Wang,et al.  Detection of geometric shape for traffic lane and mark , 2012, 2012 IEEE International Conference on Information and Automation.

[3]  Charles E. Thorpe,et al.  The YARF system for vision-based road following , 1995 .

[4]  Josef Kittler,et al.  Hypothesis Testing: A Framework for Analyzing and Optimizing Hough Transform Performance , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Rachid Nourine,et al.  VISUAL GUIDANCE OF AUTONOMOUS VEHICLE BASED ON FUZZY PERCEPTION , 1998 .

[6]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[7]  Karl C. Kluge,et al.  Extracting road curvature and orientation from image edge points without perceptual grouping into features , 1994, Proceedings of the Intelligent Vehicles '94 Symposium.

[8]  Richard O. Duda,et al.  Use of the Hough transformation to detect lines and curves in pictures , 1972, CACM.

[9]  S. Nedevschi,et al.  3D lane detection system based on stereovision , 2004, Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749).

[10]  Massimo Bertozzi,et al.  GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection , 1998, IEEE Trans. Image Process..

[11]  Romuald Aufrère,et al.  A model-driven approach for real-time road recognition , 2001, Machine Vision and Applications.

[12]  Yonina C. Eldar,et al.  A probabilistic Hough transform , 1991, Pattern Recognit..

[13]  Dong Joong Kang,et al.  Road lane segmentation using dynamic programming for active safety vehicles , 2003, Pattern Recognit. Lett..

[14]  Bo Zhang,et al.  Color-based road detection in urban traffic scenes , 2004, IEEE Transactions on Intelligent Transportation Systems.

[15]  Dinggang Shen,et al.  Lane detection and tracking using B-Snake , 2004, Image Vis. Comput..

[16]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[17]  Josef Kittler,et al.  Hypothesis testing: a framework for analysing and optimising Hough transform performance , 1990, [1990] Proceedings Third International Conference on Computer Vision.