Multilane Detection and Tracking Based on Binocular Vision Stixel World Estimation and IPM

Road detection is the basic component of many intelligent vehicle systems. In this paper, a robust multi-lane detection method based on binocular vision is proposed. First, a fast estimation technique of the Stixel World, which is the outside environment representation of stereo-vision, is developed. Taking advantage of the estimate for free space under the plane road hypothesis, the proposed method is robust against on-road obstacles when detecting lane markings. Then, the bird-view of the transitable area is obtained through Inverse Perspective Mapping (IPM) Transform; Steerable filter, parallel parabolas modal and RANdom SAmple Consensus (RANSAC) technique are introduced for multi-lane fitting. Experimental results in real urban driving situations indicate that our algorithm is robust under various conditions.

[1]  Nanning Zheng,et al.  An efficient road detection method in noisy urban environment , 2009, 2009 IEEE Intelligent Vehicles Symposium.

[2]  H. Vincent Poor,et al.  An Introduction to Signal Detection and Estimation , 1994, Springer Texts in Electrical Engineering.

[3]  D. Cheng.  On logic-based intelligent systems , 2005, 2005 International Conference on Control and Automation.

[4]  Albert S. Huang,et al.  Probabilistic lane estimation for autonomous driving using basis curves , 2011, Auton. Robots.

[5]  Toby P. Breckon,et al.  Real-Time Traversable Surface Detection by Colour Space Fusion and Temporal Analysis , 2009, ICVS.

[6]  Luc Van Gool,et al.  Stixels estimation without depth map computation , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[7]  Henry Leung,et al.  Data fusion in intelligent transportation systems: Progress and challenges - A survey , 2011, Inf. Fusion.

[8]  Florentin Wörgötter,et al.  Combining Statistical Hough Transform and Particle Filter for robust lane detection and tracking , 2010, 2010 IEEE Intelligent Vehicles Symposium.

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

[10]  Mohamed Aly,et al.  Real time detection of lane markers in urban streets , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[11]  Monson H. Hayes,et al.  A Novel Lane Detection System With Efficient Ground Truth Generation , 2012, IEEE Transactions on Intelligent Transportation Systems.

[12]  Jing Li,et al.  Robust multilane detection and tracking in urban scenarios based on LIDAR and mono-vision , 2014, IET Image Process..

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

[14]  D. Cheng.  CONTROLLABILITY OF SWITCHED SYSTEMS , 2007 .

[15]  Takashi Naito,et al.  Road region estimation using a sequence of monocular images , 2008, 2008 19th International Conference on Pattern Recognition.

[16]  Ronen Lerner,et al.  Recent progress in road and lane detection: a survey , 2012, Machine Vision and Applications.

[17]  Uwe Franke,et al.  The Stixel World - A Compact Medium Level Representation of the 3D-World , 2009, DAGM-Symposium.

[18]  Uwe Franke,et al.  Efficient representation of traffic scenes by means of dynamic stixels , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[19]  Jorge J. Moré,et al.  The Levenberg-Marquardt algo-rithm: Implementation and theory , 1977 .