Detection and classification of road lanes with a frequency analysis

This paper presents a road lane detection and interpretation algorithm for driver assistance systems (DAS). The algorithm uses an edge filter to extract lane borders to which a straight lane model is fitted. Next, the lane mark type (continuous, discontinuous or merge) is recognized using a Fourier analysis. The line type is essential for a robust DAS. Nevertheless, it has been seldom considered in previous works. The knowledge of the line types of the road helps to guide the search for other lines, to automatically detect the type of the road (one-way, two way or highway), and to tell the difference between allowed and forbidden maneuvers, such as crossing a continuous line. Furthermore, the system is able to auto calibrate, thus easing the process of installation in commercial vehicles.

[1]  Ljubo Vlacic,et al.  Intelligent vehicle technologies : theory and applications , 2001 .

[2]  Josef Kittler,et al.  A Hough transform algorithm with a 2D hypothesis testing kernel , 1993 .

[3]  Gianni Conte,et al.  Automatic Vehicle Guidance: the Experience of the ARGO Autonomous Vehicle , 1999 .

[4]  Camillo J. Taylor,et al.  Stochastic Road Shape Estimation , 2001, ICCV.

[5]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[6]  Christopher M. Kreucher,et al.  LANA: a lane extraction algorithm that uses frequency domain features , 1999, IEEE Trans. Robotics Autom..

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

[8]  Daniel DeMenthon,et al.  A zero-bank algorithm for inverse perspective of a road from a single image , 1986, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[9]  R Risack.  ROBUST LANE RECOGNITION EMBEDDED IN A REAL-TIME DRIVER ASSISTANCE SYSTEM , 1998 .

[10]  Todd Jochem,et al.  Rapidly Adapting Machine Vision for Automated Vehicle Steering , 1996, IEEE Expert.

[11]  Romuald Aufrère,et al.  Accurate road following and reconstruction by computer vision , 2002, IEEE Trans. Intell. Transp. Syst..

[12]  Alberto BroggiDipartimento di Ingegneria dell ' InformazioneUniversit.  Robust real-time lane and road detection in critical shadow conditions , 1995 .

[13]  Sridhar Lakshmanan,et al.  A deformable-template approach to lane detection , 1995, Proceedings of the Intelligent Vehicles '95. Symposium.

[14]  C. Hilario,et al.  Model based vehicle detection for intelligent vehicles , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[15]  Dinggang Shen,et al.  Lane detection using spline model , 2000, Pattern Recognit. Lett..

[16]  Ernst D. Dickmanns,et al.  Recursive 3-D Road and Relative Ego-State Recognition , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Alan L. Yuille,et al.  Fundamental Limits of Bayesian Inference: Order Parameters and Phase Transitions for Road Tracking , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  A. Fuze,et al.  Reconstruction of 3-D Road Geometry from Images for Autonomous Land Vehicles , 1990 .

[19]  Josef Kittler,et al.  An Optimizing Line Finder Using a Hough Transform Algorithm , 1997, Comput. Vis. Image Underst..

[20]  B. T. Thomas,et al.  Navigation of An Autonomous Road Vehicle Using Lane Boundary Markings , 1993 .

[21]  Maria Petrou,et al.  A Hough transform algorithm with a 2D hypothesis testing kernel , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol. III. Conference C: Image, Speech and Signal Analysis,.