Real-time lane detection in various conditions and night cases

In this paper, we propose a real-time lane detection algorithm based on a hyperbola-pair lane boundary model and an improved RANSAC paradigm. Instead of modeling each road boundary separately, we propose a model to describe the road boundary as a pair of parallel hyperbolas on the ground plane. A fuzzy measurement is introduced into the RANSAC paradigm to improve the accuracy and robustness of fitting the points on the boundaries into the model. Our method is able to deal with existence of partial occlusion, other traffic participants and markings, etc. Experiment in many different conditions, including various conditions of illumination, weather and road, demonstrates its high performance and accuracy

[1]  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.

[2]  Alexander Zelinsky,et al.  Robust vision based lane tracking using multiple cues and particle filtering , 2003, IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683).

[3]  Wilfried Enkelmann,et al.  A video-based lane keeping assistant , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

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

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

[6]  Charles E. Thorpe,et al.  SCARF: a color vision system that tracks roads and intersections , 1993, IEEE Trans. Robotics Autom..

[7]  A. Gern,et al.  Vision-based lane recognition under adverse weather conditions using optical flow , 2002, Intelligent Vehicle Symposium, 2002. IEEE.

[8]  Alberto Broggi,et al.  Vision-Based Road Detection in Automotive Systems: A Real-Time Expectation-Driven Approach , 1995, J. Artif. Intell. Res..

[9]  Ni Kai,et al.  THMR-V: an effective and robust high-speed system in structured road , 2003 .

[10]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[11]  U. Franke,et al.  Lane recognition in urban environment using optimal control theory , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[12]  Dean A. Pomerleau,et al.  RALPH: rapidly adapting lateral position handler , 1995, Proceedings of the Intelligent Vehicles '95. Symposium.

[13]  G. Jiang,et al.  Lane and obstacle detection based on fast inverse perspective mapping algorithm , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[14]  Amer Dawoud,et al.  A robust neural network multi-lane recognition system , 1998, Proceedings of the Tenth International Conference on Microelectronics (Cat. No.98EX186).

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

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

[17]  Anil K. Jain,et al.  Lane boundary detection using a multiresolution Hough transform , 1997, Proceedings of International Conference on Image Processing.

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

[19]  Kai Ni,et al.  THMR-V: an effective and robust high-speed system in structured road , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[20]  Charles E. Thorpe,et al.  UNSCARF-a color vision system for the detection of unstructured roads , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[21]  Alberto Broggi,et al.  A morphological model-driven approach to real-time road boundary detection for vision-based automotive systems , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.

[22]  C.R. Jung,et al.  A lane departure warning system based on a linear-parabolic lane model , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[23]  Charles E. Thorpe,et al.  Representation and recovery of road geometry in YARF , 1992, Proceedings of the Intelligent Vehicles `92 Symposium.

[24]  Joon Woong Lee,et al.  A Machine Vision System for Lane-Departure Detection , 2002, Comput. Vis. Image Underst..

[25]  Miki Haseyama,et al.  Estimation of the vanishing point for automatic driving system using a cross ratio , 2002, Systems and Computers in Japan.