A lane-departure identification based on LBPE, Hough transform, and linear regression

This paper presents a lane-departure identification (LDI) system of a traveling vehicle on a structured road with lane marks. As is the case with modified version of the previous EDF-based LDI approach [J.W. Lee, A machine vision system for lane-departure detection, CVIU 86 (2002) 52-78], the new system increases the number of lane-related parameters and introduces departure ratios to determine the instant of lane departure and a linear regression (LR) to minimize wrong decisions due to noise effects. To enhance the robustness of LDI, we conceive of a lane boundary pixel extractor (LBPE) capable of extracting pixels expected to be on lane boundaries. Then, the Hough transform utilizes the pixels from the LBPE to provide the lane-related parameters such as an orientation and a location parameter. The fundamental idea of the proposed LDI is based on an observation that the ratios of orientations and location parameters of left- and right-lane boundaries are equal to one as far as the optical axis of a camera mounted on a vehicle is coincident with the center of lane. The ratios enable the lane-related parameters and the symmetrical property of both lane boundaries to be connected. In addition, the LR of the lane-related parameters of a series of successive images plays the role of determining the trend of a vehicle's traveling direction and the error of the LR is used to avoid a wrong LDI. We show the efficiency of the proposed LDI system with some real images.

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

[2]  Marco Pasotti,et al.  A vision-based off-road alert system , 1996, Proceedings of Conference on Intelligent Vehicles.

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

[4]  Dean A. Pomerleau,et al.  Neural Network Perception for Mobile Robot Guidance , 1993 .

[5]  Dana H. Ballard,et al.  Computer Vision , 1982 .

[6]  O. Faugeras.  Three-dimensional computer vision: a geometric viewpoint , 1993 .

[7]  C. Ireland.  Fundamental concepts in the design of experiments , 1964 .

[8]  Ernst D. Dickmanns,et al.  EMS-vision: application to hybrid adaptive cruise control , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[9]  Gerd Wanielik,et al.  A new approach for lane departure identification , 2003, IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683).

[10]  Kwang-Ryul Baek,et al.  A CUMULATIVE DISTRIBUTION FUNCTION OF EDGE DIRECTION FOR ROAD-LANE DETECTION , 2001 .

[11]  Alberto Broggi.  A massively parallel approach to real-time vision-based road markings detection , 1995, Proceedings of the Intelligent Vehicles '95. Symposium.

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

[13]  A. Del Bimbo,et al.  Determination of road directions using feedback neural nets , 1993, Signal Process..

[14]  E D Dickmanns,et al.  AUTONOMOUS HIGH SPEED ROAD VEHICLE GUIDANCE BY COMPUTER VISION , 1987 .

[15]  Massimo Bertozzi,et al.  Real-time lane and obstacle detection on the GOLD system , 1996, Proceedings of Conference on Intelligent Vehicles.