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http://dx.doi.org/10.5302/J.ICROS.2005.11.5.435

A Lane-Departure Identification Based on Linear Regression and Symmetry of Lane-Related Parameters  

Yi Un-Kun (부산경상대학 소방안전관리과)
Lee Joon-Woong (전남대학교 산업공학과)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.11, no.5, 2005 , pp. 435-444 More about this Journal
Abstract
This paper presents a lane-departure identification (LDI) algorithm for a traveling vehicle on a structured road. The algorithm makes up for the weak points of the former method based on EDF[1] by introducing a Lane Boundary Pixel Extractor (LBPE), the well known Hough transform, and liner regression. As a filter to extract pixels expected to be on lane boundaries, the LBPE plays an important role in enhancing the robustness of LDI. Utilizing the pixels from the LBPE the Hough transform provides the lane-related parameters composed of orientation and distance, which are used in the LDI. The proposed LDI is based on the fact the lane-related parameters of left and right lane boundaries are symmetrical as for as the optical axis of a camera mounted on a vehicle is coincident with the center of lane; as the axis deviates from the center of lane, the symmetrical property is correspondingly lessened. In addition, the LDI exploits a linear regression of the lane-related parameters of a series of successive images. It plays the key role of determining the trend of a vehicle's traveling direction and minimizing the noise effect. Except for the two lane-related parameters, the proposed algorithm does not use other information such as lane width, a curvature, time to lane crossing, and of feet between the center of a lane and the optical axis of a camera. The system performed successfully under various degrees of illumination and on various road types.
Keywords
LDI; LBPE; hough transform; linear regression; departure ratio;
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