Determination of road directions using feedback neural nets
Signal Processing - Intelligent systems for signal and image understanding
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Neural Network Perception for Mobile Robot Guidance
Neural Network Perception for Mobile Robot Guidance
Digital Image Processing
Computer Vision
Rapidly Adapting Machine Vision for Automated Vehicle Steering
IEEE Expert: Intelligent Systems and Their Applications
A machine vision system for lane-departure detection
Computer Vision and Image Understanding
A vision-based autonomous lane following system for a mobile robot
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
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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.