Determination of road directions using feedback neural nets
Signal Processing - Intelligent systems for signal and image understanding
Intensity and edge-based symmetry detection with an application to car-following
CVGIP: 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
Rapidly Adapting Machine Vision for Automated Vehicle Steering
IEEE Expert: Intelligent Systems and Their Applications
A lane-departure identification based on LBPE, Hough transform, and linear regression
Computer Vision and Image Understanding
Finding multiple lanes in urban road networks with vision and lidar
Autonomous Robots
Applying fuzzy method to vision-based lane detection and departure warning system
Expert Systems with Applications: An International Journal
A lane-departure identification based on LBPE, Hough transform, and linear regression
Computer Vision and Image Understanding
Lane following and lane departure using a linear-parabolic model
Image and Vision Computing
A robust lane detection and verification method for intelligent vehicles
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Keeping the vehicle on the road: A survey on on-road lane detection systems
ACM Computing Surveys (CSUR)
Lane mark segmentation and identification using statistical criteria on compressed video
Integrated Computer-Aided Engineering
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This paper presents a feature-based machine vision system for estimating lane-departure of a traveling vehicle on a road. The system uses edge information to define an edge distribution function (EDF), the histogram of edge magnitudes with respect to edge orientation angle. The EDF enables the edge-related information and the lane-related information to be connected. Examining the EDF by the shape parameters of the local maxima and the symmetry axis results in identifying whether a change in the traveling direction of a vehicle has occurred. The EDF minimizes the effect of noise and the use of heuristics, and eliminates the task of localizing lane marks. The proposed system enhances the adaptability to cope with the random and dynamic environment of a road scene and leads to a reliable lane-departure warning system.