Lane detection using spline model
Pattern Recognition Letters
An extended hyperbola model for road tracking for video-based personal navigation
Knowledge-Based Systems
Applying fuzzy method to vision-based lane detection and departure warning system
Expert Systems with Applications: An International Journal
A novel system for robust lane detection and tracking
Signal Processing
Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation
IEEE Transactions on Intelligent Transportation Systems
Robust Lane Detection and Tracking in Challenging Scenarios
IEEE Transactions on Intelligent Transportation Systems
Linear fuzzy space based road lane model and detection
Knowledge-Based Systems
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In driving assistance systems, lane detection can provide significant information for driving safety. In this paper, we proposed a novel real-time lane detection method to extract the location of lane marking lines based on inverse perspective mapping transform (top view) for the region of interest (ROI) of a video frame. The data were then filtered by a selective oriented Gaussian high pass filter, Hough transformation, and Kalman filter to give the initial regions to our optimized RANSAC (Random Sample Consensus) Bezier splines fitting algorithm, which is the main innovation in this paper. Our experimental results and accuracy evaluation indicated that the proposed lane detection algorithm could run robustly in real time, and could achieve an average speed of 32.32 ms per frame for a 320 x 240 pixel image and 41.64 ms for a 640 x 480 pixel image, with a correct detection rate of over 92.85%. Moreover, our method was found to be suitable for various conditions.