Lane Detection via Luminance Edge Extraction
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Solution Overview
Problem
Existing driving support systems face challenges in accurately differentiating lane markings from buffering zones, leading to reduced detection accuracy due to increased processing loads when converting images to bird's eye view for width calculations.
Innovation Solution
A driving support system that extracts edge points based on luminance differences in captured images, calculates up and down edge lines, and identifies lane markings by excluding line candidates that do not meet specific conditions related to edge point density and presence of road studs, thereby reducing processing load and improving detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the captured image is converted to a bird's eye view image to calculate the width of zebra shape markings for recognizing buffering zones, then the detection accuracy of lane marking boundaries is improved, but the processing load increases
Solution Approach 1:
The patent extracts only the necessary information (luminance differences along scanning lines) from the captured image without performing full bird's eye view conversion. By extracting edge points based on luminance thresholds and analyzing only the relevant luminance difference patterns, the system obtains sufficient information to distinguish buffering zones from lane markings while avoiding the computationally expensive image transformation process.
Solution Approach 2:
The patent applies partial action by performing luminance difference calculations only along specific scanning lines where edge points are detected, rather than processing the entire converted bird's eye view image. This selective processing approach maintains detection accuracy for buffering zone identification while significantly reducing the overall processing load compared to complete image conversion and analysis.
2Measurement precision
If the system differentiates markings to determine whether a line constitutes the lane marking or buffering zone, then the detection accuracy is improved, but the complexity of the determination process increases
Solution Approach 1:
The patent applies local quality by analyzing luminance difference characteristics at specific locations (edge points along scanning lines) rather than examining the entire image or all markings uniformly. By focusing computational resources on local luminance patterns at detected edge points, the system achieves accurate differentiation between buffering zones and lane markings with simpler overall processing compared to global analysis methods.
Solution Approach 2:
The patent changes the analysis parameter from spatial geometry (width of zebra shapes in bird's eye view) to luminance intensity differences along scanning lines. This parameter transformation simplifies the determination process by using direct luminance comparisons with predefined thresholds rather than requiring complex geometric measurements of transformed image features.
Data Source
AI summary
The driving support system extracts edge points based on luminance of pixels, in which up edge point is the edge point where the luminance of the inner pixel is smaller than that of the outer pixel, down edge point is the edge point where the luminance of the outer pixel is smaller than the luminance of inner pixel. The system determines, among a plurality of line candidates acquired in accordance with the edge points location, a lane candidate excluding the line candidate that satisfies a predetermined exclusion condition, to be the lane marking, the line candidate determined as the lane marking being located most closely to the vehicle position. The exclusion condition includes a condition where the number of edge points of the up edge line is larger than or equal to a predetermined point threshold, compared to the number of edge points of the down edge line.