Vehicle Lane Deviation Detection Using Aerial View Lane Fitting
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Solution Overview
Problem
Existing lane detection technologies struggle to accurately determine vehicle deviation due to the complexity of road environments, leading to inefficiencies in driverless or assisted driving systems.
Innovation Solution
A method involving image preprocessing, perspective transformation, and sliding window fitting to enhance lane line detection, including distortion correction, binary image generation, and dynamic sliding window adjustment for precise lane line identification, followed by distance calculation to assess vehicle deviation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional lane detection technology is used, then the system is simple to implement, but the detection accuracy deteriorates due to road complexity
Solution Approach 1:
The patent divides the lane detection task into multiple processing stages: image acquisition, preprocessing (distortion correction, binary conversion), perspective transformation, and sliding window fitting. Each stage handles a specific aspect of the detection problem, improving overall accuracy while keeping each individual processing step manageable and well-defined
Solution Approach 2:
The patent performs preliminary actions by conducting distortion correction and binary image conversion before the actual lane line detection. The sliding window method also performs preliminary fitting on image blocks before determining final lane lines. These preliminary processing steps prepare the data in advance, making the subsequent detection more accurate and reliable
2Adaptability or versatility
If complex road environments are considered, then the detection coverage is improved, but the detection accuracy deteriorates
Solution Approach 1:
The patent changes key parameters including the perspective transformation parameters that convert different road views to a standardized aerial view, and the sliding window parameters (size, step length) that adapt to different lane line configurations. This allows the system to handle various road environments while maintaining detection accuracy through parameter optimization
Solution Approach 2:
The patent replaces traditional mechanical or rule-based lane detection methods with image processing and mathematical transformation techniques. By using perspective transformation and sliding window fitting algorithms, the system achieves better adaptability to complex road environments while maintaining precision through computational rather than mechanical approaches
3Measurement precision
If more processing steps are added to improve detection accuracy, then the measurement precision is improved, but the processing time increases
Solution Approach 1:
The patent segments the image processing into distinct, optimized stages: preprocessing (distortion correction, binary conversion), perspective transformation, and sliding window fitting. This segmentation allows each stage to be processed efficiently with appropriate algorithms, reducing overall processing time while maintaining high detection precision through specialized handling of each processing aspect
Data Source
AI summary
A method for detecting vehicle deviation, an electronic device and a storage medium are provided. In the method, a first foreground image is acquired, a first corrected image is obtained by performing a distortion correction on the first foreground image and an aerial view is obtained. Based on the aerial view, a distribution map of non-zero pixel points is generated. An initial position of a left lane line and an initial position of a right lane line in the aerial view are determined. A first curve corresponding to the left lane line and a second curve corresponding to the right lane line are fitted. It can be determined whether the vehicle deviates from any lane line according to a distance between the vehicle and any lane line. The method can effectively detect lane lines, and improve an accuracy of identifying lane line detection.