Lane Boundary Detection via Pixel Intensity Comparison
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles face challenges in accurately identifying lane boundaries for navigation due to inaccuracies in map information and changes in road conditions, such as construction zones.
Innovation Solution
A method that uses an image-capture device to receive images of the road and identifies lane markers by comparing pixel intensities with neighboring pixels, determining the likelihood of a pixel belonging to a lane marker, and providing instructions to control the vehicle based on this likelihood.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If map information is used for lane boundary detection, then navigation can be provided, but the accuracy deteriorates due to outdated map information and road condition changes
Solution Approach 1:
The system performs preliminary actions by capturing images and detecting lane markers in advance to update navigation data before it becomes outdated. The image capture device continuously monitors road conditions and detects lane markers proactively, allowing the system to maintain accurate navigation information without waiting for map updates.
Solution Approach 2:
The system implements feedback by using detected lane markers from real-time images to continuously update and correct navigation data. The pixel intensity comparison method provides feedback on actual road conditions, allowing the system to adjust lane boundary detection accuracy dynamically based on current visual information rather than relying on static map data.
2Measurement precision
If pixel intensity comparison with neighboring pixels is used, then lane marker identification accuracy improves, but computational complexity increases
Solution Approach 1:
The system applies segmentation by dividing the image into individual pixels and processing them in discrete units. Each pixel's intensity is compared independently with its neighboring pixels, allowing the complex detection task to be broken down into simple, manageable comparisons that can be executed efficiently by the processing system.
Solution Approach 2:
The method uses local quality by comparing each pixel's intensity only with its immediate neighboring pixels rather than analyzing the entire image globally. This localized approach maintains high detection accuracy for lane markers while significantly reducing computational complexity, as each pixel requires comparison with only a small fixed number of neighbors.
Data Source
AI summary
Methods and systems for lane boundary detection using images are described. A computing device may be configured to receive, from an image-capture device coupled to a vehicle, an image of a road of travel of the vehicle. The computing device may be configured to identify a pixel in the image based on an intensity of the pixel and a comparison of the intensity of the pixel to respective intensities of neighboring pixels. Based on the intensity of the pixel and the comparison, the computing device may be configured to determine a likelihood that the pixel belongs to a portion of the image depicting a lane marker on the road. Based at least on the likelihood, the computing device may be configured to and provide instructions to control the vehicle.