Lane Marking Detection Using Color Clustering for Worn Road Segments
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The deterioration of road surface markings poses challenges for advanced driver assistance systems, which rely on accurate detection of lane markings for improved driving comfort, safety, and efficiency, as existing technologies struggle to effectively monitor and respond to changes in marking quality and reliability.
Innovation Solution
A method and apparatus for detecting lane markings using image data processing, which involves identifying image data, defining road segments, recognizing boundary observations, clustering based on color or intensity, and outputting lane marking indicators to select appropriate driving functions, incorporating a memory, fusing module, segmenting module, and clustering module to analyze and report the state of lane markings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system performs detailed analysis of lane marking quality, then detection accuracy is improved, but processing time and computational complexity increase
Solution Approach 1:
The analysis process is divided into multiple stages: initial screening of image data, identification of candidate marking regions, detailed quality assessment of selected regions, and final quality determination. This segmentation allows the system to focus computational resources on critical areas while maintaining overall processing efficiency.
Solution Approach 2:
The system applies different levels of analysis to different portions of the road markings. High-detail analysis is applied to critical sections where precision is most important, while less critical areas receive simplified processing, optimizing the balance between accuracy and processing time.
Data Source
AI summary
Systems and methods for the detection and analysis of road markings and other road objects are described. A method for detection of road markings comprises identifying image data including lane markings associated with a road segment, defining a plurality of subsections for the road segment, identifying boundary recognition observations for the lane markings from the image data corresponding to the at least one of the plurality of subsections for the road segments, calculating one or more clusters for the boundary recognition observations according to color or intensity, and outputting a lane marking indicator indicating the color or the intensity, for the at least one of the plurality of subsections for the road segments, in response to the one or more clusters.