Dashed Lane-Line Component Extraction for Autonomous Mapping
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Solution Overview
Problem
Existing systems require significant human resources and time for manually verifying and labeling dashed lane lines, leading to potential errors and inefficiencies in map data accuracy for autonomous vehicles.
Innovation Solution
Systems and methods automatically extract and update map data to identify individual components of dashed lane lines by analyzing relationships across different portions of the lane, using techniques such as Fast Fourier Transform (FFT) analysis to determine locations, lengths, and orientations of marked and unmarked components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual verification and labeling of dashed lane lines is performed, then map data accuracy can be maintained, but human resources and time consumption increase significantly
Solution Approach 1:
The system performs self-labeling of dashed lane line components by automatically detecting and extracting individual dashes, gaps, and patterns from road surface markings. The automated component extraction and labeling process eliminates the need for manual human verification while maintaining high accuracy in map data annotation.
Solution Approach 2:
The patent replaces manual mechanical labeling processes with automated computer vision and signal processing systems. The system uses image processing, pattern recognition, and mathematical operations to automatically extract and label dashed lane line components, substituting human labor with automated technological systems.
2Measurement precision
If manual labeling of dashed lane lines is performed, then labeling can be completed, but human error and mistakes increase
Solution Approach 1:
The automated system performs self-verification through multiple stages of processing including thresholding, morphological operations, and pattern recognition. The system cross-checks extracted components against the original image data and maintains consistent labeling standards without human intervention, thereby eliminating human errors.
Solution Approach 2:
The system incorporates feedback mechanisms where extracted lane line components are validated against the original sensor data and map context. The automated labeling process includes verification steps that ensure consistency and accuracy, allowing the system to correct potential errors through iterative validation.
3Productivity
If automated extraction of dashed line components is implemented, then productivity increases, but system complexity increases
Solution Approach 1:
The patent segments the dashed lane line detection process into distinct functional modules: image preprocessing, thresholding, morphological operations, pattern recognition, and component extraction. Each module handles a specific aspect of the labeling task, making the overall complex system manageable through modular design while maintaining high productivity.
Data Source
AI summary
In various examples, systems and methods described herein may determine individual components of a dashed line based at least on identifying relationships across different portions of the dashed line. For instance, input data representing a road surface may be analyzed and a representation associated with a dashed line may be determined. In some instances, the representation may be generated based at least on intensity values associated with points corresponding to the input data. Then, based at least on the representation, information associated with one or more components of the dashed line may be determined. For instance, the representation may be indicative of the relationships across the different portions of the dashed line, and these relationships may be used to determine the information associated with the one or more components of the dashed line.