3D Road Line Generation via Medial Axis Point Cloud
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing three-dimensional digital roadbeds lack accurate representation of road lines due to inaccuracies in publicly available street maps and changes in road infrastructure, such as widening or reconfiguration of lanes.
Innovation Solution
A method and system for generating road lines in a three-dimensional digital roadbed by receiving a two-dimensional image of a roadbed, identifying road line pixels, translating these pixels into three-dimensional points, clustering points into a point cloud, generating a medial axis from the point cloud, and using the medial axis to create a generated road line.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If road lines are generated from publicly available street maps, then the process is simple and data is readily available, but the accuracy and precision of the three-dimensional digital roadbed deteriorates due to map inaccuracies and road changes
Solution Approach 1:
The patent replaces traditional map-based road line generation with an image processing system that uses computer vision algorithms to detect and extract road lines directly from images. This substitution of the mechanical/map-based system with an optical/image-based system resolves the contradiction by providing both ease of operation (automatic extraction from images) and high accuracy (direct visual measurement of current road conditions).
Solution Approach 2:
The patent transforms road line data from two-dimensional map coordinates into three-dimensional spatial coordinates with precise geometric parameters. By changing the parameter representation from abstract map data to concrete image-based coordinates and applying medial axis transformation, the system achieves both operational simplicity and measurement precision in the three-dimensional digital roadbed.
2Measurement precision
If multiple images or video are used to generate road lines, then accuracy may improve, but computational complexity and processing requirements increase
Solution Approach 1:
The patent extracts only the essential road line features from the image using edge detection and line extraction algorithms, rather than processing the entire image or video stream. This selective extraction of relevant information maintains accuracy while significantly reducing computational complexity by focusing only on the necessary geometric features for road line generation.
Solution Approach 2:
The patent applies preliminary image preprocessing steps such as edge detection and feature enhancement before main road line extraction. This preliminary action prepares the data in advance, making the subsequent processing more efficient and accurate while reducing the overall computational burden by pre-organizing the information in optimal formats.
3Manufacturing precision
If complex clustering algorithms are used to process road line points, then the quality of point cloud generation improves, but processing time and computational resources increase
Solution Approach 1:
The patent segments the road line points into distinct groups based on their spatial characteristics and geometric properties. By dividing the point cloud processing into smaller, manageable segments that can be processed independently and in parallel, the system achieves high-quality point cloud generation while reducing overall processing time through efficient resource utilization.
Data Source
AI summary
A method for generating road lines in a three-dimensional digital roadbed comprises: receiving a first two-dimensional image of a roadbed; identifying a first plurality of road line pixels in the first two-dimensional image of the roadbed, wherein each road line pixel of the first plurality of road line pixels corresponds to an identified road line of the first two-dimensional image; identifying a first plurality of road line three-dimensional points in the three-dimensional digital roadbed corresponding to the first plurality of road line pixels of the first two-dimensional image of the roadbed; clustering at least some road line three-dimensional points of the first plurality of road line three-dimensional points into a first three-dimensional point cloud; generating a first medial axis from the first three-dimensional point cloud; and generating, using the first medial axis, a first generated road line in the three-dimensional digital roadbed.