Traffic Facility Training Data Generation for LiDAR Map Accuracy

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Solution Overview

Problem

Existing technologies face challenges in generating precise road maps for autonomous vehicles due to limited data sets for traffic facilities, noise in point cloud data from LiDAR, and inaccurate color mapping, which affect the accuracy of object recognition and modeling.

Innovation Solution

A method and computer program that process traffic facility images to simulate camera captures, insert backgrounds, and correct color noise in point cloud data to generate learning data for traffic facilities, enhancing data availability and accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Area of stationary object

If aviation LiDAR is used to collect point cloud data for 3D modeling, then the coverage area is increased, but the point cloud density decreases toward the lower part of objects causing noise and reduced modeling accuracy

Engineering Contradiction:

Improvecoverage areaVSAvoidmodeling accuracy

Core Design Contradiction:

Area of stationary objectVSManufacturing precision

Solution Approach 1:

The patent segments the point cloud data processing into multiple density-based groups. Points are classified into first groups (higher density) and second groups (lower density), allowing different processing strategies for different regions. This segmentation resolves the contradiction by treating high-density and low-density areas differently, maintaining modeling accuracy while preserving broad coverage.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent applies local quality enhancement by selectively processing point clouds in low-density regions (second groups) with special attention to remove noise and enhance features. This ensures that areas with naturally lower point density (typically lower parts of objects) receive targeted quality improvement without affecting the overall coverage area.

Inventive Principle:

Principle #3Local quality

2Illumination intensity

If point cloud data is projected onto image to generate color map, then the visual representation is improved, but noise is generated due to wrong color application on recognized areas

Engineering Contradiction:

Improvevisual representationVSAvoidcolor noise

Core Design Contradiction:

Illumination intensityVSObject-affected harmful factors

Solution Approach 1:

The patent employs feedback mechanisms by comparing the projected color map with the original point cloud data and identifying discrepancies. The system detects areas where wrong colors have been applied and uses this feedback to correct the color mapping, thereby maintaining visual representation while eliminating color noise in recognized areas.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent converts the harmful effect of color projection noise into a benefit by using the projection process to identify and isolate problematic areas. The noise generation during projection actually helps reveal areas that need correction, which are then systematically corrected to produce a cleaner final color map.

Inventive Principle:

Principle #22Blessing in disguise (Convert harm into benefit)

3Loss of information

If algorithms for generating direction information using LiDAR point cloud data are used, then the direction information can be obtained, but the structure becomes very complicated reducing usability

Engineering Contradiction:

Improvedirection informationVSAvoidalgorithm complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent performs preliminary actions by pre-classifying and organizing point cloud data into meaningful groups based on density and spatial characteristics before direction information extraction. This preliminary organization simplifies subsequent processing steps and reduces the complexity of algorithms needed to extract direction information while maintaining completeness.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent creates simplified representations (copies) of the complex point cloud data structure that retain essential directional information. By working with these simplified copies rather than the full complex data structure, the system maintains direction information accuracy while significantly reducing algorithmic complexity and improving usability.

Inventive Principle:

Principle #26Copying

Data Source

PatentUS12561759B2Method for generate training data for transportation facility and computer program recorded on record-medium for executing method therefor

Publication Date: 2026.02.24 MOBILTECH

AI summary

Proposed is a method of generating learning data for traffic facilities, which can facilitate recognition of traffic facilities. The method includes the steps of: collecting, by a learning data generation unit, traffic facility sample images; generating, by the learning data generation unit, at least one processed image by processing the collected traffic facility sample images to be recognized as images captured by a camera installed in a vehicle; and generating, by the learning data generation unit, learning data by inserting a background into the generated at least one processed image.