Road Map Generation System Noise Correction
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Solution Overview
Problem
Current road map generation systems struggle to produce accurate road map data reflecting real-world conditions, particularly in poor road conditions where noise from image processing can lead to incorrect corrections of road markings, affecting the safety and reliability of automated driving systems.
Innovation Solution
A road map generation system that collects camera image data from vehicles, performs image processing to convert and combine images, extracts defective road markings, determines whether the defects are due to noise or real-world issues, and only corrects markings attributed to noise, ensuring accurate data reflection of real-world road conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If image processing is performed to correct road markings, then manufacturing precision of road map data is improved, but reliability deteriorates due to incorrect corrections of real-world defects
Solution Approach 1:
The system performs preliminary classification of defective portions into noise-based defects and actual road defects before correction. By预先 identifying and categorizing defect types using multiple image data comparisons and analysis, the system prepares correction strategies in advance, ensuring that only noise-based defects are corrected while preserving actual road conditions in the road map data
Solution Approach 2:
The system uses feedback from multiple image data sources and defect analysis results to control the correction process. By continuously comparing image data from different vehicles and time periods, and using the classification results as feedback, the system adjusts correction actions to distinguish between noise-based defects requiring correction and actual road defects requiring preservation
2Measurement precision
If dedicated data collection vehicles are used, then measurement precision of road conditions is improved, but productivity deteriorates due to limited coverage
Solution Approach 1:
The system merges image data from multiple general vehicles into a unified dataset for road condition analysis. By combining data from numerous vehicles traveling on different routes and at different times, the system achieves both high measurement precision through multiple observations and high productivity through extensive geographic coverage that would be impossible with a single dedicated vehicle
Solution Approach 2:
The system enables general vehicles to serve multiple functions: their primary transportation function plus a secondary data collection function. By making the road condition monitoring system universal and applicable to any equipped vehicle rather than requiring dedicated collection vehicles, the system simultaneously improves measurement precision through multiple data sources and productivity through broad vehicle fleet participation
Data Source
AI summary
A road map generation system is provided that collects camera image data of road conditions captured during traveling of vehicles on roads and generates road map data based on the camera image data. The system performs image processing on the collected camera image data to convert into orthographic images and to combine the orthographic images to generate a combined image. The system extracts a defective portion of a road marking on a road from the combined image and performs a determination of whether the defective portion of the road marking is due to noise at the image processing of the image processing device or there is a defect in the real world. When the defective portion of the road marking is due to the noise at the image processing, the system performs a correction process of the road marking.