Probe Data Rasterization for Automatic Lane Geometry Correction
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Solution Overview
Problem
Conventional digital maps suffer from inaccurate lane geometry due to missing or erroneous data, which affects route guidance and vehicle autonomy, necessitating laborious and costly manual corrections.
Innovation Solution
A method and apparatus that rasterizes probe data to generate images representing probe density, speed, and heading, using machine learning to correct and update map geometry automatically, employing image processing and Generative Adversarial Networks to enhance map data accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual correction methods are used to fix missing or erroneous lane geometry data, then map accuracy can be improved, but the process becomes laborious and costly
Solution Approach 1:
The system enables map data to self-correct by automatically detecting errors and generating correction data through probe data analysis, eliminating the need for manual intervention and achieving both high accuracy and efficiency
Solution Approach 2:
Manual mechanical correction processes are replaced with an automated computational system that uses probe data, image processing, and machine learning algorithms to detect and correct lane geometry errors automatically
2Reliability
If conventional digital maps are updated frequently to maintain accuracy, then map reliability improves, but the cost and time required for updates increase
Solution Approach 1:
The system continuously collects and processes probe data in real-time, enabling ongoing map validation and correction without interruption, thereby maintaining high reliability while minimizing update time through continuous operation
Solution Approach 2:
The system performs preliminary detection and correction of map errors using probe data before they affect navigation accuracy, proactively maintaining map reliability and reducing the need for frequent emergency updates
3Measurement precision
If manual verification and correction of map data is performed, then measurement precision improves, but the process becomes more complex and time-consuming
Solution Approach 1:
Complex manual verification processes are replaced with automated image processing and machine learning systems that analyze probe data, detect lane geometry errors, and generate corrections algorithmically, maintaining high precision while reducing operational complexity
Solution Approach 2:
The system introduces an intermediary automated processing layer between raw probe data and final map corrections, using image processing algorithms and machine learning models to bridge data collection and map updating, thereby simplifying the overall process while maintaining accuracy
Data Source
AI summary
A method is provided for generation of map geometry through rasterization of probe data over time, where the generated map geometry can be used for map element generation. Methods may include: receiving probe data from a plurality of probe apparatuses within a geographic area where the probe data includes location information and time information; rasterizing the received probe data to generate an image corresponding to the probe data associated with the geographic area, where each pixel of the image includes a property representing at least one component of the probe data; processing the image to obtain one or more map elements; and updating a map database with the one or more map elements.