Automated Driving Map-Camera Curvature Mismatch Correction
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Solution Overview
Problem
Existing systems rely on manual intervention by technical personnel to correct discrepancies between map and camera data in autonomous vehicles, which is time-consuming.
Innovation Solution
A system and method that automatically identifies mismatches between map and camera data by determining curvatures from both sources, generates case reports, and adjusts confidence levels or updates data based on rationality checks and environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual intervention by technical personnel is used to correct discrepancies between map and camera data, then data accuracy can be improved, but time consumption increases significantly
Solution Approach 1:
The system performs self-diagnosis and self-correction by automatically detecting mismatches between map data and camera data, generating case reports, and adjusting confidence levels without requiring manual intervention from technical personnel
Solution Approach 2:
The system establishes a feedback loop where curvature mismatches are detected, analyzed for rationality, and used to adjust confidence levels of data sources, which then feeds back into the navigation decision-making process for continuous improvement
2Productivity
If automated systems are implemented to detect and correct data mismatches, then productivity improves, but device complexity increases
Solution Approach 1:
The automated correction system is divided into distinct functional modules: curvature calculation from map data, curvature calculation from camera data, mismatch detection, rationality analysis, case report generation, and confidence level adjustment, allowing each module to be independently developed and maintained
Solution Approach 2:
The system introduces intermediate structures including case reports that document mismatches and rationality analysis that evaluates whether corrections are reasonable, serving as mediators between raw data and final navigation decisions
Data Source
AI summary
A vehicle includes a system and method of operating the vehicle. The system includes a camera, a map database and a processor. The camera obtains camera data of a location of a road being traversed by the vehicle. The map database provides map data of the location of the road. The processor determines a first curvature of the road at the location from the camera data, determines a second curvature of the road at the location from the map data, identifies a mismatch between the first curvature and the second curvature at the location of the road, generates a case report for one of the map data and the camera data at the location upon occurrence of the mismatch, and adjusts one of the map data and a confidence level in the camera data for the location based on the case report.