Vehicle Image Sensor Calibration Drift Detection
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Solution Overview
Problem
Conventional image-based sensor calibration systems in vehicles, performed on an assembly line, become inaccurate due to changes in vehicle conditions such as load and tire pressure, leading to misaligned surrounding view images that can distract drivers and affect safe navigation.
Innovation Solution
An apparatus and method that detect calibration requirements for image sensors by analyzing ground patterns and relative motion parameters using key points extracted from images, generating an output signal to trigger re-calibration when necessary, and utilizing vehicle motion sensors to adjust installation parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If image sensors are calibrated on an assembly line, then initial calibration is completed efficiently, but calibration accuracy deteriorates due to changes in vehicle conditions such as load and tire pressure
Solution Approach 1:
The system continuously monitors ground patterns in real-time images and compares the observed motion parameters against expected values to detect calibration drift. When drift is detected, the system triggers a re-calibration process, creating a closed-loop feedback mechanism that maintains calibration accuracy despite changing vehicle conditions
Solution Approach 2:
The calibration system automatically detects when recalibration is needed by analyzing ground pattern deformations and autonomously triggers the re-calibration process without requiring manual intervention, allowing the system to self-correct calibration drift caused by vehicle condition changes
2Measurement precision
If image sensors are re-calibrated frequently to maintain accuracy, then calibration precision is improved, but system complexity and processing time increase
Solution Approach 1:
Instead of performing full re-calibration routines frequently, the system performs lightweight ground pattern detection and analysis continuously, only triggering full re-calibration when necessary. This partial action approach maintains accuracy while minimizing the complexity and processing overhead of frequent calibration operations
3Measurement precision
If ground pattern detection is performed continuously, then calibration accuracy is maintained, but computational load and processing time increase
Solution Approach 1:
The system performs ground pattern detection at periodic intervals rather than continuously, analyzing ground patterns at selected moments during vehicle operation. This periodic approach maintains calibration accuracy while reducing the computational load and processing time compared to continuous monitoring
Data Source
AI summary
Described is a method for detecting a calibration requirement for image sensors in a vehicle. The method includes detecting a ground pattern in a generated image associated with a surrounding of a vehicle. The method includes extracting at least one key point associated with the detected ground pattern, from the generated image. The method includes determining a relative motion parameter associated with the extracted at least one key point based on tracking of the extracted at least one key point over a period of time. The method further includes detecting the calibration requirement for the image sensor based on the determined relative motion parameter and generating an output signal based on the detected calibration requirement.