On-Vehicle Camera Alignment Monitoring for Misalignment Root Cause Detection
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Solution Overview
Problem
On-vehicle camera misalignment can degrade the performance of spatial monitoring and autonomous vehicle control systems due to hardware issues, data quality problems, system degradation, vibration, or mechanical adjustments, affecting camera alignment with the ground reference.
Innovation Solution
A monitoring system that dynamically detects camera misalignment, identifies the root cause, and adjusts camera alignment by analyzing vehicle operating parameters, camera signal parameters, and image feature parameters, including vehicle speed, acceleration, yaw rate, road surface conditions, and image features, to control vehicle operations accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If on-vehicle cameras are used for spatial monitoring and autonomous vehicle control, then the vehicle can perform advanced driver assistance systems and autonomous functions, but camera misalignment due to hardware issues, vibration, or mechanical adjustment can degrade system performance
Solution Approach 1:
The system performs preliminary alignment monitoring by continuously capturing images and detecting feature points before misalignment affects autonomous control performance. The multi-level analysis process identifies alignment issues early through image quality assessment and feature point extraction, allowing preventive correction before degradation occurs
Solution Approach 2:
The system establishes a feedback loop where camera images are continuously analyzed through first-level and second-level processing to detect misalignment conditions. The controller receives feedback from image feature analysis and vehicle motion data, automatically adjusting or flagging alignment issues to maintain reliable autonomous control
2Measurement precision
If a monitoring system analyzes multiple image feature parameters and dynamic conditions to detect camera misalignment, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The monitoring system is segmented into distinct functional modules: first-level analysis for basic image quality and feature point detection, second-level analysis for detailed misalignment assessment, and controller-level decision-making. Each level processes specific parameters independently, reducing overall system complexity while maintaining high detection accuracy through hierarchical specialization
Solution Approach 2:
The system transitions from analyzing single-dimension parameters to multi-dimensional analysis by incorporating both spatial features (image feature points, road geometry) and temporal-dynamic features (vehicle speed, acceleration, yaw rate). This dimensional expansion enables comprehensive misalignment detection without proportionally increasing complexity, as each dimension is processed through dedicated algorithms
3Manufacturing precision
If the system dynamically adjusts camera alignment based on real-time analysis, then alignment accuracy is maintained under varying vehicle conditions, but processing time and computational load increase
Solution Approach 1:
The system implements periodic alignment monitoring at strategically determined intervals based on vehicle motion dynamics. During steady-state conditions, analysis occurs at lower frequency, while during transient conditions (acceleration, turning, uneven road surfaces), the monitoring frequency increases automatically. This periodic approach maintains alignment precision under varying conditions while minimizing unnecessary processing during stable operation
Data Source
AI summary
A system for on-vehicle camera alignment monitoring includes an on-vehicle camera in communication with a controller. The controller monitors vehicle operating parameters and camera signal parameters, and captures an image file from the on-vehicle camera. A first level analysis of the image file, the vehicle operating parameters, and the camera signal parameters is executed to detect dynamic conditions and image feature parameters that affect camera alignment. An error with one of the dynamic conditions or the image feature parameters that affects the camera alignment is detected. A second level analysis of the camera signal parameters is executed to identify a root cause indicating one of the dynamic conditions or the image feature parameters that affects the camera alignment based upon the error. A camera alignment-related fault is detected based upon the root cause, and vehicle operation is controlled based upon the camera alignment-related fault.