Camera Calibration System for Autonomous Vehicle Image Recognition
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Solution Overview
Problem
Camera calibration variations lead to inconsistencies in image recognition systems, affecting energy consumption, safety, and efficiency in autonomous vehicles, as cameras may exhibit different operational characteristics despite passing calibration tests.
Innovation Solution
A camera calibration system that includes a series of tests such as spatial frequency response, modulation transfer function, distortion, shading, boresight, ingress protection, thermal performance, and flare/ghost tests, generating calibration data stored in the camera's memory for use in adjusting operational parameters and improving manufacturing processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cameras undergo standard calibration tests, then cameras can be identified as operational according to specifications, but cameras still exhibit slight differences in operating characteristics that cause varying accuracy in image recognition systems
Solution Approach 1:
The patent applies preliminary action by performing additional calibration tests (spatial frequency response, distortion, shading, boresight tests) before cameras are deployed in autonomous vehicles. These preliminary tests generate individualized calibration parameters for each camera, ensuring that even though cameras pass standard specifications, they have personalized calibration data that compensates for manufacturing variations. This preliminary calibration action resolves the contradiction by ensuring both operational reliability and measurement precision in actual deployment scenarios.
2Measurement precision
If multiple calibration tests are performed on each camera, then individual calibration parameters can be generated to improve image recognition accuracy, but the calibration process time and complexity increase
Solution Approach 1:
The patent applies segmentation by dividing the calibration process into distinct, modular test components: spatial frequency response testing, distortion testing, shading testing, and boresight testing. Each test can be independently performed and processed. This segmentation allows for efficient parallel processing and automated execution, reducing overall calibration time while maintaining comprehensive calibration data collection for improved image recognition accuracy.
Solution Approach 2:
The patent applies parameter changes by automatically adjusting test parameters based on camera characteristics and test results. The system modifies exposure settings, focus parameters, and test target positioning dynamically during calibration. This adaptive parameter adjustment optimizes the calibration process efficiency, reducing the time required to obtain accurate calibration parameters while maintaining measurement precision.
3Measurement precision
If comprehensive calibration data is stored in camera memory, then individual camera characteristics can be compensated for in autonomous vehicle systems, but the memory requirements and data processing load increase
Solution Approach 1:
The patent applies the taking out principle by extracting only the essential calibration parameters from comprehensive test data and storing them in camera memory. Instead of storing all raw calibration data, the system extracts key parameters such as distortion coefficients, shading correction values, and boresight adjustments. This extraction reduces memory requirements and data processing load while maintaining the ability to compensate for individual camera characteristics in autonomous vehicle operations.
Data Source
AI summary
Implementations set forth herein relate to a camera calibration system for generating various types of calibration data by maneuvering a camera through a variety of different calibration test systems. The calibration data generated by the camera calibration system can be transmitted to the camera, which can locally store the calibration data. The calibration data can include spatial frequency response value data, which can be generated according to a spatial frequency response test that is performed by the camera calibration system. The calibration data can also include field of view values and distortion values that are generated according to a distortion test that is also performed by the camera calibration system. The camera calibration system can maneuver the camera through a variety of different calibration tests, as well as transmit any resulting calibration data to the camera for storage.