Cross-Sensor Vehicle Calibration for Object Position Drift
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Solution Overview
Problem
Existing vehicle sensor calibration methods, such as end of line and traditional online calibration, often rely on independent sensor measurements without cross-verification, leading to inaccuracies and delays due to manufacturing variability and environmental conditions.
Innovation Solution
A cross-sensor vehicle sensor calibration method that compares output values from multiple sensors to determine measurement differences, allowing for calibration adjustments or compensations based on convergence and statistical analysis, including recursive processes to optimize sensor accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional online calibration is performed independently for each sensor, then the calibration process is simple and quick, but the accuracy is reduced due to lack of cross-verification
Solution Approach 1:
The patent combines multiple independent sensor calibrations into a unified cross-sensor calibration process. By merging the calibration of different sensors (camera, radar, lidar) and comparing their measurements of the same objects, the system achieves higher accuracy while maintaining manageable complexity through automated coordination.
Solution Approach 2:
The system implements feedback by continuously comparing measurements from multiple sensors and using the discrepancies to adjust calibration parameters. The calibration system receives feedback from the measurement comparisons and automatically adjusts sensor calibrations to minimize inconsistencies, improving accuracy without requiring complex manual intervention.
2Reliability
If end of line calibration is used to establish ground truth, then the initial calibration is accurate, but manufacturing variability and environmental conditions cause drift over time
Solution Approach 1:
The patent implements continuous calibration through automated cross-sensor comparison that occurs continuously or periodically. Rather than a one-time EOL calibration, the system continuously monitors and adjusts sensor measurements against each other, maintaining accuracy despite manufacturing variability and environmental changes over time.
Solution Approach 2:
The calibration system performs self-service by automatically detecting measurement inconsistencies between sensors and initiating calibration adjustments without external intervention. The system uses its own sensor data to identify and correct calibration drift, maintaining reliability and precision autonomously in response to environmental conditions.
3Measurement precision
If cross-sensor calibration is implemented to improve accuracy, then sensor measurement consistency improves, but the computational requirements and processing time increase
Solution Approach 1:
The system applies partial action by selectively performing cross-sensor calibration only when necessary, such as when measurement inconsistencies exceed thresholds or during scheduled maintenance intervals. Rather than continuously processing all sensor data for calibration, the system performs calibration actions only when needed, reducing computational time while maintaining precision.
Solution Approach 2:
The calibration process is automated to perform self-service, reducing manual intervention time. The system automatically compares sensor measurements, identifies inconsistencies, and executes calibration adjustments without requiring manual setup or extensive processing, thereby minimizing time loss while achieving high measurement precision.
Data Source
AI summary
A method comprises: receiving first output values from a first sensor of a vehicle, the first output values reflecting a position of an object external to the vehicle; receiving second output values from a second sensor of the vehicle, the second output values reflecting the position of the object; determining a measurement difference regarding the position of the object based on the first and second output values; and performing an action regarding at least one of the first or second sensors based on determining the measurement difference.