Vehicle Sensor Calibration Using Real-World Target Re-Scanning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional sensor verification processes for vehicles are highly manual and costly, requiring skilled technicians and controlled calibration environments, which are expensive to set up and maintain, especially for testing the scanning range of long-range sensors.
Innovation Solution
A system and method that utilize sensor data from vehicles operating in real environments to identify and repeatedly scan targets, comparing sensor data to calibrate, align, and verify the performance of sensors, reducing the need for manual intervention and controlled environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional manual sensor verification processes are used, then measurement precision can be maintained, but device complexity and operational costs increase significantly
Solution Approach 1:
The sensor system performs self-verification by automatically comparing sensor data across multiple vehicles and time periods without requiring manual intervention. The system uses historical sensor data and geographic information to autonomously detect drift and trigger recalibration, enabling sensors to verify themselves through real-world operational data.
Solution Approach 2:
The verification system serves multiple functions simultaneously: it collects sensor data, compares readings across vehicles, detects drift automatically, triggers recalibration alerts, and maintains historical records. This multi-functional approach eliminates the need for separate manual verification processes while maintaining measurement precision.
2Manufacturing precision
If controlled calibration environments are used, then manufacturing precision is improved, but loss of time and productivity decrease
Solution Approach 1:
The system continuously collects and stores sensor data during normal vehicle operation, preparing verification data in advance. When drift is detected through automatic comparison with historical data and other vehicles, the system immediately triggers recalibration alerts, eliminating the need for scheduled manual calibration appointments and reducing overall calibration time.
Solution Approach 2:
The patent replaces manual mechanical calibration processes with automated electronic verification. Instead of technicians physically adjusting sensors in controlled environments, the system uses computational comparison of sensor data across multiple vehicles to detect drift and trigger electronic recalibration alerts, significantly reducing calibration time while maintaining precision.
3Reliability
If frequent sensor calibration is performed, then reliability is improved, but productivity and operational efficiency decrease
Solution Approach 1:
The system continuously monitors sensor performance by comparing current readings with historical data and data from other vehicles. When drift exceeds predetermined thresholds, the system provides feedback through recalibration alerts, triggering calibration only when necessary. This feedback-driven approach maintains sensor reliability while minimizing disruptions to vehicle operations.
Solution Approach 2:
The calibration schedule is dynamic rather than static. Instead of fixed periodic calibration intervals, the system adjusts calibration timing based on actual sensor performance data, environmental conditions, and detected drift patterns. This dynamic approach ensures calibration occurs only when performance degradation is detected, maintaining reliability while maximizing operational efficiency.
4Measurement precision
If manual sensor verification is used, then measurement precision is maintained, but loss of time and productivity increase
Solution Approach 1:
The verification process operates continuously in the background during normal vehicle operations. Sensor data is constantly collected, stored, and compared across the vehicle fleet without interrupting vehicle use. This continuous automated verification maintains measurement precision while eliminating the need for vehicles to be taken offline for manual verification procedures.
Solution Approach 2:
The system introduces an automated intermediary verification process that acts as a middle layer between sensor operation and manual calibration. This intermediary system continuously monitors sensor performance through data comparison and only intervenes when drift is detected, maintaining precision while minimizing the time vehicles spend in verification mode.
Data Source
AI summary
An example method involves detecting a sensor-testing trigger. Detecting the sensor-testing trigger may comprise determining that a vehicle is within a threshold distance to a target in an environment of the vehicle. The method also involves obtaining sensor data collected by a sensor of the vehicle after the detection of the sensor-testing trigger. The sensor data is indicative of a scan of a region of the environment that includes the target. The method also involves comparing the sensor data with previously-collected sensor data indicating detection of the target by one or more sensors during one or more previous scans of the environment. The method also involves generating performance metrics related to the sensor of the vehicle based on the comparison.