Sensor Data Annotation Verification With Priority-Based Error Routing
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Solution Overview
Problem
Verifying annotations of sensor data for self-driving vehicles is complex and difficult due to inaccuracies in the training data, which can lead to misidentification of objects and inefficiencies in the verification process.
Innovation Solution
A signal processing system that routes annotations of sensor data based on error priority levels by comparing the annotations with annotation criteria data, enabling efficient verification and quality assurance of the training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual verification methods are used to verify sensor data annotations, then verification accuracy can be maintained, but the verification process becomes time-consuming and inefficient
Solution Approach 1:
The patent introduces an automated verification system that acts as an intermediary between annotation creation and final acceptance. This system uses sensor data processing to automatically check annotations against ground truth, reducing manual verification time while maintaining accuracy through systematic error detection and priority-based routing of annotations requiring human review.
Solution Approach 2:
The patent replaces manual mechanical verification processes with automated computational verification. The system uses algorithms to process sensor data and compare annotations automatically, substituting human manual inspection with machine-based verification that operates faster and consistently without fatigue.
2Reliability
If comprehensive verification of all annotations is performed, then data quality improves, but processing costs and computational resources increase
Solution Approach 1:
The patent applies local quality by differentiating verification intensity based on annotation characteristics. High-priority annotations with potential safety impacts receive comprehensive automated verification, while lower-priority annotations use streamlined verification. This selective approach maintains data quality for critical elements while reducing overall processing costs.
Solution Approach 2:
The patent changes verification parameters dynamically based on annotation priority levels and error types. The system adjusts verification depth, routing decisions, and review thresholds according to the specific annotation context, optimizing the balance between data quality assurance and computational resource consumption.
3Productivity
If automated verification systems are implemented, then verification efficiency increases, but system complexity increases
Solution Approach 1:
The patent segments the verification system into modular components: automated verification modules, priority determination modules, routing modules, and human review coordination modules. This segmentation allows each component to perform a specific function efficiently, improving overall verification productivity while managing complexity through modular design that can be independently developed and maintained.
Data Source
AI summary
Provided are methods for automated verification of annotated sensor data, which can include receiving annotated image data associated with an image, wherein the annotated image data comprises an annotation associated with an object within the image, determining an error with the annotation based at least in part on a comparison of the annotation with annotation criteria data associated with criteria for at least one annotation, determining a priority level of the error, and routing the annotation to a destination based at least in part on the priority level of the error. Systems and computer program products are also provided.