Neural Network Sensor Data Annotation with Quality Feedback
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Solution Overview
Problem
Current methods for annotating sensor data, particularly for autonomous driving, rely heavily on human labor, which is time-consuming and costly, and struggles to maintain high annotation quality with increasing data volumes.
Innovation Solution
A computer-implemented method that uses neural networks to automatically annotate sensor data frames by assigning data points and state attributes, with a quality metric-based feedback loop for retraining the neural networks to improve annotation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a purely manual approach is used for annotating sensor data, then annotation quality can be maintained through human judgment, but the work required increases linearly with project volume and time constraints
Solution Approach 1:
The annotation process is segmented into multiple stages: initial automated annotation by neural networks, sampling for quality assessment, selective manual correction of deficient samples, and iterative retraining. This segmentation allows the system to leverage automation for high-volume processing while applying human expertise only where needed, thus resolving the contradiction between quality and throughput.
Solution Approach 2:
A feedback loop is established where annotated samples are assessed for quality, and when quality metrics fall below thresholds, the system triggers manual correction and subsequent neural network retraining. This feedback mechanism ensures quality maintenance while minimizing manual intervention to only the necessary portions of the dataset, thereby improving overall productivity without sacrificing annotation quality.
2Productivity
If neural networks are used for automated annotation, then productivity increases and manual workload decreases, but annotation quality may deteriorate without time-consuming quality checks
Solution Approach 1:
Instead of performing exhaustive quality checks on all automated annotations, the system applies partial action by sampling only a subset of annotations for quality assessment. When the sampled subset meets quality thresholds, the entire batch is accepted, thereby maintaining productivity while ensuring adequate quality control through selective rather than universal verification.
Solution Approach 2:
The system implements continuous feedback by monitoring quality metrics of sampled annotations and dynamically adjusting the process: when quality falls below thresholds, it triggers manual correction and retraining cycles. This feedback-driven approach ensures that automated annotation maintains high quality standards while preserving the productivity benefits of automation.
3Measurement precision
If all sensor data frames are annotated and quality-checked manually, then high annotation quality can be ensured, but the time required increases linearly with data volume
Solution Approach 1:
The system applies partial action by performing quality assessment only on sampled subsets rather than all annotations. This selective sampling approach maintains quality assurance effectiveness while dramatically reducing the time investment required, as full manual verification is performed only when necessary based on sample results.
Solution Approach 2:
The system performs preliminary automated annotation using neural networks before any quality checks are conducted. This preliminary action handles the bulk of the annotation work automatically, so that subsequent quality assurance activities focus only on verification and correction of a smaller subset, thereby reducing overall time requirements while maintaining quality standards.
4Productivity
If the volume of delivered data increases or the timeframe is shortened, then the number of labelers required increases, but larger annotation projects become infeasible with humans alone
Solution Approach 1:
The system combines multiple functions into a unified automated framework: neural networks perform both initial annotation and quality assessment, the same system triggers and manages iterative retraining cycles, and quality thresholds govern the entire process. This multi-functional integration allows the system to handle large-scale annotation projects that would be infeasible with separate manual processes, managing complexity through systematic automation rather than increased human resources.
Data Source
AI summary
A computer-implemented method for automatically annotating sensor data includes: receiving a multiplicity of sensor data frames; annotating the multiplicity of sensor data frames using at least one neural network, wherein the annotating comprises assigning at least one data point to each sensor data frame and assigning at least one state attribute to each data point; grouping the data points on the basis of the at least one state attribute, wherein a first group comprises data points for which the at least one state attribute is in a defined value range; selecting a first sample of one or more data points from the first group; and determining a quality metric for the one or more data points in the first sample.