Sensor Data Annotation Using Temporal Correlation and False Positive Filtering
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Solution Overview
Problem
Existing methods for annotating driving scenario sensor data are time-consuming, error-prone, and expensive due to the reliance on manual labor, limiting their scalability and applicability to only small- to medium-sized projects.
Innovation Solution
An automated annotation method using neural networks for object recognition, temporal correlation, and plausibility criteria to minimize human intervention, enabling high-quality annotation at lower costs and faster project completion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation methods are used, then annotation quality can be maintained, but time consumption and costs increase dramatically
Solution Approach 1:
The system performs preliminary automated annotation using neural networks before manual review, pre-processing the data to identify and annotate objects. This preliminary action reduces the subsequent manual work required while maintaining high annotation quality, directly addressing the contradiction between time consumption and quality.
Solution Approach 2:
The patent introduces an automated annotation system as an intermediary between raw sensor data and final annotated data. This intermediary system uses neural networks to perform initial annotation, which then serves as input for manual review or direct output, reducing the time burden on human annotators while preserving quality through the collaborative process.
2Loss of time
If automated annotation methods are used, then time consumption is reduced, but annotation quality and accuracy decrease
Solution Approach 1:
The system implements feedback mechanisms where automated annotation results are evaluated and used to refine the neural network models. Human annotators review and correct automated annotations, and this feedback loop continuously improves the automated system's accuracy over time, allowing high quality annotations to be produced efficiently.
Solution Approach 2:
The patent employs parameter changes in the neural network configuration and processing parameters to optimize annotation quality. By adjusting parameters such as confidence thresholds, object detection sensitivity, and post-processing filters, the system achieves high annotation accuracy while maintaining automated efficiency, resolving the contradiction between speed and quality.
3Productivity
If keyframe spacing is increased to improve efficiency, then more annotations are created automatically, but manual corrections increase dramatically
Solution Approach 1:
The system performs preliminary automated annotation on all frames including non-keyframes using neural networks, creating draft annotations before the interpolation process. This preliminary action on densely spaced keyframes with automated processing reduces the manual correction burden that would otherwise increase with greater keyframe spacing, maintaining efficiency while controlling complexity.
Solution Approach 2:
The patent replaces the manual interpolation and correction mechanism with an automated neural network-based annotation system. Instead of relying on mechanical keyframe spacing and manual extrapolation, the system uses machine learning models to automatically annotate all frames, substituting the manual correction process with automated intelligence that handles dense keyframe processing efficiently.
4Measurement precision
If neural networks are optimized for high recall, then more objects are recognized, but false positive results increase
Solution Approach 1:
The system uses feedback from false positive identification to refine neural network parameters and add post-processing validation steps. Automated annotation results are reviewed and corrections are fed back into the system to improve the balance between recall and precision, allowing high object recognition completeness while reducing false positives through iterative improvement.
Solution Approach 2:
The patent employs parameter changes in the neural network configuration and post-processing filters to optimize the balance between recall and false positive rate. By adjusting parameters such as confidence thresholds, object detection sensitivity, and validation criteria, the system achieves high object recognition completeness while maintaining reliability through automated parameter optimization.
Data Source
AI summary
A computer-implemented method for annotating driving scenario sensor data, including the steps of receiving raw sensor data, the raw sensor data comprising a plurality of successive LIDAR point clouds and/or a plurality of successive camera images, recognizing objects in each image of the camera data and/or each point cloud using one or more neural networks, correlating objects within successive images and/or point clouds, removing false positive results on the basis of plausibility criteria, and exporting the annotated sensor data of the driving scenario.