Perception Visualization Tool for Label Discrepancy Review
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Solution Overview
Problem
Autonomous vehicles face inefficiencies in manually reviewing large numbers of labels generated by multiple sources, including models and human operators, for accuracy, as manual review is time-consuming and labor-intensive, especially when dealing with numerous objects and scenes.
Innovation Solution
A method is provided to facilitate operator review by generating a grid or visual list of cells associated with label discrepancies, allowing for searching, filtering, and sorting on a per-object basis, and offering interactive visual representations and metadata for detailed analysis, enabling faster identification of patterns and anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review of labels is performed to ensure accuracy, then label quality and model reliability are improved, but time consumption and labor intensity increase significantly
Solution Approach 1:
The patent segments the label review process by organizing labels into a grid layout where each cell represents a discrete label unit. This segmentation allows operators to systematically review labels in manageable portions rather than overwhelming bulk, improving both efficiency and accuracy tracking
Solution Approach 2:
The patent introduces an intermediary computing system that automatically generates the grid interface, tracks review progress, and manages label data. This intermediary system handles the time-consuming organizational and tracking tasks, allowing human operators to focus solely on the actual label verification work
2Measurement precision
If comprehensive review of all labels is conducted to identify patterns and anomalies, then detection precision is improved, but productivity decreases due to the sheer volume of labels
Solution Approach 1:
The grid interface segments labels into individual reviewable units, enabling operators to methodically examine each label while maintaining awareness of the overall distribution. This segmentation facilitates both comprehensive coverage and efficient processing by breaking down the overwhelming task into discrete, manageable steps
Solution Approach 2:
The patent employs visual indicators including color coding to highlight anomalies, discrepancies, and review status. This visual encoding allows operators to quickly identify problematic labels without reading each one in detail, maintaining high detection precision while significantly improving review throughput
3Loss of information
If detailed metadata and visual representations are provided for each label, then analysis depth is improved, but device complexity and interface complexity increase
Solution Approach 1:
The patent displays comprehensive label information including metadata and visual representations not by expanding horizontal space, but by utilizing the vertical dimension through collapsible details and layered information display. This allows full information retention while maintaining a clean, non-overwhelming interface layout
Solution Approach 2:
The interface segments information into hierarchical layers: essential label data is displayed prominently in the grid, while detailed metadata and visual representations are available on-demand through expanded views. This segmentation allows comprehensive information availability without permanently cluttering the main interface
Data Source
AI summary
Aspects of the disclosure relate to facilitating review of labels. For instance, a first type of label for a first set of labels and a second type of label for a second set of labels may be received. The first set of labels may be generated by a first labeling source and may classify one or more objects captured by a sensor of a vehicle. The second set of labels may be generated by a second labeling source different from the first labeling source and may classify the one or more objects. A search is conducted for objects associated with both the first type of labels for the first set of labels and the second type of label for the second set of labels in order to identify search results. The histograms may be generated from the search results and histograms may be provided for display to a human operator.