Vehicle Display Pixel Verification for Software Content Updates
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Solution Overview
Problem
The complexity of vehicle displays with customizable content and frequent software updates makes it challenging and resource-intensive to verify the accuracy of displayed information, requiring extensive manual checks of thousands of image combinations.
Innovation Solution
A system that generates and compares pixel data of generated images to control images, identifying differences using threshold criteria and generating output images only for significant discrepancies, reducing the number of images needing manual review by grouping and categorizing differences with unique identifiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual verification of all generated images is performed, then verification accuracy is improved, but verification time and resource consumption increase significantly
Solution Approach 1:
An automated image comparison system acts as an intermediary between image generation and manual verification. The system automatically compares generated images against control images, identifies differences, and prioritizes them for review. This intermediary process filters out obvious matches and highlights only significant discrepancies, maintaining verification accuracy while dramatically reducing the time and resources required for manual review of all images.
2Measurement precision
If all pixels in generated images are compared to control images, then detection precision is improved, but processing complexity increases
Solution Approach 1:
The image comparison process is segmented into multiple stages: automatic pixel-by-pixel comparison to identify all differences, threshold-based filtering to eliminate insignificant variations, and regional grouping to organize remaining differences by location and significance. This segmentation allows comprehensive pixel-level analysis without overwhelming processing complexity, as each stage handles a specific aspect of the comparison task.
Solution Approach 2:
Different processing strategies are applied to different regions of the image based on their importance. Critical review areas receive full pixel-level comparison and detailed analysis, while less critical areas use threshold-based filtering and summary statistics. This local quality approach ensures detection precision in important regions while reducing overall processing complexity through selective analysis.
3Productivity
If threshold-based filtering is applied to reduce differences for review, then review efficiency is improved, but risk of missing subtle errors increases
Solution Approach 1:
Multiple threshold parameters are used with different values for different types of errors and different image regions. Critical review areas use lower thresholds to capture subtle errors, while less critical areas use higher thresholds to maintain efficiency. The system also tracks the distribution of threshold applications and allows adjustment based on verification results, balancing review efficiency with error detection reliability through parameter optimization.
Data Source
AI summary
A system for comparing updated content on a vehicle display to control content, includes a display, a processor, and memory coupled to the processor and including one or more programs. The one or more programs generate multiple generated images for presentation on a vehicle display, compare generated pixel data of each of the generated images to control pixel data of a corresponding control image that is provided for each generated image, determine if the generated pixel data is different than the control pixel data, and generate an output image including pixels having generated pixel data that is different than the control pixel data.