ML-Based Sensor Data Resolution Control for Real-Time Detection
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Solution Overview
Problem
Resource-constrained environments, such as autonomous vehicles, face challenges in accurately and timely processing sensor data due to limited processing resources, necessitating efficient data processing and resolution management.
Innovation Solution
A machine learning model is trained to determine optimal data levels for processing regions of sensor data, allowing for differential resolution processing based on location, environment, and sensor types, optimizing resource usage by identifying areas that benefit from higher or lower resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data is processed at high resolution to maintain accuracy, then detection precision is improved, but processing time and memory requirements increase
Solution Approach 1:
The patent applies local quality by differentiating processing resolution across different spatial regions of sensor data. Critical regions (e.g., areas with detected objects or high-interest zones) are processed at high resolution to maintain detection precision, while non-critical regions are processed at lower resolution to reduce processing time and memory requirements. This spatially-variable processing strategy resolves the contradiction by making resolution a local rather than global property.
Solution Approach 2:
The patent segments sensor data into multiple regions of interest and non-interest areas, then applies different processing levels to each segment. By dividing the data space and applying selective resolution processing to different segments, the system maintains high detection precision where needed while reducing overall processing time through lower-resolution processing in other segments.
2Measurement precision
If sensor data is processed at high resolution to maintain accuracy, then detection precision is improved, but memory requirements increase
Solution Approach 1:
The patent reduces memory requirements by applying high resolution only to local regions where detection precision is critical, while storing and processing other regions at lower resolution. This localized high-quality processing approach maintains detection accuracy for important features while significantly reducing the total memory burden compared to uniform high-resolution processing of entire sensor datasets.
Solution Approach 2:
The patent segments sensor data into regions requiring high precision and regions where lower precision suffices, then allocates memory resources accordingly. Critical regions are maintained at high resolution in memory, while non-critical regions use reduced resolution, optimizing the balance between detection precision and memory consumption.
3Measurement precision
If uniform high resolution processing is applied to all sensor data, then detection accuracy is maintained, but resource consumption increases
Solution Approach 1:
The patent implements local quality by applying high-resolution processing only to specific regions of sensor data where detection accuracy is paramount, while using lower-resolution processing for other regions. This spatially-selective approach maintains detection accuracy for critical areas while reducing overall processing resource consumption, directly resolving the contradiction between uniform high-resolution processing and resource efficiency.
Data Source
AI summary
Techniques are discussed for determining a data level for portions of data for processing. In some cases, a data level can correspond to a resolution level, a compression level, a bit rate, and the like. In the context of image data, the techniques can determine a region of first image data to be processed a high resolution and a region of second image data to be processed at a low resolution. The regions can be determined by a machine learned algorithm that is trained to output identifications of such regions. Training data may be determined by identifying differences in outputs based on the first and second image data. The image data associated with the determined regions and the determined resolutions can be processed to perform object detection, classification, segmentation, bounding box generation, and the like, thereby conserving processing, bandwidth, and/or memory resources in real time systems.