Variable Resolution Sensor Data Processing for Autonomous Vehicles
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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 variable resolution processing based on location, environment, and sensor types, optimizing resource usage by processing high-resolution data where necessary and reducing resolution where accuracy is not compromised.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution processing is applied to all sensor data, then accuracy is improved, but processing time and memory requirements increase
Solution Approach 1:
The patent applies different processing resolutions to different spatial regions of the sensor data based on their importance. Critical regions (e.g., areas with detected objects, regions near the vehicle) are processed at high resolution, while less critical regions are processed at lower resolution. This resolves the contradiction by maintaining high accuracy where needed while reducing processing time in less critical areas.
Solution Approach 2:
The sensor data is divided into multiple regions or zones based on their relevance to the autonomous vehicle's operation. Each region is independently processed at an appropriate resolution level. This segmentation allows the system to allocate processing resources efficiently, improving overall processing speed while maintaining accuracy in critical regions.
2Measurement precision
If high-resolution processing is applied to all sensor data, then accuracy is improved, but memory requirements increase
Solution Approach 1:
The patent stores and processes sensor data at different resolution levels for different spatial regions. High-resolution data is retained only for critical regions where accurate object detection and classification are essential, while lower-resolution data is used for less critical regions. This significantly reduces memory requirements while maintaining processing accuracy where it matters most.
Solution Approach 2:
The memory is effectively segmented to store different portions of sensor data at different resolution levels. This allows the system to optimize the trade-off between memory usage and accuracy by allocating high-resolution storage capacity to critical regions and lower-resolution storage to non-critical regions.
3Productivity
If variable resolution processing is implemented, then resource efficiency is improved, but device complexity increases
Solution Approach 1:
The patent implements a dynamic resolution selection mechanism that automatically adjusts the processing resolution based on the content and importance of different regions in the sensor data. A machine learning model or heuristic algorithm determines the appropriate resolution for each region in real-time, allowing the system to adapt to varying operational conditions without manual intervention. This dynamic approach improves resource efficiency while keeping the complexity manageable through automated decision-making.
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.