Paired Camera Downsampling for Autonomous Vehicle Data Throughput
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Solution Overview
Problem
Existing autonomous vehicle camera systems face challenges in efficiently managing the throughput of high-resolution sensor data, leading to potential processing bottlenecks and reliability issues.
Innovation Solution
Implementing redundant computing architectures with switched fabrics and power pathways, along with selective frame processing techniques such as downsampling and cropping, to optimize the handling of high-resolution camera data in autonomous vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution camera sensors are used to capture detailed environmental data, then measurement precision is improved, but device complexity and processing load increase
Solution Approach 1:
The patent segments the high-resolution image data by dividing it into multiple lower-resolution sub-images or regions of interest. This allows the system to maintain measurement precision for critical areas while reducing overall processing complexity. The segmentation enables selective processing where full resolution is applied only to relevant portions of the scene.
Solution Approach 2:
The patent extracts only the essential features and regions from high-resolution camera data that are necessary for autonomous navigation decisions. By taking out and processing only the critical environmental information rather than the entire high-resolution dataset, the system maintains measurement precision while reducing computational burden and device complexity.
2Productivity
If high-resolution camera data is processed in real-time, then productivity is improved, but use of energy increases
Solution Approach 1:
The patent applies partial action by processing only a subset of the camera data at full resolution - specifically, only the regions or features that are critical for immediate navigation decisions. This selective processing maintains real-time productivity for essential functions while significantly reducing energy consumption compared to processing the entire high-resolution dataset.
Solution Approach 2:
The patent dynamically changes processing parameters such as resolution, frame rate, and processing depth based on the current operational context. When full real-time processing is not critical, the system reduces resolution or processing intensity, thereby maintaining productivity when needed while reducing energy consumption during less critical periods.
3Reliability
If redundant computing architectures are implemented, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent implements redundancy selectively rather than uniformly across the entire system. Critical components that require high reliability (such as primary navigation processing) have redundant pathways, while less critical functions use simpler single-path architectures. This local quality approach improves reliability where needed while minimizing overall device complexity.
4Productivity
If data throughput is reduced through downsampling and cropping, then processing efficiency is improved, but loss of information occurs
Solution Approach 1:
The patent applies different processing qualities to different regions of the image data. Critical regions that require detailed information for safe navigation are processed and transmitted at high or full resolution, while non-critical background regions are downsampled or cropped. This ensures processing efficiency is improved overall while minimizing information loss in the areas that matter most for autonomous operation.
Data Source
AI summary
Reducing throughput of paired cameras, including: selecting, by a first camera of an autonomous vehicle, a first area of focus for a first frame generated by the first camera; selecting, by a second camera of the autonomous vehicle, a second area of focus for a second frame generated by the second camera by matching the first area of focus for the first frame; generating, by the first camera from the first frame, a first downsampled frame and a first cropped frame, wherein the first cropped frame is based on the first area of focus; and generating, by the second camera from the second frame, a second downsampled frame and a second cropped frame, wherein the second cropped frame is based on the second area of focus.