Paired-Camera Area-of-Focus Processing for Autonomous Vehicles
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Solution Overview
Problem
Existing autonomous vehicle camera systems face challenges in efficiently managing the throughput of high-resolution camera data, leading to potential processing bottlenecks and reliability issues.
Innovation Solution
Implementing redundant processing units and communication fabrics within the autonomous vehicle's computing system to handle high-resolution camera data, including redundant power and data pathways, and utilizing area-of-focus techniques to generate downsampled and cropped frames for efficient data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution camera data is processed at full throughput, then measurement precision and detection accuracy are improved, but processing speed and system reliability deteriorate due to processing bottlenecks
Solution Approach 1:
The patent divides the high-resolution camera frame into multiple regions of interest (ROIs) and processes only those segments at full resolution. The processor identifies specific areas requiring detailed analysis and generates lower-resolution versions of non-critical areas, thereby segmenting the processing workload to maintain accuracy where needed while improving overall throughput.
Solution Approach 2:
The patent applies different processing qualities to different parts of the camera frame. Critical regions maintain full high-resolution quality for accurate detection, while non-critical regions are downsampled to lower resolution. This local quality differentiation ensures measurement precision is preserved in important areas while reducing total data volume to eliminate processing bottlenecks.
2Reliability
If redundant processing units and communication fabrics are implemented, then system reliability is improved, but device complexity increases
Solution Approach 1:
The patent combines multiple camera inputs into a unified processing pipeline with shared processing units. Rather than completely redundant independent systems, the architecture merges data streams from multiple cameras and processes them through shared resources, achieving reliability through diversity of input paths while reducing complexity by consolidating processing logic.
Solution Approach 2:
The patent implements dynamic processing where the system adapts its processing configuration based on operational needs. The processor dynamically adjusts which regions receive full processing attention and which are downsampled, and can dynamically allocate processing resources based on detected events, providing reliability through adaptive response without requiring static redundant infrastructure.
3Productivity
If area-of-focus techniques are used to generate downsampled and cropped frames, then productivity is improved through reduced computational load, but measurement precision may deteriorate in non-focused areas
Solution Approach 1:
The patent performs preliminary identification of regions of interest before final processing. The system first scans the entire frame to detect potential areas requiring detailed analysis, then pre-determines which regions should maintain full resolution and which can be downsampled. This preliminary action ensures that even in non-focused areas, the downsampling decisions are made intelligently based on actual content analysis rather than arbitrary region selection.
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.