Smart Sensor Scheduler for Autonomous Vehicle Image Processing
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Solution Overview
Problem
Autonomous vehicles equipped with multiple sensors generate vast amounts of data that exceed processing capabilities, leading to bottlenecks and inefficiencies in data transfer and processing, particularly when high-resolution imaging is required.
Innovation Solution
The implementation of a scheduler that dynamically distributes image data processing among available image processing resources based on priority levels assigned to regions of interest (ROIs) within the image sensor, ensuring optimal utilization and throughput by selecting the most suitable resource for each ROI's image data based on detected features and environmental considerations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution imaging is required, then image quality is improved, but data processing capability becomes insufficient
Solution Approach 1:
The image sensor is divided into multiple regions of interest (ROIs), each capable of independent readout. This segmentation allows the system to process only the most critical portions of the image data, reducing the overall data volume while maintaining high-resolution imaging quality where needed.
Solution Approach 2:
Different ROIs are assigned different readout frequencies and processing priorities based on their local importance. High-priority ROIs containing features of interest are processed at higher resolutions and frequencies, while low-priority ROIs are processed at lower rates, optimizing the balance between image quality and processing capability.
2Reliability
If all ROI image data is processed, then processing completeness is improved, but resource utilization efficiency deteriorates
Solution Approach 1:
The scheduler dynamically adjusts the readout frequency and processing allocation for each ROI based on real-time feature detection and environmental conditions. This dynamic adaptation ensures that processing resources are allocated efficiently to the most critical ROIs while maintaining complete processing of all ROI data when necessary.
Solution Approach 2:
The system changes processing parameters such as readout frequency, resolution, and processing priority based on the detected features and environmental context. This allows the system to maintain processing completeness for critical data while improving resource utilization efficiency by reducing processing intensity for less critical ROIs.
3Productivity
If dynamic scheduling is implemented, then resource allocation efficiency is improved, but system complexity increases
Solution Approach 1:
The scheduler operates autonomously within the image sensor, using on-chip logic to detect features and make scheduling decisions without external intervention. This self-service capability improves resource allocation efficiency while minimizing the complexity increase by keeping the scheduling logic integrated and simplified within the sensor architecture.
Data Source
AI summary
A system includes an image sensor having a plurality of pixels that form a plurality of regions of interest (ROIs), image processing resources, and a scheduler configured to perform operations including determining a priority level for a particular ROI of the plurality of ROIs based on a feature detected by one or more image processing resources of the image processing resources within initial image data associated with the particular ROI. The operations also include selecting, based on the feature detected within the initial image data, a particular image processing resource of the image processing resources by which subsequent image data generated by the particular ROI is to be processed. The operations further include inserting, based on the priority level, the subsequent image data into a processing queue of the particular image processing resource to schedule the subsequent image data for processing by the particular image processing resource.