Incremental Data Frame Segmentation for Latency Reduction
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Solution Overview
Problem
Conventional data acquisition systems face limitations in throughput due to the sequential nature of data acquisition and processing, where processing only begins after a complete data frame is acquired, leading to high data latency and reduced efficiency, especially in applications like autonomous vehicles where rapid decision-making is critical.
Innovation Solution
The system segments data frames into incremental data of equal length, allowing for concurrent acquisition and processing of these increments, with the ability to selectively divide data frames into multiple segments and utilize machine learning to minimize processing where objects have not moved, thereby reducing data latency and improving throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data processing waits for complete data frame acquisition, then processing accuracy is maintained, but data latency increases and throughput decreases
Solution Approach 1:
The patent divides a complete data frame into multiple incremental data segments (e.g., first incremental data, second incremental data, etc.). Each segment can be processed independently as it becomes available, rather than waiting for the entire frame. This segmentation enables partial processing to begin earlier, reducing data latency while maintaining processing accuracy through systematic handling of all segments.
Solution Approach 2:
The system performs preliminary processing actions on incremental data segments as soon as they are acquired, before the complete data frame is available. This preliminary action includes acquiring, processing, and outputting processed incremental data in parallel, which reduces overall data latency while ensuring complete processing accuracy when all segments are combined.
2Device complexity
If sequential data processing is used, then system complexity is reduced, but throughput decreases
Solution Approach 1:
The patent segments the data processing workflow into independent parallel tasks: acquiring incremental data segments, processing each segment, and outputting results. This segmentation transforms a single sequential process into multiple parallel processes, increasing throughput from 1 Hz to potentially higher rates, while the modular structure actually simplifies system management compared to complex sequential dependencies.
Solution Approach 2:
The system maintains continuous useful action by processing incremental data segments as they become available without idle waiting periods. While the sensor acquires the next incremental data segment, the processor simultaneously processes previous segments, ensuring continuous productivity and maximizing throughput without increasing overall system complexity.
3Reliability
If complete data frames are processed before output, then data integrity is ensured, but processing speed decreases
Solution Approach 1:
The patent segments complete data frames into incremental portions that can be processed and output separately. Each incremental data segment maintains its integrity through dedicated processing, while the overall data integrity is ensured by systematically combining results from all segments. This approach enables faster processing speed by eliminating the requirement to wait for complete frame acquisition before beginning processing.
Solution Approach 2:
The system implements feedback mechanisms to track the processing status of incremental data segments and ensure complete data integrity. By monitoring which segments have been processed and combining their results systematically, the system verifies data integrity while maintaining accelerated processing speeds through parallel incremental operations.
Data Source
AI summary
Systems and methods are provided for segmenting a data frame to be acquired into a number of incremental data of equal data length. A first incremental data of the data frame can be acquired from one or more sensors. The first incremental data of the data frame can be processed while a next incremental data of the data frame is being acquired from the one or more sensors. The acquiring and processing of incremental data of the data frame can continue until a last incremental data of the data frame is acquired and processed. Processed incremental data can be outputted as a processed data frame.