Vehicle Sensor Data Bucketing for Processing Efficiency
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Solution Overview
Problem
Autonomous vehicle systems face inefficiencies in processing and analyzing sensor data, leading to underutilization of processing power during off-peak periods, resulting in increased costs for unnecessary processing capabilities.
Innovation Solution
A system comprising a frontend component for collecting and storing sensor data and a backend component for processing and correlating data using a hierarchical data structure, where data buckets are created based on midpoints between timestamps to efficiently associate and store sensor data from multiple sensors, reducing the need for excessive processing power.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If full processing capability is provisioned for autonomous vehicle sensor data, then data processing reliability is improved, but cost and resource utilization worsen due to underutilization during off-peak periods
Solution Approach 1:
The patent implements dynamic processing capability allocation where the system automatically scales processing resources based on real-time data arrival rates and processing queue depths. During high-activity periods, full processing capability is activated to maintain reliability, while during off-peak periods, processing capacity is reduced to eliminate underutilization and associated costs.
Solution Approach 2:
The system performs preliminary actions by pre-configuring processing pipelines and data structures before data arrives, and by maintaining buffered storage capacity ready for incoming data. This allows the system to quickly activate full processing capability when needed without requiring permanently allocated resources at full capacity.
2Productivity
If hierarchical data structure with data buckets is implemented, then data processing efficiency is improved, but system complexity increases
Solution Approach 1:
The patent segments sensor data into hierarchical data buckets organized by time intervals, sensor type, and processing priority. This segmentation enables parallel processing of different data subsets, improving overall processing efficiency. The segmentation is implemented through automated routing logic that distributes incoming data to appropriate buckets based on simple classification rules.
Solution Approach 2:
The system adds hierarchical dimensions to the data structure (time-based buckets, processing stages, priority levels) without fundamentally complicating the underlying storage format. Each dimension provides an additional organizing principle that enables efficient querying and processing while maintaining compatibility with standard data storage mechanisms.
Data Source
AI summary
A device includes a communications interface that receives sensor data sensed by a plurality of sensors sensing an environment of a vehicle. The device includes a processor and a memory including instructions that when executed by the processor, cause the processor to access a first timestamp, a second timestamp, and a third timestamp of a sensor signal from a selected sensor of a plurality of sensors. The instructions cause the processor to calculate a first midpoint between the first timestamp and the second timestamp, calculate a second midpoint between the second timestamp and the third timestamp, create a first data bucket for at least some of the sensor data based on the first and second midpoints, generate a data structure using the first data bucket, and store the data structure in a storage system.