AV Data Offloading Manifests for Integrity and Bandwidth Limits
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Solution Overview
Problem
Autonomous vehicles face challenges in managing data due to limited computing resources, unpredictable network bandwidth, and varying data transfer times, which affect the scalability, security, and efficiency of data collection and processing.
Innovation Solution
A highly reliable, scalable, and flexible AV data management platform that partitions raw data into ingestion objects, applies transformations, and stores manifests with integrity values, allowing for real-time or near-real-time offloading to data centers and servicing stations, with reconciliation mechanisms to ensure data integrity and adaptability to different resource conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If autonomous vehicles collect and store large amounts of data locally, then data availability for analysis is improved, but computing resources and storage capacity are exceeded
Solution Approach 1:
The patent segments data management into multiple components: local edge computing devices in vehicles handle real-time processing, regional data centers aggregate and pre-process data, and centralized cloud platforms perform comprehensive analysis. This segmentation allows data to be distributed across multiple levels rather than requiring all resources to be concentrated in one location, resolving the contradiction between data volume and computing resource availability.
2Quantity of substance
If data is offloaded to centralized data centers, then storage capacity is improved, but network bandwidth requirements increase
Solution Approach 1:
The patent implements preliminary action by performing data aggregation, filtering, and pre-processing at regional data centers before transferring data to centralized cloud platforms. Only essential and processed data are transmitted over the network, significantly reducing bandwidth requirements while maintaining adequate storage capacity at centralized locations.
3Speed
If real-time data processing is performed, then response time is improved, but computing power consumption increases
Solution Approach 1:
The patent applies local quality by implementing edge computing devices within vehicles that perform real-time data processing for immediate maneuvering decisions. Less time-critical data is processed asynchronously at data centers with lower power consumption. This distributes computing tasks based on their urgency and location requirements, optimizing both response time and power consumption.
4Measurement precision
If comprehensive data collection is implemented, then analysis accuracy is improved, but data management complexity increases
Solution Approach 1:
The patent introduces intermediary components including data standardization layers, protocol translators, and management platforms that mediate between diverse data sources and analysis systems. These intermediaries handle data normalization, quality validation, and coordination across the distributed architecture, enabling comprehensive data collection while managing complexity through standardized interfaces and automated processes.
Data Source
AI summary
A data management platform for Autonomous Vehicles (AVs) is provided. An AV can partition raw data into ingestion objects. The AV can transform the ingestion objects and generate associated manifests. The AV can offload first copies of the manifests in real-time to a data center. At a later time, the AV can offload second copies of the manifests to an AV servicing station. The station can upload the second copies to the data center. If the manifests match, then the station can notify the AV that it is safe to erase the manifests and transformed objects from local storage after offloading completes. If the manifests do not match and a Service Level Agreement (SLA) is violated, then the AV can be docked for further diagnosis. If no SLA is applicable, then the error can be annotated and the transformed objects can be discarded.