AV Data Manifest Reconciliation for Secure Offloading
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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 affects 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 data 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 storage and processing locations, reducing the burden on individual vehicle computing resources while maintaining data availability.
2Speed
If data is transferred in real-time to centralized servers, then data analysis speed is improved, but network bandwidth requirements increase
Solution Approach 1:
The patent implements preliminary data processing and filtering at the edge computing level before transmission. Data is pre-processed, aggregated, and filtered to extract only relevant information for centralized analysis. This preliminary action reduces the volume of data requiring network transmission while ensuring that critical information is available for timely analysis at centralized platforms.
Solution Approach 2:
Different data processing strategies are applied locally based on data type and urgency. Critical safety-related data is transmitted immediately with high priority, while non-critical data is buffered and transmitted during off-peak periods. This local quality differentiation optimizes network bandwidth utilization while maintaining analysis speed for important data.
3Adaptability or versatility
If data is cached locally for offline analysis, then network dependency is reduced, but data security risks increase
Solution Approach 1:
The patent implements a nested security architecture where encrypted data containers are stored locally in vehicles, with each container having multiple security layers. Data is encrypted using hierarchical key management systems where master keys are securely stored in hardware security modules. This nested structure allows offline access to data while maintaining robust security controls, as unauthorized access requires breaking multiple encryption layers.
4Quantity of substance
If data is compressed to reduce storage requirements, then storage capacity is optimized, but data integrity may be compromised
Solution Approach 1:
The patent creates multiple redundant copies of critical data across different storage locations (vehicle local storage, regional data centers, and cloud platforms). These copies are generated using error-correcting codes and checksum verification mechanisms. This copying strategy allows the system to tolerate some data degradation from compression while maintaining integrity through verification and recovery from redundant copies.
Data Source
AI summary
A data management platform for Autonomous Vehicles (AVs) is provided. The data management platform can receive, from an AV at a first time, a first copy of a manifest including a creation history of a transformed object generated by the AV and a data integrity value corresponding to the transformed object. The data management platform can receive, from a second computing system at a second time, a second copy of the manifest. The data management platform can reconcile the first copy and the second copy. The data management platform can receive, from the second computing system at a third time, a request to upload the transformed object. The data management platform can validate the transformed object stored in storage of the first computing system based on the data integrity value included in the manifest.