Vehicle Telematics Query Rewriting for Edge Fleet Analytics
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Solution Overview
Problem
Existing vehicle telematics systems face challenges in efficiently collecting, transmitting, and analyzing sensor data due to high network costs, privacy concerns, and data heterogeneity across diverse vehicle architectures, limiting widespread adoption and compliance with regulatory requirements.
Innovation Solution
A system that rewrites telematics queries into an edge-friendly form, allowing distributed computation across a fleet of vehicles with heterogeneous architectures, reducing data transmission and enhancing privacy by processing data locally and aggregating insights at the edge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If vehicle sensor data is collected and transmitted to centralized cloud systems for analysis, then comprehensive data analysis capability is improved, but network costs increase and privacy concerns worsen
Solution Approach 1:
The patent segments the centralized data processing function into distributed edge computing nodes deployed across multiple vehicles. Each edge node processes data locally within its vehicle or nearby vehicles, dividing the monolithic cloud processing task into smaller distributed units. This reduces the volume of data transmitted over the network while maintaining comprehensive analysis capability through coordinated processing across segments.
Solution Approach 2:
The patent introduces a new spatial dimension for data processing by deploying compute resources at the edge (within vehicles) rather than only at the centralized cloud. This creates a multi-layer processing architecture where simple queries are handled locally at the vehicle edge, intermediate queries are handled by nearby edge nodes, and complex queries are escalated to the cloud, adding a dimensional hierarchy to data processing that reduces network transmission requirements.
2Loss of information
If vehicle sensor data is transmitted to centralized systems, then data analysis is improved, but privacy concerns and regulatory compliance worsen
Solution Approach 1:
The patent extracts sensitive personal data from the data transmission pipeline by processing it locally at edge nodes within vehicles. Only aggregated, anonymized results are transmitted to centralized systems, effectively removing sensitive information from the transmission path while preserving the analytical value of the data for fleet-wide insights.
Solution Approach 2:
The patent applies local quality by allowing different processing approaches for different types of data based on their sensitivity and analytical requirements. Sensitive personal data is processed locally with strict privacy preservation, while non-sensitive aggregated data can be transmitted for broader analysis. This differentiated approach maintains privacy for sensitive information while enabling comprehensive analysis where appropriate.
3Adaptability or versatility
If telematics systems support diverse vehicle architectures, then versatility is improved, but data heterogeneity and processing complexity worsen
Solution Approach 1:
The patent implements a universal query interface and standardized data schema that works across diverse vehicle architectures. The edge computing nodes are designed with multi-functional capabilities to handle various sensor types and vehicle platforms through a common processing framework, allowing the system to adapt to different vehicle architectures without requiring architecture-specific processing logic.
Solution Approach 2:
The patent uses parameter changes by transforming heterogeneous data from different vehicle architectures into a standardized format through configurable data mapping parameters. The system allows dynamic adjustment of data transformation rules based on the specific vehicle architecture being queried, enabling seamless support for diverse platforms while maintaining consistent processing complexity through parameterized transformations rather than structural complexity.
4Loss of information
If all vehicle data is transmitted to the cloud for processing, then centralized control is improved, but network bandwidth requirements and costs increase
Solution Approach 1:
The patent applies preliminary action by performing data processing, filtering, and aggregation at the edge nodes before data transmission to the cloud. Queries are evaluated and executed locally at the vehicle edge, with only the final aggregated results transmitted to centralized systems. This preliminary processing at the edge eliminates the need to transmit raw sensor data volumes to the cloud, dramatically reducing network bandwidth requirements while maintaining centralized oversight.
Data Source
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AI summary
Methods and system for vehicle telematics are provided. The system, for example, includes an input/output device; and a processor programmed to receive a first query via the input/output; generate an abstract vehicle query that represents a query against local vehicle data configured to be executed on an individual vehicle; send the abstract vehicle query via the input/output device to each of a plurality of vehicles; receive query results from each of the plurality of vehicles that executed the abstract vehicle query based upon local data from each of the plurality of vehicles respectively; compute an aggregated final query result based upon the abstract vehicle query and the query results from each of the plurality of vehicles; and transmit the aggregated final query result to the input/output device.