Vehicle Asset Fingerprinting for Proprietary Message Decoding
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Solution Overview
Problem
Existing asset tracking systems struggle to identify and decode data messages from vehicles that deviate from standardized communication protocols, particularly in electric vehicles, due to the increasing use of proprietary messaging protocols, leading to logistical challenges and inefficiencies in data collection.
Innovation Solution
A system and method for determining asset type fingerprints and generating signal definitions to decode data messages, involving an asset tracking system and an asset data analysis system that collaboratively identify asset types and decode data using machine learning and on-demand signal definitions, even for vehicles with non-standardized protocols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If asset tracking systems use standardized communication protocols for data collection, then ease of operation and device compatibility are improved, but adaptability to vehicles with proprietary messaging protocols deteriorates
Solution Approach 1:
The system performs self-identification of asset types through automated fingerprinting algorithms that analyze received data messages without requiring manual configuration. The asset tracking system automatically detects protocol characteristics, identifies the asset type, and configures appropriate signal definitions, enabling the system to serve itself across diverse protocols without increasing operational complexity for users.
Solution Approach 2:
The system dynamically changes operational parameters by maintaining a library of signal definitions with varying data formats, message structures, and decoding parameters for different asset types. Based on the detected asset type fingerprint, the system selects and applies the appropriate parameter set, allowing seamless adaptation to proprietary protocols while maintaining standardized interaction interfaces.
2Adaptability or versatility
If asset tracking systems implement comprehensive protocol support for all vehicle types, then adaptability improves, but device complexity increases
Solution Approach 1:
The system segments protocol support into modular signal definitions organized by asset type categories (e.g., electric vehicles, conventional vehicles, heavy duty). Each signal definition represents an independent, configurable unit with specific data formats and decoding logic. This segmentation allows the system to load only relevant protocol handlers for detected asset types, reducing memory footprint and processing complexity while maintaining comprehensive protocol coverage.
Solution Approach 2:
The system introduces an intermediary asset type fingerprinting layer that sits between the standardized communication interface and the diverse protocol implementations. This intermediary automatically translates various proprietary protocols into a unified internal representation, allowing the upper-level application logic to remain simple and standardized while handling protocol diversity at the intermediary layer through automated detection and signal definition selection.
3Measurement precision
If asset tracking systems manually configure signal definitions for each asset type, then measurement precision improves, but loss of time increases
Solution Approach 1:
The system performs preliminary configuration by pre-defining signal definitions for multiple asset types during system initialization or first-time setup. The asset type fingerprinting algorithm pre-analyzes data messages from various asset types to automatically generate and store appropriate signal definitions in the library. This preliminary action eliminates the need for manual configuration during field deployment, maintaining high decoding accuracy while reducing setup time to automated detection and selection.
Solution Approach 2:
The system implements feedback mechanisms where decoded data messages are continuously analyzed to verify signal definition accuracy. If decoding failures or anomalies are detected, the system automatically adjusts signal definition parameters or requests additional data messages for re-analysis. This feedback loop ensures high measurement precision through iterative optimization while minimizing manual intervention time, as the system self-corrects configuration issues autonomously.
Data Source
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AI summary
Methods for asset type fingerprinting are provided. An example method involves an asset tracking system failing to identify an asset type fingerprint and requesting an asset type fingerprint from an asset data analysis system. The asset data analysis system generates a proposed asset type fingerprint, links the asset type fingerprint to a set of signal definitions that indicate how to decode data messages from the asset. The asset data analysis system transmits the proposed asset type fingerprint to the asset data analysis system, which then decodes at least some data messages from the asset thus obtaining asset information. The method may be used to identify vehicle types and decode proprietary or non-standard data messages via a vehicle's diagnostic port.