AI I/O Expansion Adapter for Telematics Data Processing
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Solution Overview
Problem
Telematics systems face challenges in processing high-bandwidth raw I/O expansion data from I/O expanders, such as video and audio frames, due to limited processing power and memory in telematics devices, which requires specialized AI-based methods like machine learning for effective data interpretation.
Innovation Solution
An AI-based I/O expansion adapter processes raw I/O expansion data from I/O expanders, performing tasks like image recognition and voice recognition, and sends processed data to telematics devices, reducing data complexity and bandwidth requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If telematics devices process raw I/O expansion data directly, then data interpretation capability is improved, but processing power and memory requirements increase significantly
Solution Approach 1:
The patent introduces an I/O expansion adapter as an intermediary component between the I/O expander and the telematics device. The adapter pre-processes high-bandwidth raw data (video frames, audio frames, sensor data) using embedded machine learning models, converting it into standardized, interpretable formats before transmission to the telematics device. This mediator approach enables complex data interpretation without overloading the telematics device's processing resources.
Solution Approach 2:
The system architecture is segmented into distinct functional components: the I/O expander that captures raw data, the I/O expansion adapter that performs AI-based processing and data transformation, and the telematics device that receives processed data. This segmentation allows each component to specialize in specific tasks, with the adapter handling the computationally intensive AI processing while the telematics device focuses on data reception and application-level processing.
2Loss of information
If telematics devices receive high-bandwidth raw data from I/O expanders, then data completeness is improved, but bandwidth requirements and data complexity increase
Solution Approach 1:
The I/O expansion adapter performs preliminary processing actions on raw data before it is transmitted to the telematics device. Machine learning models embedded in the adapter analyze video frames, audio frames, and sensor data in advance, extracting relevant features and converting them into standardized formats. This preliminary action ensures data completeness is maintained while significantly reducing the bandwidth required for transmission, as only processed, essential information is sent rather than raw high-bandwidth data.
3Measurement precision
If machine learning models are deployed on telematics devices, then data processing accuracy is improved, but device complexity and power consumption increase
Solution Approach 1:
The I/O expansion adapter serves as an intermediary that hosts and executes machine learning models, separating the AI processing functionality from the telematics device. The adapter receives raw data from the I/O expander, applies machine learning models to achieve accurate data processing and interpretation, then transmits the processed results to the telematics device. This approach maintains high measurement precision through AI processing while preventing device complexity and power consumption from increasing on the telematics device itself.
Data Source
AI summary
Systems and methods by a telematics server are provided. The method includes receiving, over a network, training data including model input data and a known output label corresponding to the model input data from a first device, training a centralized machine-learning model using the training data, determining, by the centralized machine-learning model, an output label prediction certainty based on the model input data, determining an increase in the output label prediction certainty over a prior predicted output label certainty of the centralized machine-learning model, and sending, over the network, a machine-learning model update to a second device in response to determining that the increase in the output label prediction certainty is greater than an output label prediction increase threshold.