Vehicle Sensor Data Characterization for Telematics
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Solution Overview
Problem
Existing on-board electronic control systems in vehicles face challenges in efficiently communicating high-frequency real-time sensor data over limited data rate networks, such as wireless networks, which requires a method to effectively downsample and provide supplemental information for accurate data reconstruction.
Innovation Solution
An on-board computing system classifies time-series sensor data, processes it to create a downsampled representation, identifies characterization parameters based on measurement type and sampling rate, and transmits these parameters along with the downsampled data over a wireless network to a server system, which uses the information to reconstruct the original data and generate qualitative linguistic terms for user reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-frequency real-time sensor data is transmitted over wireless networks, then data completeness and measurement precision are improved, but network data rate capacity is exceeded and transmission efficiency deteriorates
Solution Approach 1:
The patent extracts only the most essential information from high-frequency sensor data by identifying and transmitting characterization parameters (such as min, max, mean, std dev) that capture the fundamental characteristics of the data. This extraction approach maintains measurement precision for diagnostic purposes while dramatically reducing the data volume transmitted over the network, thereby resolving the contradiction between data completeness and transmission efficiency.
Solution Approach 2:
The patent segments the sensor data transmission into two distinct components: (1) downsampled representation at reduced sampling rates, and (2) characterization parameters that supplement the downsampled data. This segmentation allows the system to transmit minimal data while preserving the ability to reconstruct and analyze the original high-frequency data characteristics, thus improving transmission efficiency without sacrificing measurement precision.
2Productivity
If downsampled representation is used to reduce data transmission, then network bandwidth consumption is reduced, but data reconstruction accuracy may deteriorate without supplemental information
Solution Approach 1:
The patent introduces characterization parameters as intermediary information that bridges the gap between downsampled representation and original data reconstruction. These parameters (including statistical measures like minimum, maximum, mean, and standard deviation) act as mediators that provide supplemental information about the downsampled data, enabling accurate reconstruction and analysis without transmitting the complete high-frequency data stream, thus maintaining measurement precision while improving network bandwidth efficiency.
3Measurement precision
If characterization parameters are added to downsampled data, then data reconstruction quality is improved, but message size and processing complexity increase
Solution Approach 1:
The patent changes the parameters of data representation by transforming the original high-frequency sensor data into a different parameter space that includes characterization parameters (min, max, mean, std dev, etc.). This parameter transformation allows the system to represent complex time-series data using a small set of meaningful parameters that capture essential characteristics, thereby improving data reconstruction quality while keeping processing complexity manageable through standardized calculations.
4Reliability
If high-frequency sensor data is processed and transmitted, then diagnostic information quality is improved, but energy consumption and computational load increase
Solution Approach 1:
The patent extracts only the essential diagnostic information from high-frequency sensor data by calculating and transmitting characterization parameters that capture the fundamental behavior of the sensor signals. This extraction approach maintains high diagnostic information quality while significantly reducing the computational load and energy consumption associated with processing and transmitting complete high-frequency data streams, as the system only needs to compute a small set of statistical parameters rather than handle every data point.
Data Source
AI summary
A computing system located on-board a vehicle processes the input sensor data to obtain a downsampled representation of one or more time-series measurements. The computing system further identifies one or more characterization parameters to be reported as supplemental information along with the downsampled representation. The computing system processes the input sensor data to obtain a characterization value for each characterization parameter identified based on measurement conditions and communicates the characterizations based on a timing profile. The computing system formats the one or more characterization values to be reported along with the downsampled representation into one or more report messages. The computing system transmits the one or more report messages over a wireless wide area network directed to a server system. The one or more characterization values may be used by the server system as supplemental information in reconstructing the input sensor data from the downsampled representation.