Vehicle Telematics Spectral Analysis for Movement Separation
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Solution Overview
Problem
Existing vehicle monitoring systems require expert installation and periodic data download, and struggle to accurately differentiate between primary vehicle movement and secondary device movement, leading to inaccurate analysis of driving patterns and insurance risk assessment.
Innovation Solution
A computer-implemented method using spectral domain analysis to identify primary movement windows by processing telematics data from multi-axis accelerometers, separating and orienting the device data with the vehicle, and determining driving patterns through pitch, roll, and yaw angles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spectral domain analysis is used to differentiate primary and secondary movements, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex mechanical differentiation methods with spectral domain analysis using Fourier transforms. The accelerometer signal is transformed from time domain to frequency domain, where primary vehicle movements and secondary device movements can be distinguished by their characteristic frequency signatures. This substitution of mechanical analysis with spectral analysis achieves precise movement differentiation while using standard signal processing techniques.
2Loss of time
If telematics device remains in vehicle without removal, then loss of time is reduced, but measurement precision deteriorates due to inability to accurately separate primary and secondary movements
Solution Approach 1:
The patent implements feedback through continuous monitoring of the accelerometer signal's spectral characteristics. By constantly analyzing the frequency domain representation of the signal, the system can distinguish between primary movements (vehicle acceleration, braking, turning) and secondary movements (device bouncing, shifting) in real-time. This feedback mechanism allows the device to remain installed in the vehicle indefinitely while maintaining precise driving pattern analysis through ongoing spectral analysis.
Data Source
AI summary
A computer implemented method for determining a primary movement window from a vehicle trip is presented. An example method includes receiving a plurality of telematics data including multi-axis accelerometer data. One or more processors then select one or more data points from the plurality of telematics data and determine whether a total spectral power of the one or more data points meets a threshold value based upon a kernel smoothed estimate using the multi-axis accelerometer data. The example method further includes the one or more processors identifying a primary movement window including the one or more data points based upon determining that the total spectral power of the one or more data points is less than the threshold value.