Vehicle Idling Detection via Accelerometer Orientation Normalization
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Solution Overview
Problem
Current vehicle monitoring systems require expert installation and periodic data download, making them cumbersome for determining idling times and other vehicle attributes, and struggle to accurately analyze accelerometer data due to orientation issues with the client computing device.
Innovation Solution
A computer-implemented method that receives telematics data from a client computing device, identifies primary movement data, measures and normalizes variance to determine idling time windows, and adjusts accelerometer data to align with the vehicle's orientation, allowing for accurate analysis of driving patterns and insurance risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional vehicle monitoring systems are installed with expert installation and periodic data download, then vehicle attributes can be monitored, but the system becomes cumbersome and difficult to operate
Solution Approach 1:
The monitoring system automatically transmits telematics data from the vehicle to the server without requiring user intervention for data download. The system performs self-monitoring and self-reporting, eliminating the need for expert installation and periodic manual data retrieval, thereby improving ease of operation while maintaining monitoring reliability
2Productivity
If accelerometer data is collected without orientation adjustment, then data collection is simple, but the analysis of driving patterns becomes inaccurate
Solution Approach 1:
The system performs preliminary orientation adjustment of the accelerometer data by calculating the device's orientation relative to the vehicle and rotating the acceleration vectors accordingly before analysis. This preliminary action ensures that subsequent driving pattern analysis is accurate while maintaining efficient data collection processes
3Speed
If variance normalization is not applied to accelerometer data, then processing is faster, but identification of idling times becomes inaccurate
Solution Approach 1:
The system applies variance normalization as a parameter transformation to the accelerometer data, scaling the values to a consistent range that improves the accuracy of idling time identification. This parameter change enables more precise detection of vehicle states while maintaining efficient processing through optimized normalization algorithms
Data Source
AI summary
A computer implemented method for determining one or more idling time windows from a vehicle trip is presented. A data server may receive, via a computer network, a plurality of telematics data originating from a client computing device and identify primary movement data from the plurality of telematics data. The data server may also measure a total variance from the plurality of telematics data at one or more time stamps and determine an average total variance for an entire trip from the plurality of telematics data. The data server may further normalize total variance at the one or more time stamps using the generated average and determine one or more idling time windows from the normalized total variance.