Vehicular Telematics Hybrid Epoch Prediction for Stable Driver Scoring
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Solution Overview
Problem
The variability and uncertainty in pay-per-mile or pay-per-use vehicular insurance schemes due to high volume and high fidelity telematics data make it difficult for end-users to determine future invoices or driver metrics, especially with large numbers of records generated for short trips.
Innovation Solution
A cloud-based vehicular telematics system that generates hybrid epoch driver predictions by combining prior and current telematics data to provide a hybrid score, allowing for real-time or near real-time estimation of future driving metrics, thereby addressing the uncertainty and variability in insurance pricing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high volume and high fidelity telematics data are collected, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts only the essential features from high-volume telematics data by using epoch-based aggregation. Instead of processing every individual data point, the system extracts representative metrics (acceleration, braking, cornering events) and aggregates them into epoch scores, significantly reducing computational complexity while maintaining measurement precision for insurance pricing purposes.
Solution Approach 2:
The patent segments the continuous stream of telematics data into discrete epochs (time periods). By dividing the data into manageable epochs and processing them independently, the system reduces the computational burden of handling high-volume data while preserving the fidelity needed for accurate driver behavior assessment and pricing predictions.
2Measurement precision
If high volume telematics records are generated, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary aggregation of telematics data into epoch scores during or immediately after each epoch period. By pre-processing and summarizing data into compact epoch representations, the system eliminates the need for time-consuming processing of raw high-volume data when generating pricing predictions, thus reducing loss of time while maintaining pricing accuracy.
Solution Approach 2:
The system extracts only the critical driving behavior metrics from the high-volume telematics records and aggregates them into concise epoch scores. This extraction process retains the essential information needed for accurate pricing while dramatically reducing the time required for data processing and analysis.
3Reliability
If previous scores are stored for hybrid epoch calculations, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent extracts only the necessary historical information by storing compact epoch scores rather than complete historical telematics records. By storing aggregated epoch scores (which capture essential driver behavior patterns) instead of raw data, the system maintains pricing consistency and reliability while significantly reducing storage requirements and system complexity.
Data Source
AI summary
Method and system for generating a hybrid epoch score for a user. In some examples, a computer-implemented method includes: receiving, from at least one of a mobile device associated with the user and a server, prior telematics data indicative of the operation of a vehicle by the user during one or more prior trips in a prior epoch; receiving, from the mobile device, recent telematics data indicative of the operation of the vehicle by the user during one or more recent trips in a current epoch; generating a prior epoch score based at least in part upon the prior telematics data; generating a partial current epoch score based at least in part upon the recent telematics data; and generating a hybrid current epoch score based at least in part upon the prior epoch score and the partial current epoch score.