Telematics Driving Attribute Aggregation for Real-Time Driver Scoring
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Solution Overview
Problem
Conventional systems require a long data collection period to accurately determine driver scores from telematics data, making it difficult for service providers to evaluate risk at the time of sale.
Innovation Solution
A system and method that captures and communicates driving attributes from telematics data, allowing for the aggregation and denormalization of data to generate aggregated attributes, which can be used to calculate a driver score in real-time for service providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If telematics data is collected over a long period of time to accurately determine driver scores, then the accuracy of driver score determination is improved, but the time required for service providers to evaluate risk is increased
Solution Approach 1:
The system performs preliminary aggregation and denormalization of telematics data attributes before they are needed for driver score calculation. By pre-processing the telematics data into aggregated attributes organized by time periods and denormalized for direct model input, the system eliminates the need for long data collection periods while maintaining accuracy. The driving assessment platform continuously processes and stores aggregated attributes, so when a provider needs a driver score, the data is already prepared and ready for immediate calculation.
2Productivity
If telematics data is aggregated and denormalized to enable real-time driver score calculation, then the speed of risk evaluation is improved, but the system complexity is increased
Solution Approach 1:
The system segments the driver score determination process into distinct functional modules: a driving assessment platform that aggregates and denormalizes telematics data attributes, and a provider model that calculates driver scores. The telematics data is segmented into different attributes (acceleration, braking, positioning) that are independently aggregated and then combined. This modular segmentation manages complexity by assigning specific processing tasks to dedicated components rather than requiring a monolithic complex system.
Solution Approach 2:
The driving assessment platform acts as an intermediary between the raw telematics data and the provider model. It receives telematics data, aggregates attributes over time periods, denormalizes them into a format suitable for modeling, and provides prepared data to the provider model. This intermediary layer simplifies the overall system architecture by handling the complex data transformation tasks centrally, allowing the provider model to focus solely on score calculation without needing to implement its own complex data processing logic.
Data Source
AI summary
Implementations claimed and described herein provide systems and methods for determining driving attributes using telematics data. The systems and methods use telematics data generated via a telematics device disposed in a vehicle. One or more driving attributes associated with a vehicle operator and/or the vehicle based on the telematics data are determined by the system. The one or more driving attributes associated with the vehicle operator and/or the vehicle are denormalized and aggregated. Furthermore, the aggregated driving attributes are communicated with a provider computing device in response to a request for the aggregated driving attributes.