Vehicle Trip Risk Scoring Using Driving Context Correlation
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Solution Overview
Problem
Existing driver activity information, when combined with demographic data, is insufficient for accurate loss risk assessment in insurance pricing, as it does not consider the context of the driving environment, leading to potential inaccuracies in rewarding good driving behavior.
Innovation Solution
Collect and correlate driver activity data with contextual information such as weather, traffic, road conditions, and local regulations during a vehicle trip, generating event records that include both activity and context indicators, and process these records to determine a more accurate loss risk assessment score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If driver activity information is used alone for loss risk assessment, then the assessment process is simple, but the accuracy of loss risk assessment is insufficient
Solution Approach 1:
The patent combines driver activity information with contextual information (weather, traffic, road conditions, laws) into a unified assessment system. Multiple data sources are merged to create a comprehensive risk profile, where the computing device processes both types of data together to generate the loss risk assessment score.
Solution Approach 2:
The system collects and processes multiple types of information (driver behavior, environmental conditions, traffic patterns, legal context) through a single multi-functional computing device that can handle diverse data formats and sources, making the assessment system universally applicable to various driving scenarios.
2Measurement precision
If contextual information is collected and correlated with driver activity data, then loss risk assessment accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent segments the data processing into distinct modules: driver activity data collection, contextual information acquisition, data correlation, and score generation. Each segment handles specific tasks, making the overall complex process more manageable and maintainable while improving assessment accuracy.
Solution Approach 2:
The computing device acts as an intermediary that receives raw data from multiple sources (sensors, weather services, traffic databases), processes and correlates this information, and generates the final risk assessment score. This intermediary layer simplifies the interface between complex data sources and the assessment output.
3Measurement precision
If multiple data sources are integrated for comprehensive risk assessment, then insurance premium accuracy improves, but information processing time increases
Solution Approach 1:
The system performs preliminary data collection and preprocessing during the driving trip itself, accumulating driver activity information and contextual data as they occur. This preliminary action prepares the data for faster final processing and score generation, reducing the time needed when the actual assessment is performed.
Data Source
AI summary
A technique is provided for determining a loss risk assessment score for a vehicle trip. The technique includes, at a vehicle, a computing device receiving first information indicative of operation of the vehicle. The technique also includes, at the vehicle, the computing device receiving second information indicative of an environment at a particular location and time. The computing device correlates the first information and the second information to generate a data set. The technique also includes determining a score for the vehicle trip based at least in part upon the generated data set.