Driving Behavior Risk Scoring From Multi-Sensor Trip Data
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Solution Overview
Problem
Current insurance systems fail to quantify and differentiate individual insurance risk based on real-time driving behavior and environment, lacking the ability to generate data representative of actual vehicle operation and driver interactions.
Innovation Solution
A computer-implemented method and system that receives personal and vehicle sensor data to generate individual insurance risk data, utilizing modules for data collection, transmission, and analysis, enabling the creation of new insurance models and providing feedback to drivers on their behavior and risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional insurance risk assessment methods are used, then insurance pricing is based on aggregate statistical data, but individual driver behavior and real-time driving environment cannot be differentiated
Solution Approach 1:
The patent segments insurance risk assessment into multiple dimensions by collecting data from separate sources: personal electronic device sensors (accelerometer, gyroscope, magnetometer) capture driver behavior, while vehicle sensors capture driving environment. This segmentation enables precise differentiation of individual risk factors without requiring a single complex unified system.
Solution Approach 2:
The patent employs multi-functional sensor modules that serve multiple purposes: personal electronic device sensors not only track driver behavior but also detect device orientation and usage patterns; vehicle sensors monitor both driving conditions and vehicle operation. This multi-functionality reduces overall system complexity while achieving comprehensive risk assessment.
2Loss of information
If real-time sensor data from personal electronic devices and vehicles is collected, then individual driving behavior can be quantified, but data processing and analysis complexity increases
Solution Approach 1:
The patent extracts specific relevant features from raw sensor data rather than processing all data points. The system identifies and extracts key behavioral indicators (sudden movements, device usage patterns) and environmental factors (road conditions, traffic) from the continuous sensor streams, reducing processing complexity while maintaining information completeness.
Solution Approach 2:
The system performs preliminary data processing and filtering at the source devices (personal electronic device and vehicle sensors) before transmission. Sensors continuously monitor and pre-process data locally, preparing only relevant information for analysis, which reduces the burden on centralized processing systems.
3Adaptability or versatility
If comprehensive sensor data is analyzed to generate individual insurance risk data, then personalized insurance pricing can be offered, but computational resources and processing time increase
Solution Approach 1:
The patent implements periodic risk assessment rather than continuous real-time analysis. The system collects sensor data continuously but performs comprehensive risk evaluation at scheduled intervals (e.g., daily, weekly, or monthly), allowing personalized pricing updates without requiring constant computational resources. This periodic action reduces processing time and resource consumption while maintaining pricing adaptability.
Data Source
AI summary
The present disclosure generally relates to systems and methods for generating data representative of an individual's insurance risk based on actual driving behaviors and driving environment. The systems and methods may include collecting data from personal electronic device sensors, vehicle sensors, driver inputs, environment sensors and interactions of the vehicle, the driver and the environment. The systems and methods may further include analyzing the data to generate data representative of an individual insurance risk. The systems and methods may further include presenting the data representative of the individual insurance risk to an individual.