Overview

This project aims to address the need for real-time driving behavior assessment and risk profiling.

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Problem

This project aims to address the need for real-time driving behavior assessment and risk profiling. With the increasing importance of road safety and the need for effective insurance solutions, this project leverages mobile phone sensors to model driver behavior, assess risk, and provide rapid incident response capabilities.

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Solution

FlowFoundry's solution uses mobile phone sensors to measure acceleration, deceleration, cornering, and yaw while the phone owner is driving. The data is then used to build a model that assesses driving behavior, identifies drivers within 30 seconds of driving, and calculates the risk of an accident based on driving style and road conditions. The system also features crash event detection, enabling rapid "First Notification of Loss" (FNoL) to give insurers a competitive advantage in managing claims.

Key Aspects of the Project

1. Sensor Data Collection Mobile phone sensors collect real-time data on acceleration, deceleration, cornering, and yaw. This data forms the foundation for assessing driving behavior and determining risk.
2. Driving Behavior Modeling The collected sensor data is processed to model driving behavior. Using machine learning techniques, the system can identify the driver within 30 seconds of driving and score driving style to determine the risk level.
3. Risk Analysis Based on Road Conditions By combining driving data with real-time road condition information, the system calculates the risk of an accident. This risk assessment can be used by insurers to adjust policies and encourage safer driving practices.
4. Crash Event Detection and FNoL The system can detect crash events using sensor data, allowing for immediate "First Notification of Loss" (FNoL). This rapid response helps insurers manage claims more effectively, providing a competitive advantage in the claims business.

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