Telematics Driver Profiling for Distracted Driving Incentives
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Solution Overview
Problem
Current vehicle safety systems are inadequate in preventing accidents caused by distracted driving, as they primarily focus on the vehicle operator's behavior and do not effectively address external risks or vehicle malfunctions.
Innovation Solution
A computer-based system that processes telematics data from vehicles and mobile devices to determine if a user is driving, tracks the mobile device's 'do not disturb' mode, and generates user offerings such as incentives or discounts based on safe driving behavior, using a combination of sensors, AI, and machine learning to promote safer driving practices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional safety systems monitor only the vehicle operator's driving behavior, then the system complexity remains low, but the system effectiveness in preventing distracted driving accidents is insufficient
Solution Approach 1:
The patent combines multiple data sources (telematics data from vehicle sensors, mobile device location data, and mobile device mode data) into a unified monitoring system. This merging of previously separate systems enables comprehensive distracted driving detection while managing complexity through integrated architecture.
Solution Approach 2:
The monitoring system performs multiple functions: determining driver status, tracking mobile device location, monitoring mobile device modes, and generating incentives. This multi-functionality increases effectiveness while the standardized processing framework manages the associated complexity.
2Productivity
If the system processes comprehensive telematics data and mobile device data to generate personalized user offerings, then the user engagement and safety incentive effectiveness improve, but the data processing time and computational resources increase
Solution Approach 1:
The system pre-processes telematics data and mobile device data as they are collected, preparing them in advance for incentive generation. This preliminary processing reduces the computational burden when user offerings need to be generated, thereby reducing processing time while maintaining comprehensive analysis.
Solution Approach 2:
The system automatically generates and delivers personalized incentives to users based on their driving behavior patterns, eliminating manual intervention. This automation improves productivity in delivering engagement incentives while reducing the time loss associated with manual processing.
3Reliability
If the system provides real-time monitoring and feedback to users, then the immediate behavioral correction capability improves, but the energy consumption and system resource usage increase
Solution Approach 1:
The system provides feedback and incentives at periodic intervals based on driving trips and behavior patterns, rather than continuous real-time monitoring. This periodic approach maintains the ability to correct behavior effectively while significantly reducing energy consumption compared to constant monitoring.
Data Source
AI summary
A user analytics computing device for processing mobile device telematics data and generating user offerings responsive to the mobile device telematics data is provided. The user analytics computing device comprises at least one processor programmed to generate an operator model for a user based upon historical telematics data, an output of the operator model to determine whether the user is operating a vehicle. The user analytics computing device is further programmed to input telematics data into the operator model, and in response to determining the user is operating the vehicle, generate a driver profile based upon the telematics data and the device mode data, generate, based upon the driver profile, a user offering, and transmit, to the mobile device of the user, the user offering.