Driver Profile Matching for Vehicle Type Recommendation
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Solution Overview
Problem
Existing vehicle telematics technologies fail to leverage various types of information that are highly probative of a driver's characteristics or qualities, and do not recognize numerous applications where such information could provide benefits, limiting their effectiveness in assessing driver performance and risk.
Innovation Solution
A computer-implemented method and system that receives telematics data from vehicles and mobile devices, analyzes it to identify driving behaviors, generates or modifies driver profiles, and uses these profiles to suggest appropriate vehicle types or components based on matching criteria, thereby providing insights into driver characteristics and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing telematics technologies collect and analyze basic driving data (acceleration, braking, cornering), then driver performance and risk assessment is provided, but the system fails to leverage additional probative information about driver characteristics
Solution Approach 1:
The driver profile system serves multiple functions: it assesses driver performance, evaluates risk, recommends vehicles, suggests maintenance schedules, and determines insurance ratings. By making the telematics system multi-functional, it leverages existing driving data across diverse applications, thereby reducing information loss while expanding versatility.
Solution Approach 2:
The system segments driver information into distinct profile categories (driving behavior patterns, risk factors, performance metrics) that can be independently analyzed and applied to different purposes. This segmentation allows the system to extract specific characteristics from telematics data for various applications without requiring a complete system redesign.
2Measurement precision
If comprehensive telematics data is collected and analyzed to generate detailed driver profiles, then personalized vehicle recommendations and assessments are improved, but system complexity increases
Solution Approach 1:
The driver profile acts as an intermediary data structure that consolidates complex telematics information into standardized, interpretable formats. Rather than directly processing raw acceleration, braking, and cornering data for each application, the system first transforms this data into a driver profile that serves as a mediator for subsequent analysis and decision-making processes.
Solution Approach 2:
The system performs preliminary data processing by generating driver profiles in advance, organizing and pre-analyzing telematics data before it is needed for specific applications. This preliminary action reduces the computational complexity required during actual vehicle recommendations, maintenance scheduling, or insurance rating determinations.
Data Source
AI summary
In a computer-implemented method, telematics data collected during one or more time periods by one or more electronic subsystems of a vehicle, and/or by a mobile electronic device of the driver or a passenger, is received. The telematics data includes operational data indicative of how a driver of the vehicle operated the vehicle. The received telematics data is analyzed to identify driving behaviors of the driver during the time period(s). Based at least upon the driving behaviors, a driver profile is generated or modified. Based at least upon the generated or modified driver profile, a suggested vehicle type is identified, at least by determining that the profile meets a set of one or more matching criteria associated with the suggested vehicle type. An indication of the suggested vehicle type is displayed to a user.