Mobile Device Driving Pattern Identification Using Sensor Data
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Solution Overview
Problem
Existing vehicle-based systems for collecting and analyzing driving data are limited in accuracy and coverage, as they may not account for multiple drivers using the same vehicle or vehicles without such systems, and mobile devices can provide more comprehensive movement data but require efficient methods to identify driving patterns and associate them with drivers.
Innovation Solution
A system and method for receiving and analyzing movement data from mobile devices, using statistical analyses to determine driving patterns and comparing them to previously stored patterns to identify drivers, involving the use of sensors like accelerometers, GPS, and secure data transmission protocols to ensure accuracy and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vehicle-based systems are used to collect driving data, then driving data can be collected, but the systems cannot accurately reflect driving behavior when multiple drivers use the same vehicle or when vehicles are not equipped with such systems
Solution Approach 1:
The patent applies universality by using mobile devices that can serve multiple functions: they act as both personal identification devices and driving behavior monitoring devices. The mobile device collects driving data from any driver regardless of vehicle ownership, making the system universally applicable to multiple drivers and vehicles without requiring vehicle-specific installation
Solution Approach 2:
The patent introduces mobile devices as intermediaries between the driver and the data collection system. Instead of relying on vehicle-based sensors, the mobile device carried by the driver serves as the mediator to capture driving behavior data, thereby solving the problem of multiple drivers using the same vehicle and the issue of vehicle equipment availability
2Quantity of substance
If mobile devices are used to collect movement data, then comprehensive driving data can be obtained, but efficient methods are needed to identify driving patterns and associate them with drivers
Solution Approach 1:
The patent applies feedback by comparing collected movement data against stored driving patterns to identify and confirm driver identity. The system continuously monitors movement data, compares it with established patterns, and uses this feedback loop to accurately associate driving behavior with specific drivers, simplifying the complexity of pattern identification
Solution Approach 2:
The patent applies preliminary action by pre-storing driving patterns for multiple drivers before actual driving occurs. When driving data is collected, the system compares it against these pre-established patterns, eliminating the need for complex real-time analysis and simplifying the identification process
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables more accurate identification of driving patterns and associated drivers, providing comprehensive data analysis that can be used across various applications, including insurance, law enforcement, and marketing, while ensuring data security and integrity.
Implementation Method 1
the movement data may correspond to acceleration data, speed data, location data (e.g., GPS data), or other movement data collected by various movement sensors
Implementation Method 2
location data (e.g., GPS data), or other movement data collected by various movement sensors
Data Source
AI summary
A computing system may be configured to receive and analyze movement data. A stopping point within the movement data may be determined. Additional data related to the determined stopping point may be received from one or more roadway infrastructure devices. A time interval of data may be determined and analyzed based on the additional data. A driving pattern may be determined based on the analyzed determined time interval of data. A driver of a driving trip may be determined based on the driving pattern. Data corresponding to the driver and the driving trip may be outputted. Drivers associated with driving trips may be identified based on the movement data. In some cases, a driving pattern may be determined based on the movement data.