Automatic Driver Identification via Sensor Pattern Analysis
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Solution Overview
Problem
Existing vehicle monitoring systems cannot effectively characterize the driver of a vehicle without active participation, such as through physical identifiers or schedules, limiting their ability to associate vehicle behavior with the individual driver.
Innovation Solution
A vehicle monitoring system equipped with sensors and a processor that automatically identifies drivers by analyzing data from various sources, including GPS, accelerometers, and RF signals, over multiple journeys, using pattern classification and clustering algorithms to create a 'driver fingerprint' without requiring active driver involvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If physical identifiers or fixed schedules are used for driver identification, then driver identity can be determined, but the driver must take an active role in the identification process
Solution Approach 1:
The system performs driver identification automatically using sensor data and pattern recognition algorithms without requiring the driver to actively participate. The processor analyzes driving patterns, GPS data, and sensor information to self-determine driver identity, eliminating the need for manual input or active driver involvement in the identification process.
Solution Approach 2:
The patent replaces mechanical identification methods (physical identifiers, manual password entry) with an automated electronic system that uses sensors, GPS, and pattern recognition algorithms. This substitution transforms the identification process from a manual, driver-participating process to an automated, passive process that occurs in the background.
2Loss of information
If automatic driver identification is implemented, then driver behavior can be associated with vehicle data, but the system complexity increases
Solution Approach 1:
The system segments the driver identification process into distinct functional modules: sensor data collection, pattern recognition analysis, driver identity determination, and data association. This segmentation allows the complex task of automatic driver identification to be broken down into manageable components that can be processed independently and systematically.
Solution Approach 2:
The system uses a multi-functional approach where the same sensor array and processor serve multiple purposes: monitoring vehicle operation, collecting driver behavior data, performing pattern recognition, and enabling driver identification. This universality reduces overall system complexity by avoiding dedicated separate systems for each function.
Data Source
AI summary
A vehicle monitoring system includes at least one sensor in the vehicle. A processor receiving information from the at least one sensor, the processor programmed to automatically identify a driver of the vehicle based upon the information from the at least one sensor.