Driving Style Driver Identification for Vehicle Function Control
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Solution Overview
Problem
Current methods for remote vehicle driver identification are unreliable due to issues with telematics log-in procedures and biometric data matching, often resulting in incorrect or incomplete identification of drivers.
Innovation Solution
The system determines driver identity by analyzing and comparing driving styles with a database of reference styles, using event detection and reporting systems to collect and process data on driving behaviors, and updates biometric data for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If telematics log-in procedures are used for driver identification, then driver identity can be determined, but the identification is unreliable due to improper logging in, data transmission failures, corrupted or faked data
Solution Approach 1:
The patent introduces driving style data as an intermediary verification layer between the driver and the identification system. Instead of relying solely on direct log-in data, the system uses driving style characteristics (speeding patterns, braking habits, lane changing behavior) as a mediator to verify driver identity, making the identification process more reliable against corrupted or faked log-in data
Solution Approach 2:
The system implements feedback by continuously monitoring driving style data and comparing it against stored driver profiles. This feedback loop allows the system to verify driver identity dynamically during the driving excursion, correcting or confirming the initial identification based on actual driving behavior patterns
2Reliability
If camera-based biometric measurements are used for driver identification, then driver identity can be established, but there will very rarely be perfect alignment between stored biometric data and measured data
Solution Approach 1:
The patent changes the identification parameters from static biometric measurements (facial features) to dynamic driving style parameters (speeding frequency, braking patterns, lane changing behavior). These driving style parameters are more stable and consistent over time, reducing the misalignment problem inherent in biometric data that changes with lighting, angle, and facial expressions
Solution Approach 2:
Instead of relying on direct biometric copying and matching, the system creates a behavioral copy or profile of each driver's driving style. This behavioral profile serves as a more reliable reference that can be consistently matched against actual driving excursions, avoiding the alignment issues of biometric data
3Reliability
If driving style analysis is used for driver identification, then driver identity can be determined with higher reliability, but the system complexity increases due to data collection and comparison requirements
Solution Approach 1:
The patent makes the driving style data collection serve multiple functions: it collects data for safety event detection, driver behavior analysis, and driver identification simultaneously. This multi-functionality reduces overall system complexity by using a single data collection infrastructure for multiple purposes rather than requiring separate systems for each function
Solution Approach 2:
The system uses the driver's own driving behavior data to identify and verify their identity, eliminating the need for separate identification hardware or procedures. The driving style data that is already being collected for safety and operational purposes serves the additional function of driver identification, making the system self-identifying
Data Source
AI summary
A system for controlling a vehicle based on a driving style determined driver identity includes sensors that capture driving-style-based data characterizing a driving style of an unknown driver. The system also includes a control unit that determines a driving style vector of the unknown driver from the captured driving-style-based data. The control unit compares the driving style vector of the unknown driver to driving style vectors of a plurality of known drivers to generate a similarity score for each of the plurality of known drivers. The control unit ranks the plurality of known drivers according to their respective similarity scores, and identifies the unknown driver as at least one of the ranked known drivers based on the respective similarity scores. The control unit controls vehicle systems based on the identification of the unknown driver.