In-Vehicle Driver Identification Using Driving Behavior Profiles
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Solution Overview
Problem
In-vehicle computing systems face challenges in accurately identifying the driver based on driver-dependent functions, often relying on transferable items like key fobs or mobile devices, which can lead to misidentification.
Innovation Solution
The system detects driver-dependent functions by inferring the driver's identity through current driving behavior compared to generated driver profiles, using a weighted combination of data from vehicle sensors, external devices, and cloud-based networks to determine a confidence score for accurate identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If transferable items like key fobs or mobile devices are used for driver identification, then the system can perform driver-dependent functions, but misidentification occurs when these items are transferred between drivers
Solution Approach 1:
The patent replaces mechanical/physical identification methods (key fobs, mobile devices) with a behavioral biometric system that uses vehicle sensor data to infer driver identity. The system analyzes driving behavior patterns such as steering inputs, acceleration, and braking characteristics to automatically identify the driver, eliminating the need for transferable physical tokens that can be misplaced or shared.
2Measurement precision
If driver profiles are generated using multiple data sources with weighted combinations, then identification accuracy improves, but processing complexity increases
Solution Approach 1:
The patent changes the parameters used for identification from static physical tokens to dynamic behavioral parameters. Multiple vehicle sensors collect data on driving patterns, and the system applies weighted combinations to these parameters to generate confidence scores for driver identification. The weighting allows the system to prioritize more reliable indicators while maintaining computational feasibility.
Solution Approach 2:
The system performs self-identification by automatically analyzing its own sensor data without requiring external authentication devices. The vehicle's existing sensor network serves the dual purpose of vehicle operation monitoring and driver identification, eliminating the need for separate identification hardware and reducing overall system complexity.
3Adaptability or versatility
If the system selectively enables driver-dependent functions based on inferred identity, then notifications are more relevant to the driver, but false identification may lead to incorrect function activation
Solution Approach 1:
The system implements feedback through confidence score thresholds and verification mechanisms. When driver behavior is inferred, the system calculates a confidence score based on how well the observed behavior matches stored driver profiles. If the confidence score exceeds a threshold, the identification is confirmed and driver-dependent functions are activated; otherwise, the system may request alternative verification or default to a safe state, preventing false activation.
Data Source
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AI summary
Method for an in-vehicle computing system (204) including detecting a request to perform a driver-dependent function (216), and inferring an identity of the driver based on current driving data/behavior relative to one or more driver profiles (206), each of the one or more driver profiles (206) generated using driver-specific past driving data/behavior. The method may further include selectively enabling performance of the driver-dependent function (216) of the in-vehicle computing system (204) based on the inferred driver identity.