Driver Profile Adaptation for Vehicle Safety Metrics
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Solution Overview
Problem
Advanced driver assistance systems (ADAS) lack the ability to customize and personalize settings based on individual driver performance and behavior, and there is a need for systems that can learn and adapt to create a driver profile that can be transferred or shared between drivers or vehicles, while ensuring safety metrics are met.
Innovation Solution
A system comprising a processor and driver assistance module that processes driver profile data to determine conformity with safety metrics, weighing and normalizing non-conforming data to create a conformed driver profile file that can control vehicle functions, allowing for the creation, sharing, and adaptation of driver profiles across different vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If driver profile data is collected and processed to create personalized ADAS settings, then adaptability and customization are improved, but device complexity increases due to data processing and safety metric verification requirements
Solution Approach 1:
The system performs preliminary actions by collecting and processing driver profile data during normal vehicle operation before actual ADAS activation. Driver behavior data is continuously monitored and stored, then processed offline to create personalized profiles that are later applied when ADAS functions are activated, avoiding real-time processing complexity
Solution Approach 2:
The driver profile data is segmented into multiple components including driver behavior data, vehicle performance characteristics, and safety metric verifications. Each segment is processed independently and then integrated to form the complete personalized ADAS configuration, making the complex processing task more manageable
2Reliability
If driver profile data is normalized and weighed to conform to safety metrics, then reliability is improved, but loss of information occurs during data normalization and weighting processes
Solution Approach 1:
The system changes parameters by applying weights to different driver behavior data points and normalizing them to conform to safety metrics. The weighting process adjusts the significance of various behavioral parameters, while normalization transforms the data into a standardized format that meets safety requirements without completely losing the original behavioral patterns
Solution Approach 2:
The system creates a conformant copy of the driver profile data that meets safety metrics while preserving the essential behavioral characteristics. The original raw data is retained, and a processed version is generated that can be safely applied to ADAS functions, maintaining information through the copying process
3Adaptability or versatility
If driver profiles are made transferable and shareable across different vehicles, then adaptability is improved, but safety risks increase due to variations in vehicle performance characteristics
Solution Approach 1:
The system applies local quality by adjusting the transferred driver profile to match the specific vehicle's performance characteristics. Instead of applying a universal profile, the system modifies the profile data to account for local variations in vehicle dynamics, sensor configurations, and ADAS capabilities of each specific vehicle model
Solution Approach 2:
The system uses an intermediary process that verifies and adapts the driver profile data between the source vehicle and target vehicle. This intermediary verification step checks safety metrics and adjusts the profile to ensure compatibility with the receiving vehicle's performance characteristics before applying the personalized settings
Data Source
AI summary
A system, apparatus and method for controlling operation of a vehicle. Driver assistance system (DAS) data may be used for controlling one or more function of the vehicle via a driver assistance system. Driver profile data (DPD) is generated and/or received that includes training data from sensor monitoring of a driver's usage characteristics for at least one feature of a vehicle type during a driver-controlled mode of vehicle driving. The DPD is processed with the DAS data to determine if the DPD conform to one or more safety metrics. A driver profile data file may be created by weighing the portions of the DAS data with the DPD that do not conform with the safety metrics. One or more functions on the vehicle may be controlled via the driver profile data file using the driver assistance system during one of a semi-autonomous and fully autonomous mode of operation.