Driver Classification Control for Selective Vehicle Feature Access
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Solution Overview
Problem
Advanced driver-assistance systems (ADAS) lack the ability to selectively enable or disable vehicle features based on user or driver classification, which is crucial for enhancing safety and personalization, especially in scenarios involving driver fatigue, intoxication, or inexperience.
Innovation Solution
A networked system that utilizes sensors and electronic circuitry to classify drivers based on biometric and non-biometric data, employing artificial intelligence and machine learning to enable or disable vehicle features such as ADAS, entertainment, and comfort settings, adjusting settings like acceleration, steering, and automation levels according to the driver's classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ADAS systems are designed to automate and enhance vehicle safety features, then road safety and accident prevention are improved, but the system cannot selectively adapt features based on driver classification (fatigue, intoxication, inexperience)
Solution Approach 1:
The ADAS system dynamically adjusts its behavior and enabled features based on real-time driver classification. The system transitions from a static, one-size-fits-all safety approach to a dynamic system that modifies safety feature activation according to the detected driver state (fatigue, intoxication, inexperience), thereby achieving both high reliability and adaptability.
Solution Approach 2:
The system changes operational parameters by selectively enabling or disabling specific ADAS features based on driver classification results. When a driver is classified as fatigued, intoxicated, or inexperienced, the system modifies which safety features are active, adjusting the safety profile to match the driver's capabilities and reducing the risk of system misuse or failure.
2Adaptability or versatility
If vehicle features are selectively enabled or disabled based on driver classification, then personalization and safety are improved, but device complexity increases due to additional sensors and processing requirements
Solution Approach 1:
The electronic circuitry is designed to perform multiple functions: it processes biometric data from various sensors, classifies the driver's state, determines appropriate feature configurations, and controls the enabling/disabling of vehicle features. This multi-functionality reduces the need for separate dedicated systems for each task, thereby managing complexity while achieving high adaptability.
Solution Approach 2:
The system uses the driver's own biometric data and characteristics to automatically classify their state and adjust vehicle features without requiring external intervention or complex manual configuration. The driver's biological signals serve the dual purpose of identification and state assessment, simplifying the overall system architecture.
3Reliability
If biometric sensors and AI classification are implemented, then driver-specific safety adjustments are achieved, but processing time and computational resources increase
Solution Approach 1:
The system performs driver classification as soon as the driver enters the vehicle or before critical driving situations arise. By conducting the biometric analysis and classification in advance, the system ensures that the driver's state is known before safety-critical decisions need to be made, minimizing processing delays during actual driving operations.
Solution Approach 2:
The system continuously monitors driver state through biometric sensors and provides real-time feedback to adjust feature activation. This ongoing feedback loop allows the system to maintain accurate driver classification without requiring complete re-analysis, as it can track changes from the baseline classification and adjust features dynamically as the driver's state evolves.
Data Source
AI summary
A vehicle or a mobile device within or near the vehicle can have multiple sensors to sense biometric features of a user or driver, and electronic circuitry (such as a computing system) can classify the user or driver according to the sensed biometric features. Also, non-biometric factors of the user or driver or of the mobile device of the user or driver can be used to classify the user or driver, e.g., MAC address, RFID, username and password, PIN, etc. Also, factors from interaction with a user interface of the vehicle or the mobile device can be used to classify the user or driver. Such features and factors can be used alone or in combination for the classification, and the classification can use AI (such as an ANN). The vehicle or the mobile device can then selectively enable or disable features of the vehicle based on the classification.