Vehicle Occupant Support Pattern Recognition
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Solution Overview
Problem
Conventional vehicle assistance systems react in a standardized manner to driver inputs, failing to recognize and adapt to individual driver behavior patterns, which limits their efficiency and effectiveness in providing personalized support.
Innovation Solution
A method that records and analyzes user information and vehicle parameters to determine behavioral patterns, allowing for personalized vehicle function activation based on recognized patterns, with the ability to learn and store new patterns for improved support.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional assistance systems use standardized reaction to driver inputs, then the system structure is simple and reliable, but the system cannot recognize and adapt to individual driver behavior patterns, reducing effectiveness
Solution Approach 1:
The system performs preliminary actions by continuously recording user information and vehicle parameters during normal operation, building behavioral patterns in advance through learning processes. This allows the system to have driver-specific assistance ready before it is actually needed, rather than reacting generically to each input
Solution Approach 2:
The system implements feedback mechanisms where sensor data from cameras and other sensors continuously monitors driver behavior, compares it against learned patterns, and adjusts assistance functions accordingly. This closed-loop feedback enables the system to adapt to individual drivers while maintaining operational reliability
2Measurement precision
If the system records and analyzes comprehensive user information and vehicle parameters to determine behavioral patterns, then personalized support is achieved, but the data processing requirements and computational load increase
Solution Approach 1:
The system applies local quality by focusing data collection and analysis on specific, behavior-relevant parameters rather than processing all possible sensor data uniformly. The analysis unit selectively processes user information (eye position, head position, gaze direction) and vehicle parameters based on the current operational context and detected behavioral patterns
Solution Approach 2:
The system changes parameters dynamically by adjusting the level of data collection and processing intensity based on driving conditions and confidence in pattern recognition. When behavioral patterns are clearly established, the system can operate with lower processing requirements while maintaining precision
3Productivity
If the system automatically executes vehicle functions based on recognized behavioral patterns, then driver support efficiency is improved, but the risk of false activation and system errors increases
Solution Approach 1:
The system applies partial action by not automatically executing all vehicle functions with absolute certainty. Instead, it uses behavioral pattern recognition to trigger assistance functions with appropriate confidence thresholds, allowing for partial automation where human oversight remains appropriate while maintaining high efficiency in well-established patterns
Data Source
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AI summary
In the inventive method for supporting a vehicle occupant, user information about the behavior and/or condition of the vehicle occupant, as well as vehicle parameters, are acquired (1, 2). The acquired user information and vehicle parameters are combined and jointly analyzed (3), and at least one behavior pattern of the vehicle occupant is determined from the jointly analyzed user information and vehicle parameters (4). The behavior pattern is stored together with the user information (5). Upon subsequent acquisition of user information, this is compared with the user information of the at least one stored behavior pattern (6). If the behavior pattern matches, a vehicle function is automatically executed (7).