Gesture-Controlled Vehicle Lock Actuation via Adaptive Motion Profiling
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Solution Overview
Problem
Existing methods for actuating vehicle doors and hatches using gestures are inefficient, as they require precise matching of predefined movement profiles, leading to invalid actuation attempts and reduced user acceptance, especially when dealing with variations in object shape, size, and material properties.
Innovation Solution
A method that automatically actuates vehicle closing elements by detecting movements with sensors and adapting to user-specific movement profiles through self-learning, allowing for the generation and storage of new profiles if the detected movement does not match predefined ones, ensuring accurate actuation based on temporal and spatial sensor data within tolerance limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If precise matching of predefined movement profiles is required for actuation, then system security is improved, but user acceptance deteriorates due to invalid actuation attempts
Solution Approach 1:
The system performs preliminary learning of user movement patterns during a setup phase, storing multiple predefined movement profiles before actual operation. This allows the system to anticipate and recognize valid gestures without requiring users to perform exact predetermined movements, thereby reducing invalid actuation attempts while maintaining security.
Solution Approach 2:
The system changes the parameters of movement recognition by accepting a range of variations in gesture parameters (speed, amplitude, trajectory) rather than requiring exact matches. This flexibility in parameter matching reduces false rejections of valid gestures while still maintaining sufficient discrimination against unauthorized attempts.
2Adaptability or versatility
If multiple predefined movement profiles are used, then adaptability to different users is improved, but device complexity increases
Solution Approach 1:
The system provides self-service by automatically learning and storing movement profiles for multiple users through a simplified setup process. Users simply perform their natural gestures, and the system autonomously captures and stores these patterns, eliminating the need for complex manual programming or configuration of multiple user profiles.
Solution Approach 2:
The system performs preliminary learning of user movements during a setup phase, storing multiple predefined movement profiles before actual operation. This allows the system to anticipate and recognize valid gestures without requiring users to perform exact predetermined movements, thereby reducing invalid actuation attempts while maintaining security.
3Measurement precision
If movement detection is highly sensitive, then accuracy in detecting authorized movements is improved, but false detection of unauthorized movements increases
Solution Approach 1:
The system performs preliminary learning of user movement patterns during a setup phase, storing multiple predefined movement profiles before actual operation. This allows the system to anticipate and recognize valid gestures without requiring users to perform exact predetermined movements, thereby reducing invalid actuation attempts while maintaining security.
Solution Approach 2:
The system uses feedback from multiple sensor inputs and compares detected movements against stored profiles to dynamically adjust recognition thresholds. This feedback mechanism allows the system to maintain high sensitivity for authorized users while filtering out false positives from unauthorized attempts.
Data Source
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AI summary
To automatically actuate a locking element (1) of a vehicle (10), a sensor detects the movement of an object in the vicinity of the vehicle (10). If the movement detected by the sensor corresponds to at least one predefined movement profile, the locking element (1) is actuated automatically. If the detected movement does not correspond to at least one predefined movement profile, a detection area is displayed if multiple movements are detected that do not correspond to the movement profile.