Vehicle Window Pinching Detection Adaptation
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Solution Overview
Problem
Existing closure panel control systems for vehicles face erroneous detection of pinching due to varying slide resistance between open and close states of a door, and are not effective in mitigating disturbances such as vibrations from road bumps or door openings/closings.
Innovation Solution
A closure panel control apparatus with a drive means, moving speed sensing, storage, pinching sensing, and disturbance sensing, which updates learning data using different coefficients for open and close states, and corrects for disturbances to reduce erroneous pinching detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If learning is performed in the same manner in both open and close states of the door, then the system structure remains simple, but erroneous detection of pinching occurs due to different slide resistance characteristics in each state
Solution Approach 1:
The learning control is segmented into two independent modes: one for when the door is open and another for when the door is closed. The control device stores separate learning values for each state, allowing the system to adapt to the different slide resistance characteristics of each door state without increasing overall structural complexity
Solution Approach 2:
Different learning parameters are applied locally to each door state. The control device selectively uses the appropriate learning value based on the current door state (open or closed), ensuring that the pinching detection threshold matches the local friction characteristics of that specific state
2Device complexity
If a single threshold value is used for pinching detection regardless of door state, then the control system remains simple, but detection errors occur due to varying slide resistance between open and close states
Solution Approach 1:
The threshold value is made dynamic by automatically selecting between different learning values based on the door state. The control device determines whether the door is open or closed and dynamically adjusts the learning value used for pinching detection, allowing the threshold to adapt to changing operating conditions without manual intervention
Solution Approach 2:
The system changes the detection parameter (learning value) based on the door state. Separate learning values are stored for open and closed states, and the appropriate parameter is selected and applied based on the current state, ensuring accurate pinching detection across different operating conditions
3Productivity
If learning data is continuously updated without considering disturbances, then the system adapts quickly to changes, but erroneous detection occurs due to vibrations from road bumps or door operations
Solution Approach 1:
The control device performs preliminary detection to determine whether a disturbance is present before updating the learning value. By checking for abnormal acceleration patterns that indicate disturbances (such as road bumps or door operations), the system prevents premature or erroneous learning updates, ensuring that only valid learning opportunities are captured
Solution Approach 2:
The system uses feedback from the acceleration sensor to monitor the learning process. When a disturbance is detected through abnormal acceleration patterns, the learning update is inhibited. This feedback mechanism ensures that the learning process only proceeds under normal operating conditions, maintaining both speed and reliability
Data Source
AI summary
A controller prestores average moving speed data, which corresponds to respective corresponding positions of a window glass of a door driven by a motor at the time of the closing movement of the window glass, as learning data. The controller senses pinching of an object by the window glass based on the learning data and the speed measurement signal obtained at the time of the closing movement of the window glass. The controller senses a disturbance when the speed measurement signal obtained at the time of the closing movement of the window glass exceeds a predetermined value. The controller updates the learning data based on the average moving speed data at the time of the closing movement of the window glass. The controller controls execution and non-execution of updating of the learning data in response to a close state and an open state of the door.