Vehicle Touchscreen Motion Compensation for Accurate Input
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Solution Overview
Problem
Touch screens in vehicles become difficult to use when the vehicle is in motion due to vibrations, making it challenging to accurately press desired buttons on the screen.
Innovation Solution
A touch screen motion compensation system using an accelerometer to generate data indicating vehicle motion, a touch screen display to capture user input, and a computing system to compensate touch location data based on accelerometer data, potentially enhanced with neural networks and adaptive filtering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a touch screen display is used in a moving vehicle, then the user can access control functions, but the vibrations and motion make it difficult to accurately press desired buttons
Solution Approach 1:
The system performs preliminary action by predicting the intended touch location before the actual touch occurs. The neural network model uses accelerometer data and historical touch patterns to anticipate where the user intends to touch, compensating for motion-induced deviations before they affect the input accuracy.
Solution Approach 2:
The system implements feedback by continuously monitoring accelerometer data during vehicle motion and using this information to dynamically adjust touch location predictions. The neural network processes real-time vibration data and previous touch inputs to refine its predictions, creating a closed-loop system that adapts to changing motion conditions.
2Measurement precision
If the touch screen compensates for motion using accelerometer data, then touch location accuracy improves, but the system complexity increases
Solution Approach 1:
The neural network model serves multiple functions: it predicts touch locations, filters vibration noise, and adapts to different driving conditions and user behaviors. This multi-functionality reduces the need for separate dedicated components for each function, thereby managing system complexity while maintaining high touch location accuracy.
Solution Approach 2:
The system performs self-service by using its own historical touch data and accelerometer readings to automatically train and improve its prediction model. The neural network continuously learns from user behavior patterns and vehicle motion characteristics without requiring manual calibration or external intervention, reducing the complexity of system setup and maintenance.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves the accuracy of touch input detection on moving vehicles by predicting the intended touch location, compensating for vehicle motion, and adapting to user behavior over time.
Implementation Method 1
an accelerometer configured to generate accelerometer data indicating a motion of the vehicle
Data Source
AI summary
Apparatus and methods for touch screen motion compensation are disclosed herein. In certain embodiments, a touch screen motion compensation system for a vehicle includes an accelerometer that generates accelerometer data indicating a motion of the vehicle, a touch screen display that generates touch location data in response to tactile input from a user, and a computing system that generates compensated touch location data by compensating the touch location data based on the accelerometer data.