Predictive Display Compensation for Vehicle Motion Dizziness
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Solution Overview
Problem
Existing methods for compensating dizziness in mobile vehicles are ineffective due to the complexity and timing issues in calculating wobble compensation, which fail to synchronize visual and brain perceptions, leading to reduced anti-dizziness effects.
Innovation Solution
A method and system utilizing machine learning models to predict and compensate display information based on six-degrees-of-freedom, path, and road information, allowing for timely synchronization of visual and brain perceptions by processing display information in advance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wobble compensation calculation is performed using traditional methods, then compensation accuracy can be achieved, but the calculation cannot be completed timely due to extreme complexity
Solution Approach 1:
The patent pre-calculates and stores wobble compensation data for various vehicle states (acceleration, deceleration, turning, suspension) before actual use. When dizziness compensation is needed, the system directly retrieves pre-computed compensation values based on current sensor inputs, eliminating complex real-time calculations while maintaining accuracy.
Solution Approach 2:
The patent divides wobble compensation into separate modules for different vehicle dynamics: acceleration compensation, deceleration compensation, turning compensation, and suspension compensation. Each module handles specific compensation calculations independently, reducing overall computational complexity and enabling timely processing.
2Reliability
If display information is processed with wobble compensation in real-time, then visual and brain perception can be synchronized, but the calculation complexity greatly reduces the anti-dizziness effect
Solution Approach 1:
The system pre-computes compensation values for all anticipated vehicle states and stores them in lookup tables. During actual operation, the display system simply retrieves appropriate compensation data based on current sensor readings, ensuring reliable anti-dizziness effect without complex real-time calculations.
Solution Approach 2:
The patent replaces complex mechanical calculation systems with data-driven retrieval systems. Instead of performing real-time mathematical computations, the system uses sensor inputs to index into pre-computed compensation tables, significantly reducing computational complexity while maintaining synchronization between visual and brain perception.
Data Source
AI summary
A method and system for compensating anti-dizziness predicted in advance are provided. The method for compensating anti-dizziness predicted in advance includes the following steps. A six-degrees-of-freedom information is obtained. Through using a machine learning model, an attitude prediction compensation information is obtained according to the six-degrees-of-freedom information. A path information is obtained. A path prediction compensation information is obtained according to the path information. A road information is obtained. A road prediction compensation information is obtained according to the road information. A display information is compensated according to the attitude prediction compensation information, the path prediction compensation information, or the road prediction compensation information.