AI Motion Stabilization Tuning for Context-Adaptive Displays
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Solution Overview
Problem
Existing motion stabilization models in displays are not adaptable to various scenarios and contexts, leading to discomfort and reduced usability due to motion-induced mismatch between visual, vestibular, and proprioceptive systems, causing symptoms like nausea and dizziness.
Innovation Solution
An AI-driven method dynamically tunes motion stabilization algorithms based on context, using machine learning models to adjust parameters such as gaze gain, roll angle, and drift correction, accessed from cloud services, to stabilize displays in dynamic environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed motion stabilization model is used, then the system is simple and fast, but it cannot adapt to various scenarios and contexts leading to motion-induced discomfort
Solution Approach 1:
The patent implements dynamic adaptation of motion stabilization models by selecting different models based on detected context (e.g., vehicle motion, pedestrian movement). The system transitions from a fixed model to a dynamic model selection mechanism that adjusts stabilization parameters in real-time based on environmental conditions, resolving the contradiction between adaptability and complexity through contextual awareness.
Solution Approach 2:
The system changes stabilization parameters such as gain factors and correction coefficients based on detected context data. By modifying these parameters dynamically rather than using fixed values, the system achieves adaptability to different scenarios while maintaining a relatively simple underlying stabilization algorithm structure.
2Reliability
If motion stabilization is applied without context awareness, then the processing is fast and simple, but it causes motion-induced mismatch between visual, vestibular, and proprioceptive systems
Solution Approach 1:
The system performs preliminary context detection and model selection before applying motion stabilization. By pre-identifying the appropriate stabilization model based on context data (e.g., detecting vehicle motion vs. pedestrian movement), the system prepares the correct processing parameters in advance, ensuring effective stabilization without time-consuming calculations during the actual stabilization process.
Solution Approach 2:
The system incorporates feedback mechanisms where context detection continuously monitors environmental conditions and adjusts the stabilization model selection accordingly. This feedback loop ensures that the most appropriate model is always active, improving reliability while maintaining efficient processing through real-time adaptation rather than exhaustive computation.
3Reliability
If a comprehensive motion stabilization model is used, then the stabilization performance is high, but the computational load increases significantly
Solution Approach 1:
The patent segments the motion stabilization task by dividing the parameter space into different contexts (e.g., vehicle motion, pedestrian movement, stationary). Instead of using a single comprehensive model for all scenarios, the system selects and applies only the necessary subset of stabilization parameters appropriate for each context, reducing computational load while maintaining high performance for the specific scenario at hand.
Solution Approach 2:
The system applies different levels of stabilization complexity to different contexts. For example, vehicle motion scenarios receive more comprehensive stabilization treatment while pedestrian scenarios use simplified models. This local quality approach ensures high performance where needed while minimizing unnecessary computational resources in other contexts.
Data Source
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AI summary
Embodiments of the present disclosure provide techniques for adaptive tuning of motion stabilization models using artificial intelligence. A motion stabilization model for use with a device may be identified. Context data associated with a device may be identified. Metadata comprising one or more model parameters for the motion stabilization model may be identified. Model adjustment data may be generated based on the context data and the metadata by applying the context data and the metadata to a machine learning tuning model. The model adjustment data may be applied to the motion stabilization model to tune the motion stabilization model. The motion stabilization model may be configured to facilitate motion stabilization to account for screen motion of the display of the device and eye motion of a user relative to each other in a vehicle.