AI Motion Stabilization Tuning for In-Vehicle Display Contexts

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Solution Overview

Problem

Existing motion stabilization systems in displays are not adaptable to various scenarios, contexts, and devices, leading to ineffective motion stabilization, particularly in dynamic environments such as vehicles.

Innovation Solution

An AI-driven method that dynamically tunes the motion stabilization model based on context data and metadata, using machine learning models to adjust parameters such as gaze, gain, roll angle, and drift correction, thereby enhancing motion stabilization in diverse environments.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If a fixed motion stabilization model is used in displays, then the system structure remains simple, but the system cannot adapt to various scenarios, contexts, and devices, leading to ineffective motion stabilization in dynamic environments

Engineering Contradiction:

Improveadaptability to various scenarios and contextsVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements a machine learning tuning model that dynamically adjusts motion stabilization parameters based on real-time context data. The system transitions from a fixed model to a dynamic one that continuously adapts to changing scenarios, device types, and usage contexts through automated parameter tuning using algorithms that process contextual information and generate optimized stabilization settings.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes physical and operational parameters of the motion stabilization model by using a machine learning tuning model to automatically adjust parameters such as stabilization strength, response sensitivity, and compensation algorithms based on context data including device type, motion characteristics, and usage scenario, thereby achieving adaptability without manual reconfiguration.

Inventive Principle:

Principle #35Parameter changes

2Adaptability or versatility

If motion stabilization parameters are manually configured for different scenarios, then adaptability improves, but the ease of operation decreases due to complex manual adjustments

Engineering Contradiction:

Improvescenario-specific optimizationVSAvoidmanual configuration complexity

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The system performs self-service by automatically detecting the current usage context and adjusting motion stabilization parameters without user intervention. The machine learning tuning model autonomously processes context data from sensors and system state information to generate optimized parameters, eliminating the need for manual configuration while maintaining scenario-specific optimization.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system implements feedback mechanisms where context data from device sensors, usage patterns, and performance metrics are continuously monitored and fed back to the machine learning tuning model. This closed-loop feedback enables automatic parameter adjustment based on real-time conditions, improving ease of operation while maintaining adaptability through data-driven optimization.

Inventive Principle:

Principle #23Feedback

3Reliability

If a simple motion stabilization model is used, then device complexity remains low, but the effectiveness of motion stabilization is insufficient in dynamic environments such as vehicles

Engineering Contradiction:

Improvemotion stabilization effectivenessVSAvoidmodel complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent replaces traditional mechanical or rule-based motion stabilization models with an intelligent machine learning tuning model. This substitution enables the system to process complex contextual information and dynamically optimize stabilization parameters, significantly improving effectiveness in dynamic environments while managing model complexity through efficient algorithm selection and hardware acceleration.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20250138715A1Systems, apparatuses, methods, and computer program products for adaptive tuning of motion stabilization model using artificial intellegence

Publication Date: 2025.05.01 HONEYWELL INTERNATIONAL INC

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.