AI Telematics Motion Feedback for Continuous Simulation Immersion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current electronic entertainment and simulation systems fail to account for a wide range of sensory data, leading to limitations in motion and immersion, with actuators often reaching their range of motion quickly, causing feedback to stop or return clumsily, degrading the user experience.
Innovation Solution
An AI-enabled telematics system that utilizes machine learning and generative AI to process data from various sources, including actuators, speakers, and user inputs, to predict and generate future states and environments, ensuring seamless feedback and continuous motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If actuators are used to provide motion feedback in simulation systems, then immersion and realism are improved, but the actuators reach their range of motion limits quickly, causing feedback to stop or return clumsily
Solution Approach 1:
The system dynamically adjusts the neutral position of actuators based on predicted future game states. Instead of using a fixed neutral position, the actuator's reference position changes continuously to anticipate upcoming motions, allowing the actuator to operate within its optimal range throughout extended gameplay periods and preventing premature saturation.
Solution Approach 2:
The system uses machine learning models to predict future game states and proactively adjusts actuator positions before the user actually experiences the motion. By anticipating future motions and pre-positioning actuators, the system ensures continuous feedback capability and prevents the actuators from reaching their limits unexpectedly.
2Reliability
If multiple sensory systems are used to replicate real-life sensations, then immersion is improved, but latency between input and feedback increases, eroding the immersive experience
Solution Approach 1:
The system performs preliminary processing of sensory feedback by using machine learning models to predict future game states and pre-calculate the required actuator responses. This allows the system to prepare feedback in advance, reducing the actual latency when the user experiences the motion and maintaining the illusion of real-time responsiveness.
Solution Approach 2:
The system replaces traditional real-time physics calculations with machine learning-based predictions. Instead of computing complex physics equations during gameplay to determine actuator positions, the pre-trained models directly predict the required motions, significantly reducing computational latency while maintaining realism.
3Ease of operation
If actuators are configured to return to neutral orientation, then system constraints are managed, but user experience is degraded when feedback suddenly stops or returns clumsily
Solution Approach 1:
The system replaces the static neutral position concept with a dynamic reference position that evolves based on predicted future game states. The actuator continuously adjusts its target position to anticipate upcoming motions, eliminating sudden stops or clumsy returns while maintaining systematic control through the machine learning-based prediction framework.
Data Source
AI summary
A system and method AI-enabled telematics and actuation for electronic entertainment, simulation, training, and remote operations systems. The system and method disclosed support neuro symbolic reasoning and generative AI enabled experience generation to allow a user or collection of users to experience a wide range of realistic scenarios where the user can pick and choose an experience that best fits their individual or collective preferences. Additionally, the system and method have wide applications to a variety of environments, including but not limited to, racing, sports, military training, vehicle and aircraft operation, and training simulations. The proposed system and method enable realistic, immersive video game, simulation, training, and remote operations environments which are applicable to a wide range of devices, platforms, and mediums for recreational, commercial, industrial, and security uses.