AI Ride Head Tracking for Predictive Attention-Based Motion
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Solution Overview
Problem
Traditional amusement park rides with pre-programmed profiles fail to dynamically adjust to passenger interactions and attention, leading to a staged and unrealistic experience that limits engagement and immersion.
Innovation Solution
Implement a ride system with attention trackers and AI algorithms to predict passenger attention and adjust the dynamic ride profile, including content rendering and vehicle movement based on real-time head and eye movements, using sensors and control systems to enhance realism and immersion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-programmed profiles are used to control ride vehicle movement and content rendering, then the system operation is simplified and reliable, but the passenger experience becomes staged and unrealistic, limiting engagement and immersion
Solution Approach 1:
The patent transforms static pre-programmed profiles into dynamic ride experiences by continuously adjusting vehicle movement and content rendering based on real-time passenger head tracking data. The system dynamically modifies ride parameters such as vehicle speed, acceleration, and content display timing to match actual passenger attention patterns, making the ride experience adaptive rather than fixed.
Solution Approach 2:
The system implements a closed-loop feedback mechanism where head trackers continuously monitor passenger attention direction and position, feed this data back to the control system, which then adjusts vehicle movement and content rendering in real-time. This feedback loop enables the ride to respond naturally to passenger reactions, creating a more realistic and engaging experience.
2Measurement precision
If pre-programmed profiles determine content rendering irrespective of passenger attention direction, then the system operation is simplified, but the ability to immerse passengers in a realistic experience is limited
Solution Approach 1:
The system applies local quality by rendering content and adjusting vehicle movement specifically in the direction of passenger attention rather than uniformly across all directions. Head trackers identify the precise attention zone, and the control system selectively modifies ride parameters and content rendering only in those specific spatial regions, optimizing both realism and processing efficiency.
Solution Approach 2:
The system performs partial action by selectively rendering and processing content only in the passenger's field of view and attention direction, rather than processing all content universally. This partial processing approach reduces computational load while maintaining immersion, as the system focuses resources only where the passenger is actually looking.
3Adaptability or versatility
If head tracking and AI prediction systems are implemented to adjust ride profiles in real-time, then passenger engagement and immersion are enhanced, but system complexity and processing requirements increase
Solution Approach 1:
The system performs preliminary action by using AI algorithms to predict future passenger attention directions based on historical head tracking data and current ride context. This prediction capability allows the control system to pre-adjust vehicle movement and prepare content rendering before the passenger actually shifts their attention, reducing real-time computational demands while maintaining smooth adaptive responses.
Data Source
AI summary
A ride system may include a ride vehicle that supports a passenger and an attention tracker to determine a current direction of attention of the passenger. The ride system may also include a control system for maintaining an environment of the passenger. Maintaining the environment may include determining a set of content to be incorporated into the environment based at least in part on the current direction of attention as well as a future direction of attention that is determined utilizing a machine learning algorithm.