Motorcycle Helmet AR Control Using Rider State Neural Networks
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Solution Overview
Problem
Current transportation systems face challenges in optimizing complex interactions and behaviors in dynamic environments, such as those involving combustion processes, mechanical systems, and human elements, due to limitations in applying specialized AI technologies like neural networks effectively.
Innovation Solution
A motorcycle helmet system that includes a data processor for communication between the rider and motorcycle, an augmented reality display, and machine learning to adjust parameters based on rider and motorcycle inputs, enabling semi-autonomous or self-driving capabilities and optimizing user experiences through real-time feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If specialized AI technologies like neural networks are applied to optimize complex interactions in transportation systems, then system-level optimization improves, but device complexity increases
Solution Approach 1:
The patent segments the AI optimization system into multiple specialized neural networks, each handling specific tasks (e.g., combustion optimization, mechanical system control, human behavior prediction). This modular approach allows system-level optimization while managing complexity through division of functions across separate network components.
Solution Approach 2:
The patent introduces an intermediary layer that coordinates between multiple specialized neural networks and the physical transportation system components. This mediator manages the complexity by providing a standardized interface between the AI algorithms and the complex mechanical/chemical systems they control.
2Adaptability or versatility
If multiple specialized neural networks are deployed to handle different system interactions, then optimization capability improves, but system complexity increases
Solution Approach 1:
The patent implements a universal neural network architecture that can perform multiple optimization functions across different transportation system domains. This multi-functional system handles combustion processes, mechanical control, and human behavior analysis through a unified platform, reducing overall system complexity compared to completely separate specialized systems.
Solution Approach 2:
The patent employs dynamic neural network configurations that can adapt their structure and parameters based on the specific optimization task at hand. The system dynamically selects and configures appropriate network components for different operating conditions, providing versatile optimization capability while managing complexity through adaptive reconfiguration rather than fixed multiple systems.
Data Source
AI summary
A vehicle to operate with a rider according to an operating parameter. The vehicle includes a set of physiological monitoring sensors configured to measure a physiological parameter of a rider within the vehicle. The vehicle further includes a neural network trained on data related to a set of rider in-vehicle experiences to determine a state of the rider by processing outputs of the set of physiological monitoring sensors. The vehicle further includes an augmented or virtual reality system configured to present augmented reality content to the rider within the vehicle based, at least in part, on the physiological parameter. The vehicle further includes an optimization system to automatically identify a variation in the operating parameter to improve a measure of the state of the rider and generate a command to vary the operating parameter and the augmented reality content according to the variation.