Dual Neural Networks for Rider-Aware Motorcycle AR Control
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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, where existing AI technologies struggle to classify and predict system-level interactions 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 determine parameters for presenting content based on the rider's and motorcycle's states, enabling semi-autonomous or self-driving capabilities and optimizing user experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If advanced AI technologies and neural networks are deployed to optimize complex transportation system interactions, then system-level prediction and classification accuracy is improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent divides the complex transportation system into multiple independent neural network components, each specialized for specific tasks (combustion optimization, mechanical system monitoring, human behavior analysis). This segmentation allows each network to focus on particular aspects of system interaction, improving overall classification accuracy while managing complexity through modular architecture.
Solution Approach 2:
The patent creates a universal neural network platform that can be selectively deployed across different transportation system contexts. The system is designed to handle multiple types of interactions (chemical, mechanical, human) using a flexible architecture that adapts to various application scenarios, reducing the need for separate specialized systems.
2Productivity
If selective deployment of specialized neural networks is implemented, then optimization of specific transportation parameters is improved, but system integration complexity increases
Solution Approach 1:
The patent implements a dynamic system where neural networks can be selectively activated or deactivated based on current operational requirements. The system adapts its complexity in real-time, deploying only the necessary specialized networks for given conditions, which optimizes productivity while managing integration complexity through conditional activation.
Solution Approach 2:
The neural network system includes self-configuration and self-management capabilities that automatically handle integration tasks. The system can autonomously determine which networks to deploy and how to integrate them, reducing the manual integration complexity while maintaining high optimization efficiency.
Data Source
AI summary
A system may include a first neural network trained to determine an operating state of a vehicle from data about the vehicle captured in an operating environment of the vehicle, where the first neural network processes information about the vehicle captured by at least one Internet-of things device while the vehicle is operating. A data structure facilitates determining operating parameters configured to influence an operating state of a vehicle. A second neural network operates to: a) process information about a state of the rider occupying the vehicle, b) determine a correlation between the operating state and an effect on the state of the rider, and c) improve at least one of the determined operating parameters of the vehicle based on i) the determined operating state of the vehicle and ii) the correlation between the operating state of the vehicle and the effect on the state of the rider.