Motorcycle Helmet AR Display Using Location and Orientation Cues
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Solution Overview
Problem
Current transportation systems face challenges in optimizing complex interactions and behaviors in dynamic environments, such as combustion processes, mechanical systems, and human interactions, particularly in classifying and predicting system-level interactions, which limits the effective deployment of artificial intelligence and neural networks.
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
1Adaptability or versatility
If machine learning and neural networks are deployed to classify and predict system-level interactions in complex transportation systems, then the ability to optimize complex interactions and behaviors is improved, but the device complexity and computational requirements increase
Solution Approach 1:
The system divides complex transportation system interactions into multiple classification categories (e.g., object types, behavior patterns, environmental conditions) that can be processed by specialized neural network components. Each segment of the problem is handled by dedicated processing modules within the helmet system, making the overall complex system manageable through modular segmentation of computational tasks.
Solution Approach 2:
The helmet system acts as an intermediary device between the rider and the complex transportation system. It incorporates AI processing capabilities locally to classify and predict interactions, then presents simplified augmented reality information to the rider. This intermediary approach enables sophisticated analysis without requiring the entire system complexity to be managed by a single centralized processor.
2Loss of information
If augmented reality content is presented based on real-time location and orientation data, then the relevance and usefulness of information to the rider is improved, but the processing requirements and power consumption increase
Solution Approach 1:
The system processes and displays only the partial information necessary for safe and effective riding at any given moment. Rather than processing all possible data streams continuously, the helmet prioritizes critical location and orientation data for augmented reality content generation, reducing overall computational load and power consumption while maintaining high relevance of displayed information.
Solution Approach 2:
The system continuously receives feedback from location and orientation sensors, then dynamically adjusts which augmented reality content is generated and displayed. This feedback loop enables the system to focus computational resources on processing only the data that directly impacts current riding context, reducing wasted energy on irrelevant information processing while maintaining high information relevance.
3Measurement precision
If the system integrates multiple sensors and communication systems for real-time data processing, then the measurement precision and reliability of rider state detection is improved, but the device complexity and cost increase
Solution Approach 1:
The patent combines multiple sensor types (location sensors, orientation sensors, rider state sensors, motorcycle state sensors) into a single integrated helmet system. By merging these previously separate systems into one unified device, the patent reduces overall system complexity while maintaining high measurement precision through coordinated multi-sensor data processing and fusion algorithms.
Solution Approach 2:
The helmet system is designed as a multi-functional universal device that simultaneously performs navigation, communication, augmented reality display, rider state monitoring, and motorcycle state monitoring functions. This universal approach consolidates multiple specialized devices into one system, reducing overall complexity while achieving high precision measurements across all functions through shared processing resources.
Data Source
AI summary
A vehicle includes a display disposed to facilitate presenting an augmentation of content in an environment of a rider of the vehicle; a circuit for registering at least one of location and orientation of the vehicle; a machine learning circuit that determines at least one augmentation parameter by processing at least one input relating to at least one of the rider and the vehicle; and a reality augmentation circuit that, responsive to the at least one of the location or the orientation of the vehicle, generates an augmentation element for presenting in the display, the generating based at least in part on the at least one augmentation parameter.