Wearable-Driven Vehicle Parameter Control for Rider Emotion
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Solution Overview
Problem
Current transportation systems face challenges in optimizing complex interactions and behaviors in dynamic environments, such as self-driving vehicles, where combining AI technologies like neural networks with sensory data from wearable devices to assess and improve rider emotional states is needed.
Innovation Solution
An AI system using recurrent and radial basis function neural networks processes wearable sensor data to determine a rider's emotional state and optimizes vehicle parameters, like route and audio content, to enhance the rider's experience, incorporating expert systems for personalized recommendations based on similar rider responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple types of neural networks are combined to optimize system-level interactions, then the ability to classify and predict complex behaviors improves, but the device complexity increases
Solution Approach 1:
The system divides the complex AI processing into separate specialized neural networks: recurrent neural networks for temporal pattern recognition in sensor data, radial basis function neural networks for optimization decisions, and expert systems for rule-based recommendations. Each network handles specific aspects of the problem, improving overall classification accuracy while managing complexity through functional segmentation.
Solution Approach 2:
The system merges multiple types of neural networks and AI technologies into a unified transportation system that processes sensor data, predicts rider states, and optimizes vehicle operations. This combination leverages the complementary strengths of different AI approaches to achieve superior system-level prediction and optimization capabilities.
2Speed
If real-time processing of sensory input from wearable devices is performed, then the responsiveness to rider emotional state improves, but the use of energy increases
Solution Approach 1:
The system performs preliminary processing of sensor data using recurrent neural networks to identify patterns and predict rider emotional states before triggering full optimization routines. This preliminary analysis filters data to only process significant changes in rider state, reducing the frequency and intensity of energy-consuming optimization operations while maintaining real-time responsiveness.
Solution Approach 2:
The system implements continuous feedback loops where the processed emotional state information is used to adjust vehicle parameters, which in turn affects the rider's state. This feedback mechanism allows the system to learn from past actions and optimize energy usage by making incremental adjustments rather than constant full-scale processing and reprocessing.
Data Source
AI summary
A transportation system includes an artificial intelligence (AI) system for processing a sensory input from a wearable device in a self-driving vehicle to determine an emotional state of a rider and optimizing a vehicle operating parameter to improve the rider emotional state. The AI system includes a recurrent neural network to indicate a change in the emotional state of the rider by a recognition of patterns of emotional state indicative wearable sensor data from a set of wearable sensors worn by the rider. The patterns are indicative of a first degree of a favorable emotional state of the rider and/or a second degree of an unfavorable emotional state of the rider. The AI system further includes a radial basis function neural network to optimize, for achieving a target emotional state of the rider, the vehicle operating parameter in response to the indication of the change in the rider emotional state.