Vehicle Prosocial Behavior Prediction Using Physiological Inputs
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Solution Overview
Problem
There is a need for a data-driven method to predict prosocial behavior intentions in vehicle users to encourage positive interactions and improve safety and harmony in mobility environments.
Innovation Solution
A system and method that utilizes a prosocial behavior prediction module in vehicles to analyze real-time physiological and behavioral inputs from users, using a pre-trained model to predict prosocial behavior and implement action responses such as feedback or driving assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a prosocial behavior prediction module is implemented to analyze real-time physiological and behavioral inputs, then prosocial behavior intention prediction capability is improved, but device complexity increases
Solution Approach 1:
The prediction module is divided into separate functional components: a physiological data processing unit that analyzes biometric signals, a behavioral data processing unit that analyzes driving patterns and interactions, and a prosocial behavior prediction unit that integrates both data types. This segmentation allows each component to specialize in specific data processing tasks, improving prediction accuracy while managing system complexity through modular design.
Solution Approach 2:
The system introduces a communication interface as an intermediary layer between external sensors (physiological and behavioral) and the prediction module. This intermediary standardizes data input formats and preprocessing, simplifying the core prediction algorithm while enabling comprehensive data collection from multiple sources including wearable devices and vehicle sensors.
2Measurement precision
If real-time physiological and behavioral inputs are collected and analyzed, then prediction accuracy is improved, but information processing requirements increase
Solution Approach 1:
The system performs preliminary processing of physiological and behavioral data before inputting them to the prediction module. Physiological signals are preprocessed to extract relevant features (heart rate variability, skin conductance levels), and behavioral data is preprocessed to identify driving patterns and interaction contexts. This preliminary action reduces the computational burden on the prediction module while maintaining high prediction accuracy.
Solution Approach 2:
The prediction module focuses on analyzing only the most relevant features from physiological and behavioral data streams rather than processing all available information equally. By selectively processing partial data that has the highest predictive value for prosocial behavior, the system achieves accurate predictions with reduced computational energy consumption.
Data Source
AI summary
A method and system for implementing an action response in a vehicle based on predicted prosocial behavior of a user of the vehicle. In one embodiment, the method includes receiving real-time physiological and behavioral inputs from the user of the vehicle. The method also includes inputting the real-time physiological and behavioral inputs into a prosocial behavior prediction module onboard the vehicle. The method further includes using the prosocial behavior prediction module to output a prosocial behavior prediction for the user based on the real-time physiological and behavioral inputs from the user of the vehicle. In response to the output prosocial behavior prediction for the user, the method includes implementing at least one action response to a system of the vehicle.