Vehicle Output Action Selection Using Emotion and Context Sensing
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Solution Overview
Problem
Automotive vehicles generate vast amounts of sensor data that remains untapped, limiting the potential for personalized and contextually aware experiences for drivers and passengers, as existing systems fail to effectively integrate and utilize this data to adapt vehicle settings in real-time based on emotional states and environmental conditions.
Innovation Solution
A vehicle experience system that utilizes multiple sensors to measure emotional states and environmental contexts, creating personalized models to dynamically control entertainment, safety, and comfort systems by processing data from various sources, including internal and external sensors, and communicating with remote servers to provide real-time adaptations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple sensors are deployed to capture comprehensive sensor data for personalized experiences, then the quality and personalization of vehicle experience is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The system segments sensor data processing by creating separate processing pipelines for different sensor types (biometric sensors, environmental sensors, vehicle system sensors) and different user profiles. Each sensor module processes its data independently and feeds into a centralized personalization engine, reducing integration complexity while maintaining comprehensive data capture.
Solution Approach 2:
The vehicle experience system serves multiple functions simultaneously: it processes biometric data for emotional state detection, environmental data for comfort optimization, vehicle operation data for safety monitoring, and entertainment preferences for media recommendations. This multi-functional approach consolidates what would otherwise require separate systems into a unified platform.
2Speed
If sensor data is processed in real-time to adapt vehicle settings dynamically, then the responsiveness and user satisfaction is improved, but the computational load and energy consumption increase
Solution Approach 1:
The system performs preliminary processing of sensor data by pre-computing user profiles, emotional state baselines, and preference patterns during periods of low computational demand. This allows real-time adaptations to build upon pre-prepared data structures, reducing the computational load during critical response moments while maintaining fast response times.
Solution Approach 2:
The personalization engine operates continuously at a low level, constantly refining user profiles and emotional state models based on incoming sensor data. This continuous background processing eliminates the need for intensive batch processing, distributing computational load evenly over time and reducing peak energy consumption while maintaining real-time responsiveness.
3Measurement precision
If comprehensive sensor data is collected and stored for personalized models, then the accuracy of personalization is improved, but the data management complexity and privacy concerns increase
Solution Approach 1:
The system extracts only the essential features from comprehensive sensor data for storage in user profiles, such as emotional state patterns, preference tendencies, and behavioral characteristics. Raw sensor data is processed to extract these key features, which are then stored for model training and personalization, reducing data management overhead while maintaining personalization accuracy.
Solution Approach 2:
The system introduces intermediate data structures (user profiles, emotional state models, preference models) that mediate between raw sensor data and personalization applications. These intermediaries aggregate and structure data in a manageable format, reducing the complexity of directly managing comprehensive raw sensor data while preserving the information needed for accurate personalization.
Data Source
AI summary
The present embodiments relate to selection and execution of one or more output actions relating to a modification of at least one feature of a vehicle. A series of sensors on a vehicle can acquire data that can be used to identify vehicle environment characteristics indicative of a status of a vehicle environment and an emotional state of the user. The vehicle environment characteristics and the emotional state can be processed using a user model that corresponds to a user to generate one or more selected output actions. The output actions can be executed on the vehicle to increase user experience. The output actions can relate to any of entertainment features, safety features, and/or comfort features of the vehicle.