Automated Vehicle Control Using Occupant Emotion Feedback
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Solution Overview
Problem
Existing vehicle control systems for automated driving fail to account for individual occupant emotions and driving characteristics, leading to potential anxiety or discomfort when multiple vehicles interact, as they often adjust control settings ex post facto or based on generic parameters, rather than proactive, emotion-based weighting of control features.
Innovation Solution
A vehicle control apparatus and method that determines interacting vehicles and acquires occupant emotions to generate weighted average control feature parameters, prioritizing safe driving characteristics and emotions to adjust vehicle control proactively, ensuring cooperative and comfortable travel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If vehicle control systems adjust control settings ex post facto or based on generic parameters, then the system complexity is reduced, but occupant comfort and anxiety reduction are worsened
Solution Approach 1:
The system performs preliminary actions by acquiring occupant emotion information before control adjustments are needed, and by proactively adjusting control parameters based on predicted interactions with other vehicles. This allows the system to prevent anxiety and discomfort before they occur, rather than reacting ex post facto, thereby improving occupant comfort without requiring overly complex real-time processing during critical moments.
Solution Approach 2:
The system implements feedback by continuously acquiring occupant emotion information and using it to adjust control parameters. The emotion information serves as feedback from the occupant state, creating a closed-loop control system that adapts to individual occupant needs, thereby improving comfort while maintaining manageable system complexity through structured feedback processing.
2Ease of manufacture
If vehicle control systems use generic control parameters, then the ease of manufacture and deployment is improved, but adaptability to individual occupant emotions and interactions is worsened
Solution Approach 1:
The system applies local quality by customizing control parameters for specific interaction scenarios and individual occupant emotions rather than using uniform generic parameters. Control features are selectively adjusted based on the specific emotional state and interaction context, allowing the system to maintain a standardized core architecture while providing personalized adaptation where needed.
Solution Approach 2:
The system performs preliminary actions by acquiring and analyzing occupant emotion information in advance, and by pre-determining appropriate control parameter adjustments before interactions occur. This allows the system to prepare personalized control strategies ahead of time, improving adaptability without requiring complex real-time decision-making during critical interaction moments.
3Reliability
If vehicle control systems proactively adjust control parameters based on real-time emotion and interaction data, then occupant comfort and safety are improved, but device complexity and processing requirements are worsened
Solution Approach 1:
The system applies segmentation by dividing the control adjustment process into distinct components: emotion information acquisition, interaction determination, control feature parameter calculation, and execution. This modular segmentation allows each component to be optimized independently, managing overall system complexity while enabling comprehensive proactive control based on real-time emotion and interaction data.
Solution Approach 2:
The system implements parameter changes by dynamically adjusting control feature parameters based on acquired emotion information and determined interactions. Rather than redesigning the entire control system, the approach modifies specific control parameters (such as acceleration, deceleration, and following distance) in response to emotional states, thereby improving reliability through targeted adjustments without proportionally increasing overall device complexity.
4Ease of operation
If vehicle control systems consider emotions of multiple interacting vehicles, then cooperative driving comfort is improved, but information processing requirements and device complexity are worsened
Solution Approach 1:
The system applies the taking out principle by extracting only the essential emotion information and interaction characteristics needed for control adjustments, rather than processing all available data from multiple vehicles. This selective extraction reduces the quantity of data that must be processed while still enabling comprehensive cooperative control, thereby improving driving comfort without proportionally increasing processing requirements.
Solution Approach 2:
The system implements local quality by focusing emotion-based control adjustments on specific interaction scenarios and relevant vehicles rather than uniformly processing data from all surrounding vehicles. Control parameters are adjusted locally for each identified interaction based on the specific emotional states involved, reducing overall data processing load while maintaining high cooperative driving comfort in critical situations.
Data Source
AI summary
A vehicle control apparatus includes an interaction determination unit, an occupant emotion acquisition unit, and a vehicle controller. The interaction determination unit is configured to determine a second vehicle that interacts with a first vehicle during automated driving. The occupant emotion acquisition unit is configured to acquire an emotion of an occupant of the first vehicle. The vehicle controller is configured to perform vehicle control of the first vehicle, on the basis of the emotion of the occupant of the first vehicle and an emotion of an occupant of the second vehicle that has been determined to interact with the first vehicle by the interaction determination unit.