Vehicle Motion Sickness Prediction Using Passenger-Specific Models
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Solution Overview
Problem
Existing technologies for minimizing motion sickness in vehicle passengers are limited in accuracy, failing to effectively represent the extent of motion sickness, which affects passenger comfort and can lead to severe discomfort causing passengers to want to exit the vehicle while it is in motion.
Innovation Solution
An apparatus and method for controlling a vehicle that predicts a motion sickness index by generating a motion sickness model based on the motion of the passenger and a misery scale measured by the passenger, allowing the vehicle to be controlled to minimize motion sickness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a motion sickness model is generated based on passenger motion and misery scale, then the accuracy of motion sickness prediction is improved, but the device complexity increases
Solution Approach 1:
The system performs preliminary actions by collecting passenger motion data and misery scale inputs before generating the motion sickness model. This advance preparation enables accurate prediction without increasing real-time computational complexity, as the model generation is performed based on pre-collected data patterns.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring passenger motion data and misery scale inputs, then adjusting the motion sickness model accordingly. This feedback loop improves prediction accuracy over time while managing complexity through iterative optimization rather than complex static model structures.
2Object-affected harmful factors
If the vehicle operation is controlled based on predicted motion sickness index, then passenger comfort is improved, but the control system complexity increases
Solution Approach 1:
The control system applies local quality by implementing motion sickness mitigation strategies specifically tailored to the affected passenger's conditions. Rather than complex system-wide control changes, the system adjusts specific vehicle parameters (such as acceleration profiles or route selection) to address the particular motion sickness situation, reducing overall control complexity.
Solution Approach 2:
The system manages control complexity by making parameter changes to vehicle operation based on the predicted motion sickness index. Instead of complex control algorithms, the system adjusts operational parameters such as acceleration, deceleration, and routing to minimize motion sickness, achieving comfort improvement through simple parameter modulation rather than complex control logic.
Data Source
AI summary
An apparatus for controlling a vehicle includes at least one sensor to obtain status data of a user of the vehicle and data related to a motion of the user, and a processor to generate at least one motion sickness model, based on the status data of the passenger and the data related to the motion of the passenger, which are previously obtained, select a motion sickness model corresponding to the status data of the user, from among the at least one motion sickness model, and predict a motion sickness index of the user, by inputting the data related to the motion of the user into the selected motion sickness model.