Reducing Motion Sickness in Passengers of Autonomous Personal Mobility Vehicles by Presenting a Driving Path
June 30, 2025 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Yuya Ide, Hailong Liu, Takahiro Wada
arXiv ID
2506.23457
Category
cs.HC: Human-Computer Interaction
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
Autonomous personal mobility vehicles (APMVs) are small mobility devices designed for individual automated transportation in shared spaces. In such environments, frequent pedestrian avoidance maneuvers may cause rapid steering adjustments and passive postural responses from passengers, thereby increasing the risk of motion sickness. This study investigated the effects of providing path information on 16 passengers' head movement behavior and motion sickness while riding an APMV. Through a controlled experiment comparing manual driving (MD), autonomous driving without path information (AD w/o path), and autonomous driving with path information (AD w/ path), we found that providing path cues significantly reduced MISC scores and delayed the onset of motion sickness symptoms. In addition, participants were more likely to proactively align their head movements with the direction of vehicle rotation in both MD and AD w/ path conditions. Although a small correlation was observed between the delay in yaw rotation of the passenger's head relative to the vehicle and the occurrence of motion sickness, the underlying physiological mechanism remains to be elucidated.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted