Examining the Effects of Emotional Valence and Arousal on Takeover Performance in Conditionally Automated Driving
January 13, 2020 ยท Declared Dead ยท ๐ Transportation Research Part C: Emerging Technologies
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Na Du, Feng Zhou, Elizabeth Pulver, Dawn M. Tilbury, Lionel P. Robert, Anuj K. Pradhan, X. Jessie Yang
arXiv ID
2001.04509
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.AI,
cs.CY,
cs.RO
Citations
139
Venue
Transportation Research Part C: Emerging Technologies
Last Checked
2 months ago
Abstract
In conditionally automated driving, drivers have difficulty in takeover transitions as they become increasingly decoupled from the operational level of driving. Factors influencing takeover performance, such as takeover lead time and the engagement of non-driving related tasks, have been studied in the past. However, despite the important role emotions play in human-machine interaction and in manual driving, little is known about how emotions influence drivers takeover performance. This study, therefore, examined the effects of emotional valence and arousal on drivers takeover timeliness and quality in conditionally automated driving. We conducted a driving simulation experiment with 32 participants. Movie clips were played for emotion induction. Participants with different levels of emotional valence and arousal were required to take over control from automated driving, and their takeover time and quality were analyzed. Results indicate that positive valence led to better takeover quality in the form of a smaller maximum resulting acceleration and a smaller maximum resulting jerk. However, high arousal did not yield an advantage in takeover time. This study contributes to the literature by demonstrating how emotional valence and arousal affect takeover performance. The benefits of positive emotions carry over from manual driving to conditionally automated driving while the benefits of arousal do not.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Human-Computer Interaction
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
๐ป
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
๐ป
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
๐ป
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
๐ป
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Language Models are Few-Shot Learners
R.I.P.
๐ป
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted