activity
20192022
most citedUsing Eye-tracking Data to Predict Situation Awareness in Real Time during Takeover Transitions in Conditionally Automated Driving

8 citations · 36 across the 12 of their papers we have counts for

collaborators

15 papers

cs.HC20224 cited

Real-time Trust Prediction in Conditionally Automated Driving Using Physiological Measures

Jackie Ayoub, Lilit Avetisian, X. Jessie Yang +1

Trust calibration presents a main challenge during the interaction between drivers and automated vehicles (AVs). In order to calibrate trust, it is important to measure drivers' tr…

cs.RO20226 cited

Simultaneous Human-robot Matching and Routing for Multi-robot Tour Guiding under Time Uncertainty

Bo Fu, Tribhi Kathuria, Denise Rizzo +4

This work presents a framework for multi-robot tour guidance in a partially known environment with uncertainty, such as a museum. In the proposed centralized multi-robot planner, a…

cs.HC20213 cited

Disengagement Cause-and-Effect Relationships Extraction Using an NLP Pipeline

Yangtao Zhang, X. Jessie Yang, Feng Zhou

The advancement in machine learning and artificial intelligence is promoting the testing and deployment of autonomous vehicles (AVs) on public roads. The California Department of M…

cs.HC20211 cited

From the Head or the Heart? An Experimental Design on the Impact of Explanation on Cognitive and Affective Trust

Qiaoning Zhang, X. Jessie Yang, Lionel P. Robert

Automated vehicles (AVs) are social robots that can potentially benefit our society. According to the existing literature, AV explanations can promote passengers' trust by reducing…

cs.LG20211 cited

Predicting Driver Takeover Time in Conditionally Automated Driving

Jackie Ayoub, Na Du, X. Jessie Yang +1

It is extremely important to ensure a safe takeover transition in conditionally automated driving. One of the critical factors that quantifies the safe takeover transition is takeo…

cs.HC20212 cited

Toward quantifying trust dynamics: How people adjust their trust after moment-to-moment interaction with automation

X. Jessie Yang, Christopher Schemanske, Christine Searle

Objective: We examine how human operators adjust their trust in automation as a result of their moment-to-moment interaction with automation. Background: Most existing studies meas…