activity
20192023
most citedEnabling Team of Teams: A Trust Inference and Propagation (TIP) Model in Multi-Human Multi-Robot Teams

23 citations · 62 across the 16 of their papers we have counts for

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12 papers · 1 filter

cs.HC2023★ 2 cited

Building Trust Profiles in Conditionally Automated Driving

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

Trust is crucial for ensuring the safety, security, and widespread adoption of automated vehicles (AVs), and if trust is lacking, drivers and the public may not be willing to use t…

cs.HC2023

Investigating HMIs to Foster Communications between Conventional Vehicles and Autonomous Vehicles in Intersections

Lilit Avetisyan, Aditya Deshmukh, X. Jessie Yang +1

In mixed traffic environments that involve conventional vehicles (CVs) and autonomous vehicles (AVs), it is crucial for CV drivers to maintain an appropriate level of situation awa…

cs.HC2022★ 4 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.HC2021★ 3 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.HC2021★ 1 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.HC2021★ 2 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…