10 citations · 36 across the 12 of their papers we have counts for
12 papers
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…
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…
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…
Cause-and-Effect Analysis of ADAS: A Comparison Study between Literature Review and Complaint Data
Jackie Ayoub, Zifei Wang, Meitang Li +4
Advanced driver assistance systems (ADAS) are designed to improve vehicle safety. However, it is difficult to achieve such benefits without understanding the causes and limitations…
Anticipated emotions associated with trust in autonomous vehicles
Lilit Avetisian, Jackie Ayoub, Feng Zhou
Trust in automation has been mainly studied in the cognitive perspective, though some researchers have shown that trust is also influenced by emotion. Therefore, it is essential to…
An Investigation of Drivers' Dynamic Situational Trust in Conditionally Automated Driving
Jackie Ayoub, Lilit Avetisyan, Mustapha Makki +1
Understanding how trust is built over time is essential, as trust plays an important role in the acceptance and adoption of automated vehicles (AVs). This study aimed to investigat…
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…
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…
Predicting Driver Fatigue in Automated Driving with Explainability
Feng Zhou, Areen Alsaid, Mike Blommer +5
Research indicates that monotonous automated driving increases the incidence of fatigued driving. Although many prediction models based on advanced machine learning techniques were…
Modeling Dispositional and Initial learned Trust in Automated Vehicles with Predictability and Explainability
Jackie Ayoub, X. Jessie Yang, Feng Zhou
Technological advances in the automotive industry are bringing automated driving closer to road use. However, one of the most important factors affecting public acceptance of autom…
Psychophysiological responses to takeover requests in conditionally automated driving
Na Du, X. Jessie Yang, Feng Zhou
In SAE Level 3 automated driving, taking over control from automation raises significant safety concerns because drivers out of the vehicle control loop have difficulty negotiating…
Examining the Effects of Emotional Valence and Arousal on Takeover Performance in Conditionally Automated Driving
Na Du, Feng Zhou, Elizabeth Pulver +4
In conditionally automated driving, drivers have difficulty in takeover transitions as they become increasingly decoupled from the operational level of driving. Factors influencing…