Phase I: Real-world-driving video based study

• Led the study.

• Designed an experiment with split-plot design to collect data for calibrating drivers' situation awareness predictive models with eye tracking data and road user/environmental properties (75 scenarios spanning different complexity levels with 48 participants).

• Implemented the scenarios with Adobe Pr and developed a web app (JavaScript, HTML) to record participants' responses to Situation Awareness Global Assessment Technique (SAGAT) queries.

• Assessed drivers' ability to detect objects and objects' trajectories with multinomial logistic regression model (R).

Phase II: Driving simulator study.

• Designed an experiment with factorial design to collect data for assessing drivers' situation awareness, eye movement, and driving performance when interacting with pedestrians in different ways at intersections.

• Developed a web app (JavaScript, HTML) to record participants' responses and helped with scenario implementation (UE4).

Related Publication

[1] Xing, Y., Park, S., Akash, K., Wu, X., Misu, T., & Boyle, L. N., Investigating the impact of context and environment on driver's situation awareness. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (accepted. Oct 2022). [Best Paper Award]

[2] Park, S., Xing, Y., Akash, K., Misu, T., & Boyle, L. N. (2022, Sep). The Impact of Environmental Complexity on Drivers' Situation Awareness. In 14th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (pp. 131-138). [Invited for journal special issue] [Honorable Mention] [Link]

Currently working on the invited journal article as the first author and the data analysis for another paper.

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Phase I: Survey

• Investigated knowledge workers' perception towards commuting in autonomous vehicles with text mining techniques including sentiment, co-occurrence, and text clustering analysis on 754 open-ended survey responses (R).

Phase II: Virtual meeting with attendees multitasking - simple phone tasks

• Designed a virtual meeting experiment to collect data for analyzing the impact of roles (distractee/note taker) on attendees' performance on convergent/divergent thinking tasks.

• Processed and analyzed transcripts and chat messages for 23 groups (R).

Phase III: Virtual meeting with attendees multitasking - driving task

• Designed an incomplete block experiment to collect data for investigating the impact on individual/group performance while some meeting attendees were driving.

Related Publication

[1] Xing, Y., Boyle, L. N., Sadun, R., Shaer, O., Lee, J. D., & Kun, A. Perceptions related to engaging in non-driving activities in an automated vehicle while commuting: A text mining approach. Transportation research part F: traffic psychology and behavior (under second round review). (IF: 4.35)

[2] Ansah, A. A., Xing, Y., Kamaraj, A. V., Tosca, D., Boyle, L., Iqbal, S., ... & Shaer, O. (2022, Jun). "I need to respond to this"–Contributions to group creativity in remote meetings with distractions. In 2022 Symposium on HCI for Work (pp. 1-12). [Honorable Mention] [Link]

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• Processed the data from a driving simulator study with 38 participants, which involves a take-over request from the autonomous vehicle while the participants voluntarily engaged in secondary tasks.

• Analyzed the impacts of response types (steer/brake) on task performance with linear mixed effect models (R).

Related Publication

[1] Strle, G., Xing, Y., Miller, E. E., Boyle, L. N., & Sodnik, J. (2021). Take-Over Time: A Cross-Cultural Study of Take-Over Responses in Highly Automated Driving. Applied Sciences, 11(17), 7959. (IF: 2.84) [Link]

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Idealization, data processing, model selection, data analysis, reporting.

[Link to report]

Phone Distracted Driving Behavior

• Extracted 1,244 phone use events and Investigated drivers' speed adaptation strategy under phone distraction with ANOVA and decision tree algorithms (MATLAB and R).

Trip Characteristics Analysis

• Merged broken trips for 60 drivers' 161,055 km naturalistic driving data. Extracted the road types and origin-destination address of each trip via Amap API. Analyzed trip characteristics such as travel frequency (MATLAB and JavaScript).

Cut-in and Car-Following Behaviors

• Developed rule-based algorithm for cut-in event extraction and performed descriptive analysis (MATLAB and SQL).

• Assisted with car-following event extraction (MATLAB and SQL).

Related Publication

[1] Xing, Y., Wang, X., & Lee, C. (2019). Characteristics of Drivers' Cell Phone Use and Their Influence on Driving Performance: A Naturalistic Driving Study. In Transportation Research Board 98th Annual Meeting (No. 19-03410). DC, United States. [Link]

[2] Xing, Y., & Wang, X. (2018). Phone Use Behavior of Chinese Drivers Based on Naturalistic Driving Data. In CICTP 2017: Transportation Reform and Change—Equity, Inclusiveness, Sharing, and Innovation (pp. 4740-4749). Reston, VA: American Society of Civil Engineers. [Best Paper Award] [Link]

[3] Wang, X., Xu, R., Asmelash, A., Xing, Y., & Lee, C. (2020). Characteristics of driver cell phone use and their influence on driving performance: a naturalistic driving study. Accident Analysis & Prevention, 148, 105845. (IF: 6.38) [Link]

[4] Wang, X., Zhu, M., & Xing, Y. (2016). Impacts of collision warning system on car-following behavior based on naturalistic driving data. Journal of Tongji University (Natural Science), 44(7), 1045-1051. (EI, in Chinese)

Designed a driving simulator study to evaluate the impact of interfaces (speech or touch) and destination types (franchised or unique) on the crash risk.

Coordinated with transport companies to install data recorder for naturalist driving data collection. Based on data analysis results, designed, and provided safety training programs for commercial vehicle drivers.

Related Publication

[1] Wang, X., Xing, Y., Luo, L., & Yu, R. (2018). Evaluating the effectiveness of Behavior-Based Safety education methods for commercial vehicle drivers. Accident Analysis & Prevention, 117, 114-120. (IF: 6.38) [Link]

Located the issues of current speech-based editing systems based on literature and testing Google's Voice Typing editor. In charge of testing, design, and prototyping.

[Link to report]

Investigated the issues of GRE Prep. App for non-native English speakers. Took the lead in idealization, design, and prototyping.

[Link to report]

Related Publication

[1] Martell, M., Xing, Y., Sun, X., & Kim, J. E. (2020, Dec). Human-Centered Design of GRE Preparation Applications for Non-Native English Speakers. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 64, No. 1, pp. 1095-1099). Sage CA: Los Angeles, CA: SAGE Publications. [Link]