Modeling driver behavior during platooning failures

In this project we are developing a model of human behavior during automation failures that may be integrated into current and future design processes for automated vehicles. We will use this model to generate a set of design guidelines for future automated vehicle following technologies that will promote safety and reduce automated driving crashes.

Lab Members: Hananeh Alambeigi

Collaborators:
Dr. Gustav Markkula
Dr. Johan Engström
Dr. Jeffrey Hickman

Sponsors: Safety through Disruption (Safe-D)

Publications:

McDonald, A. D., Alambeigi, H., Engström, J., Markkula, G., Vogelpohl, T., Dunne, J., & Yuma, N. (2019). Toward computational simulations of behavior during automated driving takeovers: a review of the empirical and modeling literatures. Human factors, 61(4), 642-688.

Alambeigi, H., McDonald, A. D. (2020). Modeling Post-takeover Avoidance and Stabilization Steering Control in Automated Vehicles. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 64). Sage CA: Los Angeles, CA: SAGE Publications.

Alambeigi, H., McDonald, A. D., & Tankasala, S. R. (2020). Crash Themes in Automated Vehicles: A Topic Modeling Analysis of the California Department of Motor Vehicles Automated Vehicle Crash Database. In Proceedings of the 99th Transportation Research Board Annual Meeting.

Reducing drowsy driving in night-shift workers

In this project, we will employ a user-centered design approach to develop an educational and technological intervention program to prevent drowsy driving crashes among shift-workers.

Lab Members: Alec Smith

Collaborators:
Dr. Farzan Sasangohar
Karim Zahed
Houston Methodist Research Institute (HMRI).

Sponsors: Road to Zero Safe Systems Innovation Grant, National Safety Council (NSC)

Publications:
Smith, A., Sasangohar, F., McDonald, A. D., Bonuel, N., Shui, H., Ouko, C., & Lazaro, L. (2019, September). Drowsy Driving Among Shift Work Nurses: A Qualitative Data Analysis. In Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care (Vol. 8, No. 1, pp. 167-171). Sage CA: Los Angeles, CA: SAGE Publications.

Smith, A., Sasangohar, F., McDonald, A. (2019). Mitigating Drowsy Driving in Night Shift Nurses Using a Technological Device. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting. Santa Monica, CA: Human Factors and Ergonomics Society.

Smith, A., McDonald, A., Sasangohar, F. (2020). Preventing drowsy driving crashes among night shift nurses: An evaluation of education and technological interventions. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting. Santa Monica, CA: Human Factors and Ergonomics Society.

Smith, A., McDonald, A.D., Sasangohar, F. (2020). Night-shift nurses and drowsy driving: A qualitative study. International Journal of Nursing Studies.

Zahed, K., Smith, A., Sasangohar, F., McDonald, A. (2020). Investigating the effect of education and drowsiness detection on nurses’ beliefs and attitudes towards drowsy driving. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting. Santa Monica, CA: Human Factors and Ergonomics Society.

Social Media Analyses of Autonomous Vehicle Crashes

Lab Members: Jaycelyn Jefferson, Hananeh Alambeigi, Ran Wei

Sponsors: Safety through Disruption (Safe-D)

Publications:

Jefferson, J., & McDonald, A. D. (2019, November). The autonomous vehicle social network: Analyzing tweets after a recent Tesla autopilot crash. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 63, No. 1, pp. 2071-2075). Sage CA: Los Angeles, CA: SAGE Publications.

Wei, R., Alambeigi, H., McDonald, A. D. (2020). Crash Tags: Topic Modeling Social Media Data After Fatal Automated Vehicle Crashes. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 64). Sage CA: Los Angeles, CA: SAGE Publications.

Inverse Reinforcement Learning to Detect Mental Fatigue

Lab Members: Ran Wei

Collaborators
Dr. Alfredo Garcia

Sponsors: Army Research Office

Using Machine Learning to Improve Mental Health

Lab Members: Mahnoosh Sadeghi

Collaborators:
Dr. Farzan Sasangohar

Publications:

Smith, A., McDonald, A.D., Sasangohar, F. (2020). Night-shift nurses and drowsy driving: A qualitative study. International Journal of Nursing Studies.

Zahed, K., Smith, A., Sasangohar, F., McDonald, A. (2020). Investigating the effect of education and drowsiness detection on nurses’ beliefs and attitudes towards drowsy driving. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting. Santa Monica, CA: Human Factors and Ergonomics Society.

Sadeghi, M., Sasangohar, F., McDonald, A., & Hegde, Sudeep (2020). Understanding Heart Rate Reactions to Post Traumatic Stress Disorder (PTSD) Among Veterans. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 64). Sage CA: Los Angeles, CA: SAGE Publications.

Sadeghi, M., Sasangohar, F., & McDonald, A. (2019). Analyzing Heart Rate as a Physiological Indicator of Post-Traumatic Stress Disorder: A Scoping Literature Review. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 63, No. 1, pp. 1936-1936). Sage CA: Los Angeles, CA: SAGE Publications.

Driver Attitudes and Emotions Bicyclist Safety Study (T3 Triads for Transformation)

This research explores mechanisms of traffic crashes between drivers and bicyclists, particularly the role of emotional associations of drivers with bicyclists and how those associations effect visual search, inattentional blindness, and roadway interactions with bicyclists.

Triads for transformation

Information on the T3 (Texas A&M Triads for Transformation program) can be found here.

Lab Members: Hananeh Alambeigi

Collaborators:
Dr. Tara Goddard
Dr. Brian Anderson
Kim Jeesu

Publications:

Goddard, T., McDonald, A. D., Alambeigi, H., Kim, A. J., & Anderson, B. A. (2020). Unsafe bicyclist overtaking behavior in a simulated driving task: The role of implicit and explicit attitudes. Accident Analysis & Prevention, 144, 105595.

Using Machine Learning to Detect Crash Risks

Lab Members: Daniel Ik-Obazee

Sponsors: Safety through Disruption (Safe-D)

Using Machine Learning to Design Procedures

Vehicle-based drowsy driving detection

This goal of this project is to use innovative data analysis and reduction techniques to design state of the art algorithms for detecting drowsy driving. The project specifically focuses on integrating driving context (i.e. types of roads, traffic, road condition) and various graphical modeling approaches (e.g. Hidden Markov Models, Hidden semi-Markov Models) to improve algorithm performance.

Distracted driving detection

The goal of this study is to design innovative algorithms that detect various types of driver distraction including cognitive, sensory-motor, and emotional distraction.

Naturalistic driving and chronic health conditions

This series of naturalistic driving studies focuses on analyzing how drivers’ behavior changes as they are impacted by chronic health conditions such as obstructive sleep apnea (OSA), diabetes, and age related decline. The goal of the project is to design algorithms to comb through large amounts of driving data and identify changes in driving behavior that may lead to safety hazards. After development and refinement the algorithms can be integrated into the health care process to reduce in patient visits and assess treatment options.