This is an NCDOT-funded project that used a driving simulator to compare the driving performance and mental state of young and middle-aged drivers while navigating standard intersections and novel GSI (contraflow and quadrant) conditions. The experiment also manipulated driver exposure to different configurations of lane assignment and decision point signs.

2.1 Project overview

This study developed datasets on driver behavior in using novel interchange designs to support computational models for predicting performance with new designs and accelerating DOT interchange implementation. Changing Bullet Colors My contributions in this project including:

  • Designed and implemented situation awareness queries to collect data on driver behavior and performance at different interchanges and signage configurations.
  • Designed and executed a human factors experiment to investigate the effects of innovative interchange geometry and traffic control device design on driver visual behavior and performance.
  • Processed and analyzed experimental data using Python and R Studio, demonstrating data analysis skills and expertise in statistical methods, including descriptive and inferential statistics.
  • Led the development of machine learning models to predict erroneous driver actions based on driver status.
  • Drafted the final report for the project, presenting the findings and conclusions.
  • Led the writing of one journal paper and two conference proceeding papers draft.

AHFE Presentation Video

Published and Submitted paper

[6]. Liu, Y., Kaber, D., Cunningham, C., Chase, T., and Pyo, K. (in 2nd review). Analysis of driver behavior at grade-separated intersections to support design. Submitted to Applied Ergonomics.

[C1]. Liu, Y., Kaber, D., Sabahi, S., Cunningham, C, and Pyo, K. (2022). Machine learning models of erroneous driver actions at novel interchange configurations.IEEE International Conference on Human-Machine Systems, pp. 1-6.

[C2]. Liu, Y., Pyo, K., Cunningham, C., Chase, T. and Kaber, D. (2022). Driver situation awareness and cognitive workload effects of novel interchange configurations and associated signage. International Conference on Applied Human Factors and Ergonomics, Vol. 60: 287-296.

Working paper

[7]. Liu, Y., Pyo, K., Cunningham, C., Chase, T. Kaber, D. Novel interchange configurations and associated signage assessment. Target journal: Applied Ergonomics.

2.3 Poster for NC DOT R&I Summit

[4]. Roadway signing and marking of unconventional grade separated intersection designs. North Carolina Department of Transportation (NC DOT) 4th Annual Research & Innovation Summit (March 2023), Raleigh, NC.



2.4 Demo video