MIT Unit Affiliation:
Lab Affiliation(s):
AgeLab
Post Doc Sponsor / Advisor:
Joseph F. Coughlin / Bryan Reimer
Areas of Expertise:
  • Human Factors
  • Data Analysis
  • Driving Safety
Date PhD Completed:
May, 2014
Expected End Date of Post Doctoral Position:
August 8, 2017

Joonbum Lee

  • Post Doctoral

MIT Unit Affiliation: 

  • Data, Systems, and Society

Lab Affiliation(s): 

AgeLab

Post Doc Sponsor / Advisor: 

Joseph F. Coughlin / Bryan Reimer

Date PhD Completed: 

May, 2014

Top 3 Areas of Expertise: 

Human Factors
Data Analysis
Driving Safety

Personal Statement: 

An Industrial Engineering Ph.D. with a background in Human Factors and driving safety. Significant experience in processing and statistical analysis of time course behavioral data based upon visual, physiological, and performance measures in the driving research domain. Proven track record of publishing high quality innovative research in Human Factors and Computer Science literature. Strong ability to collaborate and work on interdisciplinary research projects as well as work independently. Experience in a fast paced research environment, managing multiple competing project demands, and mentoring junior staff and students. 

Expected End Date of Post Doctoral Position: 

August 8, 2017

CV: 

Research Projects: 

  1. Assessing road type and traffic volume using drivers’ glance data (Sponsored by Jaguar Land Rover): Led project team that assessed effects of road type and traffic volume on drivers’ glance behavior, using manually coded glance/traffic volume data and hidden Markov model
  2. Utilizing head-pose data to surrogate eye-tracking data for driver distraction research (Sponsored by Santos Family foundation): Led project team that utilized head-pose data to surrogate eye-tracking data for driver distraction research, using drivers’ face video and learning algorithms
  3. Advanced Human Factors Evaluator for Automotive Distraction (Sponsored by AHEAD Consortium): Member of technical team for a large academic industry consortium that has developed a new theoretical perspective on driver attention measurement and is creating software tools to assess the attentional demand of multi-modal driver vehicle interfaces 

Thesis Title: 

Integrating the Saliency Map with Distract-R to Predict and Evaluate Distraction Potential

Thesis Abstract: 

There are a growing number of potential distractions in vehicles today, such as navigation, collision warning, and entertainment systems. These systems promise substantial benefits for driving comfort, efficiency and safety, but they might also distract drivers. This dissertation develops computational cognitive models of driver behavior to assess the distraction potential of vehicle displays.

One of the main goals of this dissertation is to integrate a saliency-based model, a saliency map, into Distract-R to build a computational model that can account for top-down and bottom-up attentional influences. The saliency-based model quantifies exogenous influences (e.g., visual features of a display) of visual attention while Distract-R quantifies endogenous influences (e.g., drivers’ goals and expectations) of visual attention with respect to secondary tasks and vehicle displays.

Two experiments were conducted to guide model development and to validate model predications. The experiments showed that design features of vehicle displays affected driving performance and glance duration to the secondary task, and both top-down and bottom-up attentional processes were engaged when drivers interacted with driver-vehicle interfaces.

To integrate Distract-R and the saliency map, activation fields that describe the interaction between top-down and bottom-up attentional process were used to determine glance duration to the display. The integrated model was validated with empirical data, showing that the model could predict drivers’ pattern of glance durations to a level comparable to between-subject variability—the theoretical limit of prediction. This dissertation contributes to modeling driver distraction by integrating two models to account both top-down and bottom-up influence on visual attention, and by building a tool for assessing the potential distraction of vehicle displays.

Top 5 Awards and honors (name of award, date received): 

2012: 1st place for the Intel Outstanding Student Paper award at 4th International Conference on Automotive User Interface and Interactive Vehicular Applications
2007: Best Research Achievement Award from Department of Psychology, Pusan National University

5 Recent Papers: 

Lee, J., Mehler, B., Reimer, B., Ebe, K., and Coughlin, J. F. (Accepted). "Relationship between older drivers’ cognitive abilities as assessed on the MoCA and glance patterns during visual-manual radio-tuning while driving," The Journals of Gerontology, Series B: Psychological Sciences. 

Lee, J., Mehler, B., Reimer, B., and Coughlin J. F. (Accepted). "Sensation seeking and drivers’ glance behavior while engaging in a secondary task," Human Factors and Ergonomics Society 60th Annual Meeting. 

Lee, J., Munoz, M., Fridman, L., Victor, T., Reimer, B., and Mehler, B. (Submitted). "Investigating drivers’ head and glance correspondence," PeerJ Computer Science. 

Fridman, L., Lee, J., Reimer, B., and Victor, T. (In press). "'Owl' and 'Lizard': Patterns of head pose and eye pose in driver gaze classification", IET Computer Vision. 

Lee, J., Reimer, B., Mehler, B., Angell, L., Seppelt B. D., and Coughlin J. F. (2015). "Analyses of glance patterns of older and younger drivers during a visual-manual HMI interaction," In Proceedings of Transportation Research Board 94th Annual Meeting, No. 15-4781. 

Contact Information:
77 Massachusetts Ave.
E40-215
Cambridge
MA
02139
319-471-3898