

DRIVING SAFETY RESEARCH INSTITUTE

Assessing driver monitoring systems
Elevating the Fundamentals
The bounce pass—a fundamental skill in basketball that every player must master to succeed. The recent success and attention garnered by Caitlin Clark and our women’s basketball team this past year highlight the importance of mastering the basics, a principle that also holds true in our work at the DSRI: that we take the fundamentals and elevate them to state-of-the-art levels.
At DSRI, we’ve applied this approach to automated driving, successfully collecting data on unmarked and gravel roads. This achievement was made possible by assembling an expert team of our talented staff and students, in collaboration with key partners like Hexagon | AutonomouStuff and Mandli Communications, among others. While automated driving represents the future of transportation, we must continue to remind the public that driver attention remains a fundamental factor in crash causation.
Crashes are complex, involving multiple factors, but attention-related issues—such as distraction, sleepiness, and impairment from alcohol or drugs—are measurable and predictable for each
driver. These issues are core areas of interest for us. By characterizing and predicting individual driver performance, we aim to teach machine learning models to recognize inattention and take appropriate actions, such as alerting the driver or intervening with steering or braking when necessary. This year, we’ve focused on advancing sophisticated next-generation driver monitoring systems by refining these models through data and parameter adjustments to minimize errors and improve predictions.
In the pages ahead, you’ll see how the fundamentals evolve into state-of-the-art innovations. Our team is composed of champions who are driving safety to new levels, ultimately saving lives on our roads.
Daniel V. McGehee Director, Driving Safety Research Institute Professor
Industrial
& Systems Engineering Emergency Medicine Public Health














DRIVING SAFETY RESEARCH INSTITUTE
Mission
To make our roads safer by researching the connection between humans and vehicles
Research Expertise
We conduct research with simulators and on-road vehicles. Funded by government and industry partners, our expertise includes:
• Human factors
• Distracted driving
• Drowsy driving
• Drugged driving
Our Simulators
• Connected and automated vehicles
• At-risk populations (older and novice drivers)
• Safety and crash data analysis
• Simulation science and digital twins

National Advanced Driving Simulator (NADS-1): One of the world’s largest and most realistic driving simulators


Instrumented On-Road Vehicles




NADS-2: A full cab miniSim with 135 degrees field of view, motion base, digital side mirrors, and now connected virtually to the NADS-1
miniSim™: A customizable PC-based portable simulator available for purchase (see page 24)
Toyota Camry XLE
Volvo XC90
Tesla Model S75D
Ford Transit shuttle bus
STUDENTS ACROSS THE COUNTRY
We work with talented high school, undergraduate, graduate, and post-doctoral students from various institutions across the country. As a research university, part of our mission is to educate and mentor students in all phases of their academic careers. They bring fresh perspectives and invaluable insights to our work, while their experiences help prepare them for their future careers. In short, they help us get the job done.
Here are a few examples of ways we’ve worked with a variety of students in 2024.
K–12 students

We regularly give tours to groups of local junior high and high school students, such as the one pictured here.
Andrew Chen, summer high school intern, worked with human models and figured out how to animate pedestrians in Unreal Engine (not pictured).

The FIRST Tech Challenge, shown here, is a high school state robotics competition held in our area each year. Chris Schwarz, our director of engineering and modeling research, is a volunteer judge.
Undergraduate students

Grant Ernst and Kenneth Reichert, both mechanical engineering majors, University of Iowa
Ernst (pictured here) and Reichert both assisted in the assembly of miniSims. Ernst is shown working on a minSim cab for the University of California, Irvine. He helped with the build, installed the braking system, and made various electrical connections.

Matthew Wu, electrical engineering major, University of Alabama
Wu converted virtual vehicle models to Unreal Engine, a game engine that provides more trueto-life renderings of our virtual environments. He updated wheels, vehicle lights, and surface reflections for more realistic simulations.

Faythe
Evans, clinical psychology major, University of Iowa
Evans, shown here with staff design engineer Alec La Velle, worked on building and troubleshooting scenarios for the simulator and learned about our research process. In our scenario development tool, she learned to create merges onto an interstate, lane changes, and changes in speed limits.
“I’m getting a lot more familiar with research working here,” Evans said. “I’m gaining experience working with data, with variables ... and just learning the research process will definitely help me after graduation and allow me to apply that knowledge to other roles.”

Annika Veit, biology major, Luther College
Veit researched advanced driver assistance systems on specific makes and models, looking up safety feature warnings, blind spot warnings, collision mitigation, human machine interfaces (HMIs), etc.
“ ”
“This experience has significantly enhanced my prospects for a career in machine learning or data science, and it has broadened my perspective on the practical applications of my technical skills in a real-world research environment.”
—Irene
Agusti, Grinnell College

Irene Agusti, mathematics major; Brad Carlton, mathematics and economics major; and Minh Nguyen, computer science major, Grinnell College
A small group of Grinnell College students used machine learning (ML) models on data from some of DSRI’s cannabis and alcohol impaired driving studies. They were mentored by Grinnell assistant professor Ryan Miller and DSRI’s director of drugged driving research Tim Brown.
“Being able to partner with DSRI is tremendously valuable to have that data for our students to work with,” said Miller. “Many of our students are aspiring to get jobs where they need to show that they’ve worked with large, complex data sets.”
In their first project, the students aimed to assess ML models meant to identify drivers who were impaired based on vehicle behavior, focusing on determining which factors are important in
identifying impairment such as the type of input (lateral position, speed, etc.), method of summarization (mean, range, rolling range, etc.), and measurement duration.
In their second project, they looked at data augmentation approaches to improve the training of ML models meant to identify impaired drivers. By overcoming the challenges associated with a lack of labeled impaired driving data, they were able to improve the performance of these models on new, unseen data.
The students themselves coded, ran experiments on PCs, analyzed data, discussed results, planned next steps, and assisted with decision-making and writing.
Graduate students
Jimin (Joy) Kim, University of Iowa
Kim completed her PhD from the University of Iowa Department of Industrial and Systems Engineering in the spring of 2024. She is shown here with her advisor and DSRI director Dan McGehee.
Her main research focused on the safety concerns surrounding the transfer of learning and knowledge gaps around new vehicle technologies.
She is now a postdoctoral research associate at the University of Wisconsin-Madison, working on enhancing drivers’ mode awareness while using driving automation.
Haolan (Lan) Zheng, PhD student, University of Florida
Zheng is working with DSRI researcher Justin Mason and exploring the effects of training on drivers’ understanding and use of adaptive cruise control (ACC).
He has taken a systematic approach to developing and providing training to drivers to better understand the ingredients included in the training material and thus better understanding the effects of training on ACC use.

ASSESSING DRIVER MONITORING SYSTEMS
A new generation of driver monitoring systems (DMS) are being developed to identify when drivers are drowsy, distracted, and/or under the influence of alcohol or cannabis. DSRI researchers have been busy assessing these systems across multiple projects.


Alcohol DMS
Sponsored by the National Highway Traffic Safety Administration (NHTSA)
Researchers are analyzing the effectiveness of driver monitoring systems on their ability to detect if someone is driving while drunk—specifically, if they are over the legal limit or not.
“It’s important to differentiate between drivers who are drunk and drivers who are drowsy or otherwise impaired,” explained Tim Brown, director of drugged driving research at DSRI, “and figuring out which measures are most sensitive in differentiating the type of impairment.”
The study will have participants drive in a driving simulator at four distinct times: 1) when they are alert and sober, 2) when they are drowsy and sober, 3) when their BAC is at 0.08, and 4) again when their BAC is at 0.12. Investigators will collect data on driver performance, eye tracking, head and body movements, heart rate, and respiration, among others. The pilot data collection started in November of 2024.
Distraction and Drowsiness DMS
Sponsored by NHTSA, with partner Westat
The study aims to use DMS to detect if a driver is distracted and identify when this occurs alongside drowsiness. This research will help differentiate between distraction and drowsiness, enabling researchers to understand the effects of distraction with and without the presence of drowsiness. Pilot data collection was in late 2024 in the NADS-1, with primary data collection to occur in 2025.
Greg Wagner, director of instrumentation engineering at DSRI, installs a new DMS in the NADS-1 cab
Drowsiness DMS with Exponent
Sponsored by NHTSA, with partner Exponent
The team is assisting NHTSA by conducting research to provide recommendations for test procedures to evaluate DMS systems that are designed to detect drowsiness. In collaboration with Exponent, they developed a protocol to gather data on drowsy drivers and are evaluating it through DSRI’s driving simulator and Exponent’s test track.
Findings to report
Seeing Machines DMS for cannabis
Sponsored by the Institute for Cannabis Research, with partners Seeing Machines, University of Colorado Anschutz Medical Campus, and Swinburne University
Researchers used a Seeing Machines DMS to study changes in eye behaviors after cannabis use. They looked at what measures allow us to identify who is acutely under the influence of cannabis and whether signals of acute use are consistent between occasional and frequent users.
Findings showed that there were changes in scanning patterns of cannabis users before and after the consumption of cannabis. Additionally, changes in average eye opening were found to decrease following use. Final manuscripts are being written as of December 2024.

Alcohol Detection with IIHS
Findings to report
Sponsored by the Insurance Institute for Highway Safety (IIHS), with partner Seeing Machines
What measures from a DMS are predictive of alcohol impairment? Researchers examined different eye-related measures (e.g., blink rate, pupil size) to assess: 1) if the driver recently used alcohol, and 2) if these measures are consistent across users relative to BAC levels.
Findings showed that median eye opening and percent of time spent focused on the forward roadway could be used as predictors of alcohol-impaired driving. The final report will be available in early 2025.
Tim Brown, director of drugged driving research

Findings to report
Aisin DMS
The DSRI team detected and analyzed impairment from alcohol and cannabis using a DMS provided by Aisin, a global automotive supplier. The Aisin DMS was installed on one of DSRI’s quarter cab miniSims, and the team used face and eye data from the DMS as well as vehiclebased measures for identifying impairment. The analysis was completed in September, with a final report and publication in progress, led by Chris Schwarz, director of engineering and modeling research.
The findings show that an alcohol model that used vehicle and DMS face features was effective at classifying alcohol impairment. A cannabis model that used mostly eye features also had good performance, but they suspect it to be a brittle result that will not generalize well to new data.

Chris Schwarz, director of engineering and modeling research
Overall, camera-based DMS devices hold promise for detecting different types of impairment that lead to lapses in attention and possible crashes. “The most promising path forward,” Schwarz said, “is to find ways to integrate DMS data with other types of signals from the vehicle and other sensors to provide the most holistic picture possible of driver state.”

Rose Schmitt (right), research coordination specialist, demonstrates the use of a breathalyzer as used in alcohol-related driving studies.
So which sensors are the most effective?
DSRI has also created a uniform data set from six past DMS studies that can be built upon and applied to future studies. In the analysis of these six studies, the team looked at which sensors were the most useful in detecting and differentiating types of impairment.
“Vehicle-based sensors become much less useful as soon as you turn on automation because at that point you stop getting any information about the driver,” explained Schwarz. “So these newer measures like gaze location become more valuable as a measurement tool.” Camera-based DMS are becoming more common in vehicles without the presence of automation; even a car being driven manually may have a camera-based DMS looking for signs of impairment.



Face and eye landmark points, collected from an open-source tool called OpenFace, are shown on the left. Landmark points can be used to detect expressions and emotion. For example, yawning is an indicator for drowsiness. Testing for broken symmetry in these points can indicate anomalies caused by touching the face or other occlusions.
OTHER DRUGGED DRIVING STUDIES
Drunk Driving Telltale
Sponsored by NHTSA, with partner Westat
Researchers are doing a survey followed by two phases of testing with an in-person simulator drive where some participants will be dosed with alcohol and others will not. A telltale light will display, meant to indicate to the driver that they are too impaired to be driving. The study is meant to test understanding and interpretations of the initial icon set, then narrow to a final icon used for the telltale. Example icons are shown below.
ADAS PROJECTS
Our investigators have continued their focus on a series of studies analyzing advanced driver assistance systems (ADAS) technology and consumer understanding of ADAS features.
ADAS Education and Outreach
Behavioral Traffic Safety Cooperative Research Program (BTSCRP)
Researchers completed Phase 1 this year, which involved reviewing and classifying ADAS education materials, standards, and literature. “This wasn’t the typical literature review. We focused on identifying how this material is used in the real world,” explained Justin Mason, assistant research scientist.
Now in Phase 2, the team is developing a guide to assist practitioners such as DOT and DMV personnel to provide training about ADAS. This guide will present key considerations for practitioners as well as strategies to identify, evaluate, and modify educational materials to fit their intended purpose.
The team is completing case studies using adaptive cruise control (ACC) to illustrate how the guidance can be applied. “The ACC case studies can be easily modified in the future to fit other ADAS and several driving populations,” added Mason.
Beyond Original Owners
Sponsored by the AAA Foundation for Traffic Safety
In a project titled, “ADAS and Automation Safety Considerations Beyond the Original Purchaser,” the research team will explore drivers’ understanding of vehicle technology among those who have purchased new vehicles and those who have not (i.e., ADAS users who have purchased used vehicles or have rented or borrowed one).
The team is developing a survey to identify potential gaps in ADAS understanding and experience among these consumer groups and to better understand how learning methods differ between them.

for various
projects

year.
DSRI researchers scour owner’s manuals
ADAS-related
this
Left to right: Cher Carney, Justin Mason, Michelle Reyes, and Cheryl Roe

Findings to report
ADAS involvement in near-crashes, crashes, and crash investigations
Sponsored by the Iowa Department of Transportation (DOT) and Colorado DOT
Findings and recommendations were presented this past fall from a recent study involving interviews and surveys with motorists and officers. The motorists were involved in crashes or near-crash situations while driving a vehicle with ADAS, and officers were asked how they consider ADAS when they complete crash reports.
Motorists: The findings showed that while some motorists are well-informed, many misunderstand or over-rely on their ADAS features. Some motorists conflate functions performed by different ADAS; for example, distance alerts and forward collision warnings were often described as being part of adaptive cruise control. Information and training on ADAS from dealership sales staff also varies widely.
Officers: Like motorists, officers have a wide range of ADAS understanding. Many officers believe ADAS can only be considered in a crash investigation if data can be retrieved from a vehicle’s event data recorder. Eighty percent of officers said they want more training on ADAS to know what questions to ask in their crash investigations.
OEMs: Recommendations for manufacturers include making it easier for motorists to identify the exact features on their vehicle by allowing them to enter their VIN online – since owner’s manuals are not vehicle-specific.
“It’s critical that consumers and officers are better educated about these systems,” said Michelle Reyes, senior research associate. “We’ve seen that if drivers don’t understand the ADAS on their vehicle, it can contribute to crashes and near-crashes.”
See more findings and recommendations in the project webinar at: bit.ly/3Zu8tiW

Toyota Consumer Education
In a multi-year project with the Toyota Collaborative Safety Research Center, researchers have completed Parts 1 and 2, where they assessed consumer education content for ADAS understanding after over-the-air updates.
Now, in Part 3, they will look at consumer education over time, including where, how, and when drivers should get educated. Study participants will attend multiple visits with simulator drives and will be given consumer education at different points.
“We’re helping Toyota understand where the good touch points are for drivers who have purchased a new vehicle. How can they shape new-driver training, and when would be the best time to provide that training?” explained John Gaspar, director of human factors research at DSRI.

John Gaspar, director of human factors research
ELECTRIC VEHICLES I
Human Factors Issues with EVs
Sponsored by NHTSA, with lead Westat NHTSA has identified potential human factors issues with the widespread deployment of electric vehicles (EVs).
Collaborating with Westat, DSRI conducted extensive research to identify and prioritize these and other potential human factors issues involving EVs. This included literature reviews, interviews (with OEMs, rescue responders, owners, and technicians), analyzing public discourse on social media, and crash data analysis.
The DSRI team completed a research plan and pilot study in 2024, which included comparing foot behavior and pedal interaction while driving an EV vs ICE vehicle. The final report is in progress.

CONNECTED SIMULATION I
Sponsored by NHTSA
The past year saw more behind-the-scenes work to connect the NADS-1 and NADS-2 simulators, allowing two drivers in different simulators to interact in the same virtual environment.
This will support a study titled Human Interactions with Driving Automation Systems, looking at roadway interactions between human-driven vehicles with no automation and varying levels of automated vehicles.
The major development this year is the integration of the Unreal Engine for more realistic graphics rendering. This upgrade involves setting up and configuring the engine for warping and blending the simulation scene across multiple screens, with contributions from Pixela Labs.
Other visual enhancements include more realistic surfaces, trees, shadows, brake lights, and other lighting effects. Simulated vehicles have also improved, with some new vehicle models added (see page 25). To handle the increased computational load, more powerful graphics cards have also been installed.
Additionally, new digital side mirrors on the NADS-2 will provide a more immersive experience by digitally displaying the view in the side mirrors.

A digital side mirror is shown here (mid-installation) in the NADS-2 simulator.
Two drivers . . .


in one simulation

Projects in Planning
Additional NHTSA-sponsored research topics awarded in 2024 include:
• Assessing intelligent speed assist
• Reanalysis of lane departure data
• Distraction and secondary devices: the use of cell phones versus in-vehicle systems
• Determine feasibility of alternative tests for the Standardized Field Sobriety Test (SFST)
NADS-2 simulator
National Advanced Driving Simulator (NADS-1)
How parental instruction impacts hazard anticipation in teen drivers TEEN DRIVERS
While teens experience relatively few crashes during supervised learning, crash rates spike once they begin driving independently. “This trend suggests that while parents effectively teach functional driving skills, they may overlook teaching their teen to recognize or anticipate potential hazards,” said Elizabeth O’Neal, assistant professor in the University of Iowa College of Public Health.
A new study aims to address this gap by examining the impact of a parent-focused intervention designed to improve parental instruction on hazard anticipation.
Participants in the study will include parents and their teens, who will first complete an acclimation
drive in the NADS-2 simulator. The intervention group will complete a web-based training program at home, which includes watching simulated hazards and learning how to discuss these with their teens. Both the control and intervention groups will also receive dash cams to record four drives in their own vehicle. Both groups will then participate in a hazard-laden drive in the NADS-2 simulator.
Researchers will analyze differences in parental communication and teen responses, aiming to enhance the effectiveness of parental guidance in teaching hazard anticipation and ultimately reduce teen crash rates.

Elizabeth O’Neal (passenger seat) and research assistant Giuliana Rokke-Smith
RURAL AUTOMATION

Public Dataset Available for Analysis
Sponsored by the U.S. Department of Transportation
The Automated Driving Systems (ADS) for Rural America project collected data on the unique needs of operating a high-speed ADS vehicle in rural environments, while working toward solutions that improve safety and mobility. Our ADS Transit vehicle was driven and tested under automation on rural roadways at speeds up to 65 mph, on various road types (including gravel and unmarked), as well as various lighting and weather conditions. Opportunities for future research on this topic are being actively explored.
The data is publicly available for analysis at our data portal, linked below. Raw data has been organized for ease of use and can be filtered using various criteria. Types of available data include:
• Automation performance data
• Weather and road conditions
• Interactive map of every drive
• Raw video data


• Questionnaire data from the riders regarding trust and acceptance
• Physiological data from riders and safety driver regarding stress/anxiety
MINISIM NEW FEATURES
NADS-SC.UIOWA.EDU/MINISIM DSRI-MINISIM@UIOWA.EDU
Connected simulation


The same technology we’ve used to virtually connect our NADS-1 and NADS-2 simulators is now available to connect multiple miniSims, allowing multiple miniSim drivers to interact in the same simulation.
Pictured left: This is one of three miniSims that will be virtually connected at North Carolina State University. Five screens wrap around the driver in this build pictured at DSRI prior to its installation at NC State.
Instrument panel customization
By leveraging Cascading Style Sheets in HTML, the display elements can be customized using typical web development tools.
Users can design their own data displays such as gauges, numerical readouts, and icons, and change font, size, and color. The architecture uses a lightweight web server written in Node.js to interface with miniSim, and it launches automatically on the dashboard display.

Integration of Unreal Engine

Enhanced rendering capabilities using Unreal Engine will be available to miniSim users in 2025. Unreal Engine is a leading-edge 3D graphics game engine that provides miniSim with more realistic rendering of the driving environment. Enhancements include reflections, highlights, and shadows, along with improvements to particulate effects such as rain, snow, fog, and smoke, and improved detail on vehicles, pedestrians, and trees. Utilization of Unreal’s capabilities may require computer upgrades due to the increased processing requirements.
Throughout 2025, more elements in our virtual environments will continue to be converted into Unreal format. This change also opens the door to a smooth workflow to bring in real-world road network data using commercially available tools.



New virtual models

New models for work zone simulations and other signage are now available to add to miniSim environments.

Human flaggers with SLOW and STOP signs

“AWARE” automated work zone assist device, used at flagger stations for safety, produced by Oldcastle Materials



European Union speed limit signage

The auto flagger will move from the STOP sign to the SLOW sign while the arm moves up or down accordingly.
Installation highlight: American University of Sharjah, UAE

This half-cab miniSim was installed at the America University of Sharjah, United Arab Emirates, in May 2024. This half-cab system features a 2019 Hyundai Sonata cab, a 166 degree curved screen with projector autoblending and warping, and a rear screen for the rearview mirrors. The projection system is mounted to a floor-standing gantry. It is also equipped with DSRI’s infotainment system in the OEM location.







EDUCATING FOR SAFETY
After 10 years of active research and outreach, SAFER-SIM came to a conclusion in 2024. The team held a series of 14 webinars in the summer of 2024, each summarizing the most recent research projects funded by SAFER-SIM.
SAFER-SIM is a grant-funded Tier 1 University Transportation Center that has been led by the University of Iowa, with partners University of Massachusetts-Amherst, University of Central Florida, University of Wisconsin-Madison, and University of Puerto Rico-Mayaguez.
Find all the SAFER-SIM webinars at dsri.uiowa.edu/safer-sim
Recent webinar topics include:
• Pedestrian, bicyclist, and e-scooter interactions with drivers

• Driver interactions with and trust in automated vehicle technologies
• Pedestrian crossing behavior
• Remote and virtual air traffic control
• Other related topics

10 Years of...

Research by:
117 students
58 principal investigators
22 industry collaborators

Projects: 13 symposia hosted
151 total projects
30 collaborative projects across sites

STEM Outreach:
297 total STEM events
17,554 K-12 students reached
Webinar slide by Pei Li of the University of Wisconsin-Madison on quantifying AV pedestrian interactions at intersections

Our 2022 DSRI Annual Report was recognized by the 30th Annual Communicator Awards by the Academy of Interactive & Visual Arts. It was recognized as
• Gold/Excellence in the “Annual Report - Educational Institution” category and
• Silver/Distinction in the “Design Features - Overall Design” category.
View the full winners’ gallery at bit.ly/3ZeUrBs.
The Communicator Awards is the leading global awards program honoring creative excellence for marketing and communications professionals. The 30th Annual Communicator Awards received over 3,000 entries and is an annual competition recognizing the best in advertising, corporate communications, public relations, and design.



OUR PARTNERS
DSRI Advisory Board Members
Linda Angell President and Principal Scientist Touchstone Evaluations, Inc.
Stacy Balk National Highway Traffic Safety Administration (NHTSA)
Tom Banta Vice President, Director Strategic Growth Iowa City Area Development Group
Pujitha Gunaratne Senior Executive Engineer Toyota Collaborative Safety Research Center
Terry Johnson Chief Financial Officer and Treasurer University of Iowa
Gary Kay President
Cognitive Research Corporation
Scott Marler Director Iowa Department of Transportation
Brian Philips Senior Research Psychologist NHTSA, Turner-Fairbank Highway Research Center
Ann Ricketts
Division of Sponsored Programs University of Iowa
Trent Victor Director of Safety Waymo
C.Y. David Yang Executive Director AAA Foundation for Traffic Safety
University of Iowa Faculty Partners
Ned Bowden College of Liberal Arts and Sciences Chemistry
Carri Casteel College of Public Health Injury Prevention Research Center
Alejandro Comellas Freymond Carver College of Medicine Internal Medicine
Soura Dasgupta College of Engineering Electrical and Computer Engineering
Jeffrey Dawson College of Public Health Biostatistics
Gary Gaffney Carver College of Medicine Psychiatry
Milena A. Gebska Carver College of Medicine Cardiovascular Medicine
Amanda Haes College of Liberal Arts and Sciences Chemistry
Cara Hamann College of Public Health, Epidemiology Injury Prevention Research Center
Loreen Herwaldt Carver College of Medicine Internal Medicine
Karin Hoth Carver College of Medicine Psychiatry
Gary Milavetz College of Pharmacy Pharmacy Practice and Science
Nicholas Mohr Carver College of Medicine Emergency Medicine
Elizabeth O’Neal College of Public Health Community and Behavioral Health
Jodie Plumert College of Liberal Arts and Sciences Psychological and Brain Sciences
Thomas Schnell College of Engineering Industrial and Systems Engineering
Steven Spears Graduate College School of Planning and Public Affairs
Deema Totah College of Engineering Mechanical Engineering
Ergun Uc Carver College of Medicine Neurology
Shaun Vecera College of Liberal Arts and Sciences Psychological and Brain Sciences
Chao Wang College of Engineering Industrial and Systems Engineering
Mark Wilkinson Carver College of Medicine Ophthalmology
External Faculty Partners
Grinnell College
Ryan Miller
New York University
Linda Ng Boyle
Oregon State University
David Hurwitz
University of California, Irvine
Federico Vaca
University of Central Florida
Mohamed Abdel-Aty
Naveen Eluru
Zhaomiao (Walter) Guo
Samiul Hasan
Amr Oloufa
Omer Tatari
Yina Wu
Lishengsa Yue
Mohamed Zaki
University of Colorado Anschutz
Medical Campus
Ashley Brooks-Russell
Michael Kosnett
University of Leeds
Natasha Merat
Richard Romano
University of Massachusetts–Amherst
Chengbo Ai
Eleni Christofa
Cole Fitzpatrick
Michael Knodler
Anuj Pradhan
Shannon Roberts
University of Puerto Rico–Mayagüez
Carla López
Alberto M. Figueroa Medina
Benjamin Colucci-Rios
Didier Valdés
University of Wisconsin–Madison
Madhav Chitturi
John D. Lee
Dan Negrut
David Noyce
Jon Riehl
Kelvin R. Santiago
Radu Serban
Volpe National Transportation Systems
Center
Donald Fisher
Yale University
Barbara Banz
Additional External Partners and Sponsors
AAA Foundation for Traffic Safety
Acclaro Research Solutions, Inc.
Advanced Brain Monitoring
Aisin Technical Center of America, Inc.
American University of Sharjah
A.T. Still University of Health Sciences
Battelle Memorial Institute
Behavioral Traffic Safety Cooperative
Research Program
Booz Allen Hamilton, Inc.
Charles River Associates
Cognitive Research Corporation
Colorado Department of Public Health and Environment
Colorado Department of Transportation
Colorado State University
Dunlap and Associates, Inc.
Exponent
Federal Law Enforcement Training Centers
Federal Transit Administration
Federal Highway Administration
Florida Gulf Coast University
General Motors Corporation
Georgia Institute of Technology
Hexagon | AutonomouStuff
Hyundai America Technical Center, Inc.
Iowa City Area Development Group
Iowa Department of Transportation
Iowa Governor’s Traffic Safety Bureau
Iowa State University
ISBRG Corp.
Leidos, Inc.
Lenstec, Inc.
Loyola Marymount University
Mandli Communications
Marche Polytechnic University
Massachusetts Department of Transportation
Massachusetts Institute of Technology
MetroPlan Orlando
Michigan Technological University
National Highway Traffic Safety
Administration
National Institute for Occupational Safety and Health
National Institute on Drug Abuse
NORC at the University of Chicago
Oakland University
Office of the Assistant Secretary for Research and Technology
Purdue University
San Jose State University
State Farm
Swinburne University of Technology
Tongji University
toXcel
Toyota Collaborative Safety Research Center
University of California, Irvine
Transport Canada
University of Hartford
University of Kansas
University of New Hampshire
University of Toronto
University of Windsor
U.S. Department of Transportation
U.S. Department of Homeland Security
Veterans Affairs
Volpe National Transportation Systems Center
Wisconsin Department of Transportation
Workplace Learning Connection
Westat, Inc.
Driving Safety Research Institute
2401 Oakdale Boulevard
Iowa City, Iowa 52242 dsri.uiowa.edu engineering.uiowa.edu



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