EXPLORATORY ADVANCEDRESEARCH

Exploratory Advanced 

Research (EAR) Program

Compendium of Papers from Funded Research Projects

UNIVERSITY OF MICHIGAN

Researchers for this study developed improved video 

processing algorithms that can identify more complex 

behavior and actions of individuals (i.e., pedestrians, drivers, 

bicyclists) in various traffic situations. The research team 

wanted to build on existing video-processing algorithms that 

can identify basic behavior, “primitives” (such as when a 

subject’s hand is near their head), but cannot always identify 

a “high-level” behavior (such as talking on a cellphone). 

Specifically, the researchers aimed to:

• Use a bottom-up machine-learning approach to 

train an algorithm to recognize high-level behaviors 

using object detection, human-pose estimation, and 

behavior classification.

• Use a top-down approach to catalog primitive and 

high-level behaviors and develop a statistical prediction 

model to link them.

ACCEPTED FOR PUBLICATION AND PRESENTATIONS

1. Miao, H., S. Zhang, and C. Flannagan. 2022. “Driver 

Behavior Extraction From Videos in Naturalistic Driving 

Datasets with 3D ConvNets.” Journal of Big Data Analytics 

in Transportation 4, no. 1: 41–55.

2. Kanu-Asiegbu, A. M., R. Vasudevan, and X. Du. 2021. 

“Leveraging Trajectory Prediction for Pedestrian Video 

Anomaly Detection.” In IEEE Symposium Series on 

Computational Intelligence (SSCI), 1–8. Piscataway, NJ: 

IEEE. DOI: 10.1109/SSCI50451.2021.9660004, last 

accessed November 4, 2022. 

AUTOMATED VIDEO PROCESSING ALGORITHMS TO 

DETECT AND CLASSIFY HIGH-LEVEL BEHAVIORS

Illustration of the general cascade concept.

© University of Michigan Transportation Research Institute.

USING BEHAVIORAL ECONOMICS TO BETTER 

UNDERSTAND MANAGED LANE CHOICE

TEXAS A&M UNIVERSITY 

Behavioral economics draws on insights from psychology 

and economics to better understand human decisionmaking. 

Understanding cognitive biases and heuristics—such as loss 

aversion and present bias—may help policymakers predict 

human behavior and facilitate choices that support public 

benefits. Behavioral economists call these efforts to shape 

decisionmaking nudges—subtle tweaks to an environment 

that may influence human behavior. When applied to 

transportation studies, a behavioral economics framework 

may enable the development of more accurate models for 

predicting travelers’ choices on the road and may inform 

policy and transportation planning decisions. Researchers 

for the project “Using Behavioral Economics to Better 

Understand Managed Lane Choice” are evaluating how 

behavioral economics could improve travel demand models.

ACCEPTED FOR PUBLICATION

1. Ashraf, S., M. Burris, A. Brown, V. Vitaku. 2022. 

“Using Behavioral Economics to Identify Potential 

Managed Lane Users.” Transportation Research 

Record 2676, no. 8: 144–158.

Traffic modeling relies on data from highways such as the 

Lyndon B Johnson Freeway in Dallas, TX. 

© Texas A&M Transportation Institute.

Behavioral Economics 

for Managed Lanes

Computer Vision

Behavioral Economics for Managed Lanes & Computer Vision
EXPLORATORY ADVANCED RESEARCH

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Compendium of Papers from Funded Research Projects

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3. Yao, Y., E. Atkins, M. Johnson-Roberson, R. Vasudevan, 

and X Du. 2021. “BiTrap: Bi-Directional Pedestrian 

Trajectory Prediction with Multi-Modal Goal Estimation.” 

IEEE Robotics and Automation Letters 6, no. 2: 1463–

1470. https://doi.org/10.48550/arXiv.2007.14558, last 

accessed November 4, 2022.

4. Yao, Y., E. Atkins, M. J. Roberson, R. Vasudevan, and X. 

Du. 2021. “Coupling Intent and Action for Pedestrian 

Crossing Behavior Prediction.” In Proceedings of the 

Thirtieth International Joint Conference on Artificial 

Intelligence (IJCAI). Montreal, ON: IJCAI. 1238–1244. 

https://doi.org/10.24963/ijcai.2021/171, last accessed 

November 4, 2022.

5. Du, X., R. Vasudevan, and M. Johnson-Roberson. 2020. 

“Unsupervised Pedestrian Pose Prediction: A Deep 

Predictive Coding Network-Based Approach for 

Autonomous Vehicle Perception.” IEEE Robotics and 

Automation Magazine 27, no. 2: 129–138. https://

ieeexplore.ieee.org/document/9042808, last accessed 

November 4, 2022. 

6. He, W., M. R. Anderson, M. Strome, and M. Cafarella. 

2020. “A Method for Optimizing Opaque Filter Queries.” In 

Proceedings of the 2020 ACM SIGMOD International 

Conference on Management of Data. New York, NY: 

Association for Computing Machinery. 1257–1272. https://

dl.acm.org/doi/10.1145/3318464.3389766, last accessed 

November 4, 2022.

DEEP INSIGHT: DEEP EXTRACTION OF DRIVER 

STATE FROM NATURALISTIC DRIVING DATASET

IOWA STATE UNIVERSITY, SYRACUSE UNIVERSITY, 

UNIVERSITY OF MISSOURI, AND UNIVERSITY OF NEBRASKA 

MEDICAL CENTER

For the project “Deep InSight: Deep Extraction of Driver 

State from Naturalistic Driving Dataset,” researchers 

designed a driver-state estimation platform to improve the 

capacity to analyze large datasets related to human driving 

behaviors. This Deep InSight platform incorporated recurrent 

neural network (RNN) models trained to automatically detect 

and estimate human behaviors and deal with detection 

challenges, such as extreme-angle face detection when a 

driver is looking to the side or down. These RNNs are ideal 

for applications that involve complex interactions and input 

from multiple sensors, crucial for automated evaluation of 

driver state. Evaluating driver state requires tracking 

combinations of cues over many frames—from multiple 

camera views—and merging those with vehicle sensor data 

over time. The platform also makes it easier for researchers 

to manually check those automated annotations and verify 

the model’s performance.

Deep InSight platform design including tools and stages. 

© REACTOR Lab.

ACCEPTED FOR PUBLICATION AND PRESENTATIONS

1. Naphade, M., S. Wang, D. C. Anastasiu, Z. Tang, M-C. 

Chang, Y. Yao, L. Zheng, M. S. Rahman, A. 

Venkatachalapathy, A. Sharma, Q. Feng, et al. 2022. “The 

6th AI City Challenge.” 2022 IEEE/CVF Conference on 

Computer Vision and Pattern Recognition Workshops 

(CVPRW). https://doi.org/10.48550/arXiv.2204.10380, last 

accessed November 7, 2022. 

Computer Vision
EXPLORATORY ADVANCED RESEARCH

EAR Program

Compendium of Papers from Funded Research Projects

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2. Venkatachalapathy, A., A. Raj, A. Sharma, and J. Merickel. 

2021. “Implementing a Comprehensive Cloud-Based

Platform for Naturalistic Driving Studies.” 6th International 

Symposium on Future Active Safety Technology Toward 

Zero Traffic Accidents (FASTzero ’21). Kanazawa, Japan: 

Society of Automotive Engineers of Japan. https://www.

researchgate.net/publication/355972350_Implementing_a_

comprehensive_cloud-based_platform_for_naturalistic_

driving_studies, last accessed November 7, 2022.

3. Wang, J., W. Chai, S. Velipasalar, A. Venkatachalapathy, K. 

L. Tan, A. Haghighat, Y. Adu-Gyamfi, and A. Sharma. 2021. 

“A Survey of Driver Behavior Analysis From In-Vehicle 

Cameras.” accepted for publication IEEE Transactions on 

Intelligent Transportation Systems.

SUBMITTED FOR PUBLICATION

1. Chai, W., J. Chen, J. Wang, S. Velipasalar, A. 

Venkatachalapathy, Y. Adu-Gyamfi, J. Merickel, and A. 

Sharma “Driver Head Pose Detection From Naturalistic 

Driving Data.” submitted to IEEE Transactions on 

Intelligent Transportation Systems.

2. Aboah, A., Y. Adu-Gyamfi, S. V. Gursoy, J. Merickel, M. 

Rizzo, and A. Sharma. “Driver Maneuver Detection and 

Analysis Using Time Series Segmentation and 

Classification.” Submitted to Journal of Transportation 

Engineering, Part A: Systems.

SECOND STRATEGIC HIGHWAY RESEARCH 

PROGRAM (SHRP 2) NATURALISTIC DRIVING 

STUDY (NDS) VIDEO ANALYTICS RESEARCH

VOLPE NATIONAL TRANSPORTATION SYSTEMS CENTER

A Volpe National Transportation Systems Center research 

team developed a video-processing tool that uses machine 

learning to train neural networks to identify and classify 

roadway features and driving conditions. In phase Ι of the 

project, the team focused on detecting and mapping work 

zone features, such as barrels, cones, and signs. During 

phase ΙΙ, the researchers trained neural networks to detect 

traffic signals and signal states as well as weather events 

and roadway weather conditions visible in the forwardfacing video stream from the SHRP 2 NDS dataset. Once 

extracted, these data can be added to the SHRP 2 NDS 

time series, which includes radar and Global Positioning 

System data, or the roadway information database. 

Including this information in those databases will enable 

safety researchers and other stakeholders—such as 

representatives from the insurance industry or vehicle 

manufacturers working on advanced transportation 

solutions like autonomous vehicle development—to more 

easily access items of interest in the databases. The 

algorithms developed in this project could also inform future 

projects focused on other unextracted data, such as 

detecting and counting lanes, mapping work zone structure, 

and identifying other roadway features relevant to 

transportation safety initiatives. The project furthers the 

original mission of the SHRP 2 NDS data—to find strategic 

solutions to enhance highway safety, reduce congestion, 

and improve roadway and bridge renewal—by making the 

datasets more accessible to end users working on 

innovating roadway safety.

ACCEPTED FOR PUBLICATION AND PRESENTATIONS

1. Abodo, F., R. Rittmuller, B. Sumner, and A. Berthaume. 

2018. “Detecting Work Zones in SHRP 2 NDS Videos 

Using Deep Learning Based Computer Vision.” 17th IEEE 

International Conference on Machine Learning and 

Applications (ICMLA). Orlando, FL: IEEE. 679–686. https://

ieeexplore.ieee.org/document/8614133, last accessed 

November 7, 2022.

2. Yi, H., C. Bizon, D. Borland, M. Watson, M. Satusky, R. 

Rittmuller, R. Radwan, R. Srinivasan, and A. 

Krishnamurthy. 2021. “AI Tool with Active Learning for 

Detection of Rural Roadside Safety Features.” 2021 IEEE 

International Conference on Big Data (Big Data). Orlando, 

FL: IEEE. 5317–5326. https://ieeexplore.ieee.org/

document/9671360/authors#authors, last accessed 

November 7, 2022.

RESEARCH STANDARDS AND TECHNICAL 

ASSESSMENT SUPPORT

OAK RIDGE NATIONAL LABORATORY

Researchers at Oak Ridge National Laboratory developed 

calibration and measurement techniques to help the broader 

community of researchers wanting to work with NDS data. 

The techniques they employed in the “Research Standards 

and Technical Assessment Support” study enabled 

benchmarking progress and technical assessment of EAR 

Program-sponsored research teams.(1)

 

ACCEPTED FOR PUBLICATION AND PRESENTATIONS

1. Aykac, D., T. Karnowski, R. Ferrell, and J. Goddard. 2020. 

“Detection and Characterization of Rumble Strips in 

Roadway Video Logs.” Electronic Imaging no. 6: 50-1–50-8. 

10.2352/ISSN.2470-1173.2020.6.IRIACV-050, last 

accessed November 7, 2022. 

Computer Vision
EXPLORATORY ADVANCED RESEARCH

EAR Program

Compendium of Papers from Funded Research Projects

4

Computer vision can annotate secondary behaviors inside 

the vehicle, extravehicular context, and interactions between 

the driver and the environment. 

© Virginia Tech Transportation Institute.

2. Wali, B., A. Khattak, and T. Karnowski. 2020. “The 

Relationship Between Driving Volatility in Time to Collision 

and Crash Injury Severity in a Naturalistic Driving 

Environment.” Analytic Methods in Accident Research 28, 

December: 100136. https://doi.org/10.1016/j.

amar.2020.100136, last accessed November 7, 2022.

3. Baragchizadeh, A., K. Orsten-Hooge, T. Karnowski, D. 

Bolme, R. Ferrell, P. Jesudasen, C. Castillo, and A. 

O’Toole. 2020. “Seeing Through De-Identified Faces in 

Videos by Humans and a Deep Convolutional Neural 

Network.” Journal of Vision 20, no. 11: 757. https://doi.

org/10.1167/jov.20.11.757, last accessed November 7, 

2022.

4. Orsten-Hooge, K., A. Baragchizadeh, T. Karnowski, D. 

Bolme, R. Ferrell, P. Jesudasen, C. Castillo, and A. 

O’Toole. 2020. “Evaluating Automated Face IdentityMasking Methods with Human Perception and a Deep 

Convolutional Neural Network.” ACM Transactions on 

Applied Perception 18, no. 1: 1–20. https://dl.acm.org/doi/

abs/10.1145/3422988, last accessed November 7, 2022.

5. Ferrell, R., D. Aykac, T. Karnowski, and N. Srinivas. 2021. 

A Publicly Available, Annotated Dataset for Naturalistic 

Driving Study and Computer Vision Algorithm 

Development. Report No. ORNL/TM-2019/1106. Oak 

Ridge, TN: Oak Ridge National Laboratory. https://info.ornl.

gov/sites/publications/Files/Pub122418.pdf, last accessed 

November 7, 2022.

6. Aykac, D., R. Ferrell, N. Srinivas, and T. Karnowski. 2021. 

“Methods and Comparisons Between Computer Vision and 

Radar Based Vehicle Location.” In IS&T International 

Symposium on Electronic Imaging. Springfield, VA: Society 

for Imaging Science and Technology. 334-1–334-6. https://

www.osti.gov/servlets/purl/1814334, last accessed 

November 7, 2022.

7. Khattak, Z. H., M. Fontaine, W. Li, A. Khattak, and T. 

Karnowski. 2021. “Investigating the Relation Between 

Instantaneous Driving Decisions and Safety Critical Events 

in Naturalistic Driving Environment.” Accident Analysis & 

Prevention 156: 106086. https://doi.org/10.1016/j.

aap.2021.106086, last accessed November 7, 2022.

8. Karnowski, T., D. Aykac, R. Ferrell, C. Gambrell, Z. 

Langford, and L. Torkelson. 2022. “Leveraging Gradient 

Weighted Class Activation Mapping to Improve 

Classification Effectiveness: Case Study in Transportation 

Infrastructure Characterization.” Symposium on Electronic 

Imaging 34, no. 6: 1–6. https://doi.org/10.2352/

EI.2022.34.6.IRIACV-275, last accessed November 7, 2022.

9. Liu, A., A. Boka, A. Baragchizadeh, C. Muthukumar, V. 

Huang, A. Sarup, R. Ferrell, G. Friedland, T. Karnowski, M. 

Lee, and A. O’Toole. 2021. “Driving Road Safety Forward: 

Video Data Privacy Task” presented at MediaEval 2021. 

Bergen, Norway: MediaEval. https://www.osti.gov/servlets/

purl/1879957, last accessed November 7, 2022.

VIDEO ANALYTICS FOR AUTOMATIC ANNOTATION 

OF DRIVER BEHAVIOR AND DRIVING SITUATIONS IN 

NATURALISTIC DRIVING DATA

VIRGINIA TECH TRANSPORTATION INSTITUTE

In this study, researchers at the Virginia Tech Transportation 

Institute worked on developing computer vision methods that 

would facilitate the automatic generation of annotations from 

the SHRP 2 NDS database using the continuous videos. The 

study aimed to make it easier for researchers to create smaller 

subsets of data subsets from the more than 1 million hours of 

video data that make up the SHRP 2 NDS database. The 

research team developed and evaluated a series of deep 

neural network models (including convolutional neural network 

and RNN) to capture the spatial and temporal information 

embedded in the video. Enhanced access to very large 

Computer Vision
EXPLORATORY ADVANCED RESEARCH

EAR Program

Compendium of Papers from Funded Research Projects

5

datasets with appropriate video annotations will facilitate a 

quantum leap forward in transportation safety research. Such 

work could make it possible to explore questions that are 

currently out of reach of investigators, including the 

interactions between human drivers and other road users, 

road infrastructure elements, and roadside objects.

ACCEPTED FOR PUBLICATION AND PRESENTATIONS

1. Sundharam, V., A. Sarkar, J. Hickman, and L. Abbott. 2022, 

Accepted. “Characterization, Detection, and Segmentation 

of Work Zone Scenes from Naturalistic Driving Data.” 

Transportation Research Record.

2. Winkowski, C., A. Sarkar, J. Hickman, and L. Abbott. 2022, 

Accepted. “Residual Network-Based Driver Gaze 

Classification in Naturalistic Driving Studies.” 

Transportation Research Record.

3. Papakis, I., A. Sarkar, A. Svetovidov, J. S. Hickman, and A. 

L. Abbott. 2021. “Convolutional Neural Network-Based 

In-Vehicle Occupant Detection and Classification Method 

Using Second Strategic Highway Research Program Cabin 

Images.” Transportation Research Record 2675, no. 8: 

443–457. https://doi.org/10.1177/03611981219986, last 

accessed November 7, 2022.

CONFERENCE PROCEEDINGS LEADING TO BOOK CHAPTERS

1. Sarkar, A. 2022. “A Comprehensive Safety Analysis for 

Gaze Fixation of Drivers to Outside Scene.” Presented at 

Human Factors in Transportation. AHFE (2022) 

International Conference. New York, NY. In Applied Human 

Factors and Ergonomics Open Access, vol 60. eds. Katie 

Plant and Gesa Praetorius. Applied Human Factors and 

Ergonomics International. http://doi.org/10.54941/

ahfe1002481, last accessed November 8, 2022.

2. Sarkar, A. 2022. “Visual Dictionary of Human Action in 

Vehicular Environment Using Computer Vision.” Presented 

at Human Factors in Transportation. AHFE (2022) 

International Conference. New York, NY. In Applied Human 

Factors and Ergonomics Open Access, vol 60. eds. Katie 

Plant and Gesa Praetorius. Applied Human Factors and 

Ergonomics International. http://doi.org/10.54941/

ahfe1002446, last accessed November 8, 2022. 

CONFERENCE PRESENTATIONS

1. Sarkar, A. 2022. “Visual Dictionary of Human Action in 

Vehicular Environment Using Computer Vision.” In 13th 

International Conference on Applied Human Factors and 

Ergonomics. New York, NY: Applied Human Factors and 

Ergonomics International.

2. Sarkar, A. 2022. “A Comprehensive Safety Analysis for 

Gaze Fixation of Drivers to Outside Scene.” In 13th 

International Conference on Applied Human Factors and 

Ergonomics (Accepted). New York, NY: Applied Human 

Factors and Ergonomics International.

3. Datta, D., A. Sarkar, C. Winkowski, L. Abbott, J. Hickman, 

M. Camden, and J. Sudweeks. 2022. “Detecting Gaze 

Location From Temporal Relations of Driver Face Pose 

Using Recurrent Neural Network.” Presented at 

Transportation Research Board 101st Annual Meeting. 

Washington, DC: Transportation Research Board.

4. Winkowski, C., A. Sarkar, A. Svetovidov, J. Hickman, and 

L. Abbott. 2022. “Residual Network-Based Driver Gaze 

Classification in Naturalistic Driving Studies.” Presented at 

Transportation Research Board 101st Annual Meeting. 

Washington DC: Transportation Research Board.

5. Sundharam, V., A. Sarkar, A. Svetovidov, J. Hickman, and 

L. Abbott. 2022. “Characterization, Detection, and 

Segmentation of Work Zone Scenes From Naturalistic 

Driving Data.” Presented at Transportation Research 

Board 101st Annual Meeting. Washington DC: 

Transportation Research Board.

6. Papakis, I., A. Sarkar, A. Svetovidov, J. Hichman, and L. 

Abbott. 2021. “A CNN-Based In-Vehicle Occupant 

Detection and Classification Method Using SHRP 2 Cabin 

Images.” Presented at Transportation Research Board 

100th Annual Meeting. Washington DC: Transportation 

Research Board.

Computer Vision
EXPLORATORY ADVANCED RESEARCH

EAR Program

Compendium of Papers from Funded Research Projects

6

PHYSICALLY INFORMED DATA-DRIVEN METHODS 

FOR GREATLY ENHANCING THE USE OF 

HETEROGENEOUS CEMENTITIOUS MATERIALS 

IN TRANSPORTATION INFRASTRUCTURE 

UNIVERSITY OF CALIFORNIA, LOS ANGELES

Researchers at the University of California, Los Angeles, are 

seeking to develop what they’re calling “new data-guided 

pathways” to help determine which grades of fly ashes, 

including reclaimed and off-spec fly ashes, can be used in 

the production of concrete for highway construction 

applications. The project aims to decipher, through a 

data-guided and machine-learning approach, how the 

physical and chemical features of fly ashes—the “genome” 

of the fly ash—control their performance in concrete. 

ACCEPTED FOR PUBLICATION

1. Young, B. A., A. Hall, L. Pilon, P. Gupta, and G. Sant. 2019. 

“Can the Compressive Strength of Concrete Be Estimated 

from Knowledge of the Mixture Proportions? New Insights 

from Statistical Analysis and Machine Learning Methods.” 

Cement and Concrete Research 115, January: 379–388. 

https://doi.org/10.1016/j.cemconres.2018.09.006, last 

accessed November 7, 2022.

2. Oey, T., S. Jones, J. W. Bullard, and G. Sant. 2020. 

“Machine Learning Can Predict Setting Behavior and 

Strength Evolution of Hydrating Cement Systems.” Journal 

of the American Ceramic Society 103, 480–490. https://doi.

org/10.1111/jace.16706, last accessed November 7, 2022.

Fly ash, a supplementary cementitious material (SCM), is an important constituent in the production of concrete. Although demand for SCMs is increasing, the amount of fly ash produced by coal-fired power 

plants is decreasing, so the transportation industry is looking for viable alternatives to traditional fly ash that

can provide reliable performance. These alternatives, which include nontraditional or off-specification fly ash 

as well as other SCM sources, are promising because of their abundance and potential economic value. 

Three projects supported through the EAR Program seek to study and document how the chemical and 

physical properties of these alternative materials affect the performance of concrete. These projects aim to 

provide State departments of transportation (DOT) with information that describes how these materials affect 

the durability, sustainability, and strength of concrete to help engineers make informed and timely decisions 

regarding material specifications and concrete mixture performance criteria.

A clipboard showing samples of fly ash before and after 

electrostatic separation to reduce the amount of carbon in 

fly ash. Source: FHWA.

3. Liu, H., Z. Fu, K. Yang, X. Xu, and M. Bauchy. 2019. 

“Machine Learning for Glass Science and Engineering: A 

Review.” Journal of Non-Crystalline Solids 557, 119419. 

https://doi.org/10.1016/j.jnoncrysol.2019.04.039, last 

accessed November 7, 2022.

4. Liu, H., Z. Fu, Y. Li, N. F. A. Sabri, and M. Bauchy. 2019. 

“Balance Between Accuracy and Simplicity in Empirical 

Forcefields for Glass Modeling: Insights From Machine 

Learning.” Journal of Non-Crystalline Solids 515, 133–142. 

https://doi.org/10.1016/j.jnoncrysol.2019.04.020, last 

accessed November 7, 2022.

Supplementary Materials

Supplementary Materials
EXPLORATORY ADVANCED RESEARCH

EAR Program

Compendium of Papers from Funded Research Projects

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NONTRADITIONAL AND NATURAL POZZOLANBASED SCMS OR INORGANIC POLYMERS FOR 

TRANSPORTATION INFRASTRUCTURE 

PURDUE UNIVERSITY

While fly ash is a dominant choice among SCMs, there are 

other types of materials that could also be used for concrete 

production. These other materials include nontraditional and 

natural pozzolans. Researchers at Purdue University, with 

assistance from researchers at Penn State University and 

Clarkson University, are seeking to analyze and conduct 

further studies on how these types of SCMs might perform in 

concrete pavements or other transportation structures. 

These nontraditional sources—calcinated clays, natural 

pozzolans, bottom ashes, and fluidized bed combustion 

ashes—are cost competitive and relatively abundant in 

different U.S. regions. Laboratory tests can create an 

understanding of how these resources could be used as 

viable alternatives to fly ash.

ACCEPTED FOR PUBLICATION

1. Yoon, J., K. Jafari, R. Tokpatayeva, S. Peethamparam, 

J. Olek, and F. Rajabipour. 2022. “Characterization and

Quantification of the Pozzolanic Reactivity of Natural

and Non-Conventional Pozzolans.” Cement and 

Concrete Composites 133, 104708. https://doi.

org/10.1016/j.cemconcomp.2022.104708, last accessed

November 8, 2022.

PERFORMANCE-BASED CLASSIFICATION 

METHODS FOR RECLAIMED FLY ASH

OKLAHOMA STATE UNIVERSITY

Much of the fly ash used in concrete production is of a 

certain specification grade. But there are landfills and 

impoundments of other, older fly ash that have been stored 

over the last 50 to 60 yr. It is that fly ash—called “reclaimed” 

fly ash—that researchers want to study to see if they provide 

viable alternatives to the approved fly ash sources currently 

in use. Researchers at Oklahoma State University, with help 

from the Georgia Institute of Technology, Ohio State 

University, and Diversified Engineering Services, seek to 

combine advanced material characterization methods, 

performance-based testing, mechanistic modeling, and 

machine learning to create engineering tools to classify 

reclaimed fly ash. Also involved in the project as 

collaborators are Boral Materials, the Minnesota Department 

of Transportation, the Oklahoma DOT, and Southern 

Company. The project involves taking existing performancebased tests, tweaking them to better analyze the reclaimed 

fly ash, and determining how it might perform when used for 

concrete production. The researchers want to capture the 

differences in chemical composition among the various 

reclaimed fly ashes and see how those differences can be 

measured in a way that describes how that fly ash performs in 

concrete. The team also expects to analyze the test data with 

machine-learning methodology to see what patterns emerge.

ACCEPTED FOR PUBLICATION

1. Kang, S., B. Boyd, and M. T. Ley. 2022. 

“Performance and Prediction of Non-traditional 

Coal-ash in Concrete with the Particle Model.” 

Construction and Building Materials 345: 128170. 

https://doi.org/10.1016/j.conbuildmat.2022.128170, 

last accessed November 7, 2022.

2. Kang, S., Z. Lloyd, A. Behravan, and M. T. Ley. 2021. “The 

Relationship Between the Apparent Diffusion Coefficient 

and Surface Resistivity of Fly Ash Concrete.” Construction 

and Building Materials 299: 123964. https://doi.

org/10.1016/j.conbuildmat.2021.123964, last accessed 

November 7, 2022.

3. Kang, S., M. T. Ley, and A. Behravan. 2021. 

“Predicting Ion Diffusion in Fly Ash Cement Paste 

Through Particle Analysis.” Construction and Building 

Materials 272: 121934. https://doi.org/10.1016/j.

conbuildmat.2020.121934, last accessed November 7, 

2022.

4. Kang, S., M. T. Ley, Z. Lloyd, and T. Kim. 2020. “Using 

the Particle Model to Predict Electrical Resistivity 

Performance of Fly Ash in Concrete.” Construction and 

Building Materials 261: 119975. https://doi.

org/10.1016/j.conbuildmat.2020.119975, last accessed 

November 7, 2022.

5. Kang, S., Z. Lloyd, T. Kim, and M. T. Ley. 2020. “Predicting 

the Compressive Strength of Fly Ash Concrete with the 

Particle Model.” Cement and Concrete Research 138: 

106218. https://doi.org/10.1016/j.cemconres.2020.106218, 

last accessed November 7, 2022.

6. Kim, T., M. T. Ley, S. Kang, J. M. Davis, S. Kim, and P. 

Amrollahi. 2020. “Using Particle Composition of Fly Ash to 

Predict Concrete Strength and Electrical Resistivity.” 

Cement and Concrete Composites 107: 103493. https://

doi.org/10.1016/j.cemconcomp.2019.103493, last 

accessed November 7, 2022.

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Compendium of Papers from Funded Research Projects

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COOPERATIVE PERCEPTION AND CONTROL FOR 

FREEWAY TRAFFIC SYSTEM OPERATIONS

UNIVERSITY OF CINCINNATI AND THE UNIVERSITY OF 

CALIFORNIA, LOS ANGELES

The research team aims to develop next-generation Traffic 

System Management and Operations (TSMO) solutions for 

freeway systems based on cooperative driving automation 

(CDA). The proposed solution involves cooperative 

perception (i.e., estimation and prediction using various data 

sources based on machine-learning and filtering methods) 

and cooperative control (i.e., advanced artificial intelligence 

algorithms customized for vehicle- and infrastructure-level 

control, such as cooperative merging, platooning, and speed 

harmonization). The work is focusing on computational 

applications that could substantially increase freeway system 

safety and mobility to meet the following objectives: 

• Integrate traditional and nontraditional highway data to 

better explain and predict system performance. 

• Provide decision support to assist experts in highway 

system design, operations, and management.

ACCEPTED FOR PUBLICATION AND PRESENTATIONS

1. Xu, R., H. Xiang, Z. Tu, X. Xia, M-H. Yang, and J. Ma. 

2022. “V2X-ViT: Vehicle-to-Everything Cooperative 

Perception with Vision Transformer.” Accepted for

publication European Conference on Computer Vision

ECCV2022.

The following projects were jointly funded by the Federal Highway Administration’s (FHWA) EAR 

Program and the National Science Foundation (NSF).

TRAFFIC OPERATING SYSTEM FOR SMART CITIES

UNIVERSITY OF CALIFORNIA (UC) BERKELEY

In addition to minimizing congestion and reducing driver 

errors, the CPS research at UC Berkeley aims to curtail 

collisions at intersections and to contain the costs of city 

parking by reducing the time it takes to find open spots. To 

achieve these goals, researchers use existing automotive, 

communication, and computation tools to manage traffic at 

three levels—the vehicle, the road link, and the network link. 

The options include adjusting the speed and headway of 

automated vehicles, optimizing the timing of traffic signals 

and special lanes (like opening high-occupancy vehicle 

lanes to all traffic), and setting traffic signals to favor traffic 

on particular routes at specific times. The UC Berkeley team 

is collecting field data in two locations—Arcadia, CA, and 

North Bethesda, MD. At the Maryland location, researchers 

are pulling data from actual traffic as it moves through eight 

intersections, and they are building simulation models based 

on those real-life situations. (NSF award 15-45116)

ACCEPTED FOR PUBLICATION AND PRESENTATIONS 

1. Li, R., P. Brown, and R. Horowitz. 2021. “Employing 

Altruistic Vehicles at On-Ramps to Improve the Social 

Traffic Conditions.” Presented at American Control 

Conference. New Orleans, LA: IEEE.

2. Mehr, N., R. Li, and R. Horowitz. 2021. “A Game Theoretic 

Macroscopic Model of Lane Choices at Traffic Diverges 

with Applications to Mixed-Autonomy Networks.” 

Transportation Research Part B: Methodological 144, 

45–59. https://doi.org/10.1016/j.trb.2020.11.004, last 

accessed November 8, 2022.

3. Arcak, M., and N. Martins. 2021. “Dissipativity Tools for 

Convergence to Nash Equilibria in Population Games.” 

IEEE Transactions on Control of Network Systems 8, no. 1: 

30–50. https://ieeexplore.ieee.org/document/9219202, last

accessed November 8, 2022.

TRAFFIC SIGNAL CONTROL WITH CONNECTED AND 

AUTONOMOUS VEHICLES IN THE TRAFFIC STREAM 

UNIVERSITY OF FLORIDA

As America’s urban population multiplies, so does the 

number of cars on city streets. The resulting congestion can 

contribute to increased or unreliable travel time. The 

emergence of highly automated vehicles could help improve 

urban mobility if the cars can be safely integrated into cyber 

physical systems (CPS) that also include technologically 

advanced traffic signals and road sensors.(2,3)

Artificial Intelligence

Interagency Research

Artificial Intelligence & Interagency Research
EXPLORATORY ADVANCED RESEARCH

EAR Program

Compendium of Papers from Funded Research Projects

9

AUGMENTED REALITY FOR CONTROL OF 

RESERVATION-BASED INTERSECTIONS WITH 

MIXED AUTONOMOUS NON-AUTONOMOUS FLOWS

UNIVERSITY OF TEXAS AUSTIN AND UNIVERSITY OF 

WASHINGTON

If there were only fully automated vehicles, an intersection 

could, in theory, greatly reduce wait times and traffic 

congestion. Yet even one manually driven vehicle can 

significantly reduce these benefits. Legacy vehicles will be 

on the road for years to come, so researchers at the 

University of Texas at Austin are examining how to 

accommodate both fully automated and manually driven 

vehicles on the road. The researchers will use an 

intersection control system to transmit a time reservation to 

fully automated vehicles and use augmented reality and a 

heads-up display, such as a projection on the windshield, to 

notify the human drivers to either speed up or slow down to 

drive through the intersection at their appointed time. This 

smart intersection concept would also incorporate 

nonmotorized travelers, who would be connected through 

augmented-reality goggles or their smartphones. 

These devices would then communicate to the pedestrian or 

the bicyclist when to cross. The researchers will develop 

algorithms based on human behavior that will help vehicles 

and humans safely and efficiently pass through an 

intersection. The next phase of the research project will 

involve experimenting with these algorithms with human 

participants outfitted with augmented-reality devices in 

vehicles and on their person. (NSF award 17-39964)

ACCEPTED FOR PUBLICATION

1. Du, B., K. Qian, C. Claudel, and D. Sun. 2021. 

“Parallelized Active Information Gathering Using 

Multisensor Network for Environment Monitoring.” IEEE 

Transactions on Control Systems Technology 30, no. 2: 

625–638. https://ieeexplore.ieee.org/document/9420289, 

last accessed November 8, 2022.

The CPS research at the University of Florida seeks to 

develop signal control strategies by optimizing the 

technology in automated vehicles and other sensor 

mechanisms. The researchers are developing algorithms 

that obtain the locations, directions, and speeds of vehicles 

as they enter the communications range of an intersection; 

send that data to the traffic signal; and calculate the best 

approach trajectories for each vehicle. By optimizing the 

signal timing, this approach can improve traffic flow and 

minimize travel time. Twice in 2017, the researchers tested 

some computer simulations on a closed-course intersection 

at the Florida DOT’s Traffic and Engineering Research Lab 

in Tallahassee, FL. Based on the lessons learned, the team 

is enhancing the algorithm to implement a testbed on the 

university’s Gainesville, FL, campus. The researchers are 

working to improve the accuracy of the information they 

receive from sensors and extend the communications range 

so they can gather data from vehicles farther from the 

intersection. (NSF award 14-46813)

ACCEPTED FOR PRESENTATIONS 

1. Emami, P., P. He, S. Ranka, A. Rangarajan. 2021. 

“Efficient Iterative Amortized Inference for Learning 

Symmetric and Disentangled Multi-Object 

Representations.” Presented at the 38th International 

Conference on Machine Learning. Virtual.

This self-driving vehicle, named NaviGator, is part of the 

University of Florida’s research on cyber physical systems. 

© University of Florida.

Interagency Research
EXPLORATORY ADVANCED RESEARCH

EAR Program

Compendium of Papers from Funded Research Projects

10

IDENTIFYING, CHARACTERIZING, AND SHAPING 

MULTI-SCALE CYBER-HUMAN INTERACTIONS IN 

MIXED AUTONOMOUS/CONVENTIONAL VEHICLE 

TRAFFIC

UNIVERSITY OF WISCONSIN-MADISON

Researchers at the University of Wisconsin-Madison are 

exploring what makes human drivers lose trust in vehicle 

automation, leading the driver to intervene unnecessarily. 

The researchers are exploring scenarios that include human 

drivers in both fully automated and manually controlled 

vehicles and how erroneous human interventions could 

negatively impact traffic flow. With more automated vehicles 

on the roadways in the upcoming decades, researchers are 

interested in increasing trust in automated vehicles, which 

would then help improve traffic flow. In the first phase of the 

project, the researchers are using a driving simulator and 

software to simulate traffic, providing the capability to test 

automated vehicle algorithms and human-driven vehicle 

simulations safely and efficiently. For the next phase of this 

project, the researchers are designing experiments to 

include human participants in controlled field tests. (NSF 

award 17-39869)

ACCEPTED FOR PUBLICATION

1. Elmquist, A., and D. Negrut. 2021. “Modeling Cameras for 

Autonomous Vehicle and Robot Simulation: An Overview.” 

IEEE Sensors Journal 21, no. 22: 25547–25560. https://

ieeexplore.ieee.org/abstract/document/9564034, last 

accessed November 8, 2022. 

2. Elmquist, A., R. Serban, and D. Negrut. 2021. “A 

Sensor Simulation Framework for Training and Testing 

Robots and Autonomous Vehicles.” Journal of 

Autonomous Vehicles and Systems 1, no. 2. 021001. 

https://doi.org/10.1115/1.4050080, last accessed 

November 8, 2022.

MAKING MICROMOBILITY SMARTER 

AND SAFER (M2S2) 

Interagency Research

RUTGERS UNIVERSITY

The growth of micromobility vehicles (transportation 

devices such as pedal-driven and electric-assist bicycles as 

well as electric-assist scooters) in the United States over 

the past decade has been staggering. From 2010 to 2019, 

shared micromobility vehicle ridership ballooned from 

321,000 trips annually in 2010 to 136 million annually in 

2019.(2)

 In 2020, travelers in the United States took an 

estimated 67.9 million trips on shared micromobility 

vehicles.(3)

 Driven by the rise of shared rentals deployed by 

municipalities and private companies, micromobility 

vehicles have become a popular transportation alternative 

for individuals in cities and, increasingly, in smaller towns 

and suburbs throughout the Nation.

The types of micromobility vehicles available for use have 

also increased. As a result, policymakers and researchers 

have grappled with the implications of this growing mode of 

transit. In particular, public safety for micromobility vehicle 

users, as well as the pedestrians and drivers they encounter, 

has become an increasing concern. The Rutgers University 

research team seeks to gather better data and create 

technological tools that help improve safety for pedestrians 

and micromobility vehicle users. (NSF award 19-51890)

ACCEPTED FOR PUBLICATION 

1. Noland, R. B. 2021. “Scootin’ in the Rain: Does Weather 

Affect Micromobility?” Transportation Research Part A: 

Policy and Practice 149, 114–123. https://doi.org/10.1016/j.

tra.2021.05.003, last accessed November 8, 2022.

2. Zhang, T. T., M. Guo, P. J. Jin, Y. Ge, and J. Gong. 2021. 

“Longitudinal-Scanline-Based Arterial Traffic Video 

Analytics with Coordinate Transformation Assisted by 3D 

Infrastructure Data.” Transportation Research Record: 

Journal of the Transportation Research Board 2675, no 3. 

https://doi.org/10.1177/0361198120971257, last accessed 

November 8, 2022. 

E-scooters being used alongside car traffic. 

© 2021 Rutgers University.

3. Liu, C. K., and D. Negrut. 2021. “The Role of PhysicsBased Simulators in Robotics.” Annual Review of Control, 

Robotics, and Autonomous Systems 4: 35–58. https://doi.

org/10.1146/annurev-control-072220-093055, last 

accessed November 8, 2022.
EXPLORATORY ADVANCED RESEARCH

EAR Program

Compendium of Papers from Funded Research Projects

11

HYBRID TWINS FOR URBAN TRANSPORTATION: 

FROM INTERSECTIONS TO CITYWIDE MANAGEMENT

COLUMBIA UNIVERSITY

Traffic management in metropolitan areas poses distinct 

challenges. Faced with congested city streets shared by 

pedestrians, bicyclists, electric-scooter riders, and drivers, 

traffic managers must find new ways to maintain smooth and 

safe traffic flow. Traffic managers can harness the data 

collected by electronic devices that connect to the internet, 

infrastructural sensors, other devices or systems, and 

communications networks in the Internet of Things (IoT) to 

transform urban streets. This 3-yr research project leverages 

the IoT to develop an urban traffic management system that 

will help improve traffic safety, mobility, and reliability. To 

explore the possibilities of this advanced traffic management 

system, the research team at Columbia University is creating 

a hybrid twin of an area of New York City. (NSF award 

20-38984)

ACCEPTED FOR PUBLICATION AND PRESENTATIONS

1. Ghasemi, M., Z. Yang, M. Sun, H. Ye, Z. Xiong, J. Ghaderi, 

Z. Kostic, and G. Zussman. 2021. “Video-Based Social 

Distancing Evaluation in the Cosmos Testbed Pilot Site.” 

MobiCom ’21: Proceedings of the 27th Annual International 

Conference on Mobile Computing and Networking. 

874–876. New York, NY: Association for Computing 

Machinery. https://doi.org/10.1145/3447993.3510590, last 

accessed November 8, 2022. 

2. Ghasemi, M., S. Kleisarchaki, T. Calmant, L. Gürgen, J. 

Ghaderi, Z. Kostic, and G. Zussman. 2022. “Real-Time 

Camera Analytics for Enhancing Traffic Intersection Safety.” 

in MobiSys ’22: Proceedings of the 20th Annual International 

Conference on Mobile Systems, Applications and Services. 

630–631. New York, NY: Association for Computing 

Machinery. https://doi.org/10.1145/3498361.3538669, last 

accessed November 8, 2022.

3. Duan, Z., Z. Yang, R. Samoilenko, D. S. Oza, A. 

Jagadeesan, M. Sun, H. Ye, Z. Xiong, G. Zussman, and Z. 

Kostic. 2022. “Smart City Traffic Intersection: Impact of 

Video Quality and Scene Complexity on Precision and 

Inference.” 19th International Conference on Smart City. 

Haikou, Hainan, China: IEEE. https://ieeexplore.ieee.org/

document/9781018, last accessed November 8, 2022.

4. Kostic, Z., A. Angus, Z. Yang, Z. Duan, I. Seskar, G. 

Zussman, D. Raychaudhuri. 2022. “Smart City 

Intersections: Intelligence Nodes for Future Metropolises.” 

https://arxiv.org/pdf/2205.01686.pdf, last accessed 

November 8, 2022.

5. Yang, Z., M. Sun, H. Ye, Z. Xiong, G. Zussman, Z. Kostic. 

2022. “Bird’s-Eye View Social Distancing Analysis System.” 

2022 IEEE International Conference on Communications 

Workshop on Edge Learning for 5G Mobile Networks and 

Beyond. Seoul, South Korea: IEEE. https://ieeexplore.ieee.

org/document/9814627, last accessed November 8, 2022.

The researchers are using cameras to capture traffic data. 

© 2022 Columbia University.

Interagency Research
EXPLORATORY ADVANCED RESEARCH

EAR Program

Compendium of Papers from Funded Research Projects

12

FHWA’s Turner-Fairbank Highway Research Center (TFHRC) conducts research across a wide range 

of topics and disciplines. To supplement the expertise 

of the permanent staff, it is important to bring in 

researchers with the appropriate backgrounds to 

investigate specific problems on a short-term basis. 

Through the EAR Program, FHWA utilizes the RAP of 

the National Academy of Sciences (formerly the 

National Research Council (NRC)) for this purpose. 

The National Academy of Sciences provides a process 

for selecting candidates on a competitive merit basis 

and (subsequently) for the administration of the 

resident fellows during their tenures at FHWA.

HENG LIU, PH.D. 

During his RAP tenure, Heng Liu developed two condition 

forecast models for the FHWA Long-Term Bridge 

Performance InfoBridgeTM web portal. He also published one 

peer-reviewed journal paper and made several presentations 

at the Transportation Research Board Annual Meeting and 

various FHWA meetings and webinars.

ACCEPTED FOR PUBLICATION

1. Liu, Heng. 2022. “Performance Forecasts of US Highway 

Bridge Networks Using Generalized Poisson–Binomial 

Distribution.” ASCE Journal of Infrastructure Systems 28, 

no. 4. https://doi.org/10.1061/(ASCE)IS.1943-

555X.0000710, last accessed November 7, 2022.

2. Liu, H., J. Nehme, and P. Lu. 2021. “An Application of 

Convolutional Neural Network for Deterioration Modeling of 

Highway Bridge Components in the United States.” 

Structure and Infrastructure Engineering. https://www.

tandfonline.com/doi/full/10.1080/15732479.2021.1979597, 

last accessed November 7, 2022.

RAPHAEL ISOKPEHI, PH.D.

During his 12-mo RAP tenure, Raphael Isokpehi focused on 

applying big data techniques to pavement management. In 

his project, titled “Leveraging Big Data for Enhanced 

Pavement Management,” Dr. Isokpehi considered diverse 

data types for pavement management, a wide range of 

technologies for measuring attributes of a pavement 

network, an effective collection of massive amounts of data 

at traffic speed, and auxiliary data’s increased availability. 

Dr. Isokephi engaged and established collaboration with 

pavement management experts through participation in 

conferences. The research team collaborated with other 

pavement engineers and researchers in the public and 

private sectors as well as academia through the 

Transportation Pooled Fund study “Pavement Structural 

Evaluation with Traffic Speed Deflection Devices” and the 

Big Data for Smart Pavement Management and Deflection at 

Road Traffic Speed User Group. 

ACCEPTED FOR PRESENTATIONS

1. Isokpehi, R., A. Nadkarni, and N. Sivaneswaran. 2022. “Big 

Data Investigations to Knowledge for Enhanced Pavement 

Management.” Presented at pre-conference workshop at 

ICMPA 2022—11th International Conference on Managing 

Pavement Assets. Chicago, IL: Transportation Research 

Board.

2. Isokpehi, R., A. Nadkarni, and N. Sivaneswaran. 2022. “Big 

Data Investigations to Knowledge for Enhanced Pavement 

Management.” Presented at the 16th Deflection at Road 

Traffic Speed User Group meeting held in conjunction with 

the Transportation Research Board 101st Annual Meeting. 

Washington DC: Transportation Research Board. 

Research Associateship Program

Research Associateship Program (RAP)

Through RAP, the EAR Program:

• Provides postdoctoral scientists and engineers who

show unusual ability and promise an opportunity to 

conduct research—on problems largely of their 

personal choice—that is compatible with the research 

interests of TFHRC.

• Contributes to the general research efforts of TFHRC.

RAP provides postdoctoral candidates the opportunity to 

spend 2 or 3 yr working on leading-edge transportation 

research in world-class facilities with top-notch experts. 

Research associates are matched with an FHWA adviser 

who mentors them throughout the program.
EXPLORATORY ADVANCED RESEARCH

EAR Program

Compendium of Papers from Funded Research Projects

13

1. Federal Highway Administration. 2015. Video Analytics 

Research Projects. Report No. FHWA-HRT-15-025. 

Washington, DC: Federal Highway Administration. https://

www.fhwa.dot.gov/publications/research/ear/15025/index.

cfm, last accessed November 21, 2022.

2. National Association of City Transportation Officials. 2020. 

Shared Micromobility in the U.S.: 2019. New York, NY: 

National Association of City Transportation Officials. 

https://nacto.org/shared-micromobility-2019/, last 

accessed April 18, 2022.

References

FHWA-HRT-23-021

HRTM-30/01-23(50)E

References

3. North American Bikeshare and Scootershare Association. 

2021. 2nd Annual Shared Micromobility State of the 

Industry Report. Portland, ME: North American Bikeshare 

and Scootershare Association. https://share.hsforms.

com/19DY9nNTJQemTS57h3SD6CA55271, last accessed 

May 18, 2022.

Recommended citation: Federal Highway Administration, 

Exploratory Advanced Research (EAR) Program 

Compendium of Papers from Funded Research Projects 

(Washington, DC: 2023) https://doi.org/10.21949/1521965.