Category Archives: NASA

Hurricane Active Sensing


EAST GREENSBORO, N.C. (June 1, 2020) – As another hurricane season begins, a computational science and engineering faculty member and two Ph.D. students at North Carolina Agricultural and Technical State University aim to improve tropical forecasting and modeling by deploying drones to collect data from the eye of hurricanes.

Hyoshin Park, Ph.D., and Ph.D. candidates Larkin Folsom and Justice Darko will explore ways to maximize the data collection and possible flight paths in future storms in this project supported by NASA’s Jet Propulsion Laboratory (JPL). During the summer of 2019, Park visited JPL and formulated the problem of efficient hurricane data collection using drones in collaboration with Masahiro Ono, Ph.D., a research technologist at the laboratory.

“Our department’s innovative, artificial intelligence-based model explores the search space to improve the hurricane track and intensity,” said Park, an assistant professor in the College of Engineering.

In 2014, the National Oceanic and Atmospheric Administration (NOAA) released two unmanned aerial vehicles (UAVs) into the eye of Hurricane Edouard, at the time the first category 3 or stronger storm to form in the Atlantic Ocean since Hurricane Sandy. The UAVs were the first-ever deployed into the eye of a tropical system and collected data inside the eye and the storm’s outer vortex, enduring for 68 minutes before plunging into the ocean.

The team will use early measurements from NOAA when the hurricane is forming to determine the storm’s wind-field and other important data. The crew will then know the ideal location to drop up to 10 drones, which will communicate with each other and to their base on the plane, effectively collecting as much data as possible.

For years, hurricane hunters have launched dropsondes into the center of the storm, near its eyewall, to determine the boundaries and structures of a hurricane. The device contains a GPS receiver, along with pressure, temperature and humidity (PTH) sensors to capture atmospheric profiles and thermodynamic data.

This method, while critical to yield important data for forecasting, requires aircraft and crew to drop to unsafe altitudes for a better view of the drop zone. This maneuver takes more time and fuel and puts the crew at risk. Additionally, the process requires numerous dropsondes to gather sufficient data about the hurricane and its forecast.

The team will repurpose the methodology from Park and Folsom’s 2018 proposal funded by the National Science Foundation’s Division of Information and Intelligent Systems Robust Intelligence program. The study focused on maximizing data collection for a Mars mission exploration of an unexplored environment with no flow of humans or traffic.

“This research is important because it will help improve hurricane forecasts, saving local economies money by more precisely constraining the forecast track and intensity,” said Folsom. “Locations that will not be impacted can keep their economies open, and those that will be impacted can more effectively prepare.”

Additionally, the research will help improve public trust in tropical forecasting. The A&T team members are also joined by Hui Sui, Ph.D., principal investigator and JPL engineering and science directorate Stratosphere and Upper Troposphere, Masahiro Ono, Ph.D., JPL research technologist from Robotic Surface Mobility and Masashi Minamide, Ph.D., an assistant professor at the University of Tokyo. 

Park & NASA-JPL Education

written by Kim Orr


Finding the best driving route for a Mars rover isn’t as easy as turning on a navigation app – but John Park and Hiro Ono want to make it so. A program at NASA’s Jet Propulsion Laboratory is helping them turn their idea into a reality, all while promoting diversity in STEM.

A tenure-track faculty member at North Carolina A&T State University, Park has spent the past two summers at JPL through an Education Office initiative designed to connect students and researchers from Historically Black Colleges and Universities (HBCUs) to the Laboratory’s missions and science. The NASA-backed pilot program has brought more than a dozen student interns and several faculty researchers to JPL for projects investigating Mars, Earth and planets beyond our solar system.

Until his stint at JPL, Park’s research focused solely on Earth-bound transportation technologies, such as those used by self-driving cars. When he learned about JPL’s HBCU initiative from a colleague who had participated in the program, he seized on the chance to apply his research to space exploration.

“My previous projects and publications have dealt with decision-making tools for exploring uncertain areas on Earth and maximizing the information that’s available. I thought I could help bring that perspective to Mars rovers and helicopters.” 

says Park, who also helped connect several students from North Carolina A&T to internship opportunities with the HBCU initiative.

While researching potential applications for his research at JPL, Park learned that the challenges of getting around on Mars are similar to those faced by drivers on Earth. Rovers also need to get from place to place safely and efficiently – they’re just avoiding boulders instead of traffic jams.

It was precisely those challenges that Hiro Ono in JPL’s Robotic Mobility Group also wanted to overcome.

“I had an idea that I wanted to try, and we had all the ingredients. The HBCU program allowed us to try the idea”

says Ono, who designs artificial intelligence systems for future rover missions.

The HBCU initiative brought Park and Ono together along with Larkin Folsom, a student intern from North Carolina A&T. Together, the trio developed a proposal for a future system that would work similarly to the navigation apps we use to get through rush-hour traffic. The system would allow rovers to analyze routes as they drive, providing mission planners with information about the routes most likely to be hazard-free so they can make the most efficient use of the spacecraft’s limited energy supply and maximize the mission’s science goals.

“Previously, the way that we operated on Mars was to make the best guess about drivability solely from looking at orbital images. The idea that we are working on is to introduce the concept of probability. So if there are two terrains that are important to you but one of them is 90% traversable and the other is 60% traversable, which are you going to choose?”

says Ono.

In September, the National Science Foundation awarded Park, who submitted the proposal, with a grant to pursue the project. Park says the funding will go toward a JPL internship opportunity for a Ph.D. student from his university to continue research with Ono’s team.

Jenny Tieu is a STEM education project manager at JPL who manages the HBCU initiative with Roslyn Soto. She helped connect Park and Ono and says it’s collaborations like these that the initiative was designed to foster.

“Our goal with this initiative is to expand the number of HBCU students and faculty members participating in research at JPL and ultimately increase diversity among the Laboratory’s workforce. This National Science Foundation award is a positive indication that the initiative is not only building strong relationships between HBCUs and JPL, but also creating a ripple effect for additional opportunities.”

says Tieu.

Now in its fourth year, the HBCU initiative will once again bring students and faculty to JPL for research opportunities in the summer of 2020.

Meanwhile, Park and Ono are exploring ways to expand their technology into other arenas, including hurricane research and emergency response. Park has already received support from the U.S. Department of Transportation as well as the state DOT in Virginia and North Carolina for additional Earth-based applications of the technology.

Ono is serving as a consultant on the projects and has high hopes the results of the research will make its way back to JPL.

Says Ono,

“In the long run, having an intern, giving them a good experience, helping their career is going to come back to us. We, as JPL, can build connections around the world and among industry partners that are going to come back to us eventually.”

NSF Robust Intelligence. Information-theoretic Multiagent Paths for Anticipatory Control of Tasks (IMPACT)

INFORMATION Theoretic Nagivation

written by Kelly S. Morgan


Imagine you are a food writer for a magazine, sent to a city for a day to discover and report on local cuisine. You have three meals (breakfast, lunch, and dinner) over which to gather information and maximize your visit to create the clearest impression for your readers. As you come into town, you have a decision to make. Will you visit familiar, fast food and chain restaurants, or, will you seek out new, undiscovered eateries to paint your culinary picture? Obviously, patronizing unfamiliar restaurants is going to yield new, quality information, helping readers back at home draw sound, comprehensive conclusions about the locale.

Following the same logic, imagine navigating with a Google map. The most direct route has a reputation for heavy traffic which could jeopardize timely arrival. A slightly longer distance with less traffic might give you a better chance to reach your destination on time. Maximizing information gain by traveling unchartered territory is the goal of Dr. John Park’s research. Park is using autonomous and connected vehicles armed with high-performance onboard computers to traverse unexplored areas. Under strict limitations of time and energy, he is trying to gain and transmit the most data possible from new, dynamic environments.

On the Floor

“We are not vacuuming, it just looks like we want a (very!) clean rug,”

says Park. Five Ph.D. candidates are kneeling on the floor, encircling a test-bed in the middle of Park’s Hines Hall lab. The team has taped-out a grid on low-pile carpet to border a series of two by two-foot squares called “cells”. Crawling over the carpet cells are four iRobot Roomba® vacuums.

While most Consumers purchase these devices in a pre-programmed suction mode, Park acquired them unprogrammed. His team has fitted each Roomba® with two round decks creating tiered towers above each device. The hip, techy, blinking wedding cakes crawl and spin on the floor, learning, updating, clarifying and confirming their environment. “The process by which autonomous vehicles learn about carpet in Hines Hall using is actually quite similar to how we would receive and process data in other, more difficult environments,” explains Park. “Autonomous vehicles allow operators to observe, record, make decisions and even take action in locations where physical human presence is either impossible or undesirable.”

Adorning the Roomba decks are serious technology: ZED Stereo Cameras, RPLIDAR 360-Degree Laser Ranger Scanners, NVIDIA Jetson AGX Xavier High-Performance Computing Units and Cray Supercomputers. Funding for this and other equipment has been provided by the NASA Jet Propulsion Laboratory, the Virginia Department of Transportation, the North Carolina Department of Transportation, the United States Department of Transportation, and most recently a $240,000 three-year grant from the National Science Foundation. This new project is known as IMPACT (Information-theoretic Multiagent Paths for Anticipatory Control of Tasks).

Learn, Update, Clarify, Confirm

Park’s project promotes the scientific and engineering value of intelligent navigation systems by finding the best routes of vehicles with autonomous decision-making based on the desired level of exploration, risk, and energy constraints. The navigation analyzes images (autonomous driving feature detection), selectively collecting data without interrupting their trips.

When an autonomous vehicle travels through Park’s floor grid, information is gained by visiting unclassified or uncertainly classified cells, observing the condition in those cells, and estimating the entropy (degree of disorder or randomness) in other cells. Each vehicle updates its path plan every time it moves to a new cell. By sharing information about the state of the cells it encounters, it helps to define the optimal parameters to be used in other vehicles’ journeys. If a cell is visited by another vehicle and found to be in the same state as the original cell of that type, then all vehicles have confirmation that these cells are correlated. The vehicles gather and confirm data during their journeys, updating Park’s knowledge bank as new information about the terrain is discovered.

Understanding how information can be learned throughout navigation will produce a guide to government planners and transportation engineers, and offer substantial benefits to society in improved choice modeling especially in congested traffic networks when heterogeneous users cause complex situations with weak road resilient networks. Future navigation and autonomous vehicle driving will realize improved efficiency by considering the tradeoffs between energy, time and environmental challenges.

The Team

Dr. Park has staffed his grant with five capable Ph.D. candidates from the Department of Computational Science and Engineering in the College of Engineering. Khadijeh Shirzad, Justice Darko, Yaa Takiwaa Acquaah, Larkin Folsom, and Nigel Pugh are fearless, despite the enormous programming tasks ahead. While they describe this project as “intricate” and “complicated” the team seems clear-minded and process-oriented with how they intend to gather and analyze the robots’ data.

The five students hail from three countries boasting majors in mathematics, physics and computer science, yet all seem to agree on one thing: while computational science and engineering is a complex, multifaceted field, it lives in reality.

“Our studies mimic and therefore benefit real life,”

says Acquaah.

“We use math and computational thinking to analyze data, solve pressing problems, and improve the world.”

NSF Robust Intelligence. Information-theoretic Multiagent Paths for Anticipatory Control of Tasks (IMPACT)


Information-theoretic Route Planning for a Heli-Rover Team.

Co-I Park (NCA&T)
PI: Ono, Co-I: Otsu, Agha (NASA JPL)
Contract: RSA1625294, Sponsor: NASA JPL
4/2019 – 9/2019

Information-theoretic Route Planning for Autonomous Vehicle.

2018 & 2019 Visiting Professor Park (NCA&T)
Sponsor: NASA JPL
Collaboration with Robotics Group PI Ono
5/2018 – 8/2018, 5/2019 – 8/2019


Information-theoretic Multiagent Paths for Anticipatory Control of Tasks (IMPACT)

PI: Park (Single)
Sponsor: NSF Robust Intelligence 

Intelligent Data Exploration & Analysis for New & Existing Transportation Technology (IDEANETT)

PI Park (NCA&T), Co-PI Mcbride (NCA&T), Mcdonald (UNC DCRP)
Sponsor: NC Transportation Center of Excellence in Advanced Technology Safety and Policy.
1/2020 – 12/2022

Information-theoretic Route Planning for a Heli-Rover Team.

Co-I Park (NCA&T)
PI: Ono, Co-I: Otsu, Agha (NASA JPL)
Contract: RSA1625294, Sponsor: NASA JPL
4/2019 – 9/2019

Developing a Plan for Using Unmanned Aerial Vehicles for Traffic Operations Applications in Virginia.

Co-PI Park (NCA&T), PI Alden (VTTI), Co-PI Coggin (VTTI)
VTRC 116038, Sponsor: VDOT
8/2019 – 4/2021 

DRONETIM: Dynamic Routing of Unmanned-aerial and Emergency Team Incident Management.

PI Park (NCA&T), Co-PI: Yi (NCA&T), Alden (VTTI)
Sponsor: USDOT Tier 1 CATM
2/2019 – 8/2020 
Exhibit F 

Information-theoretic Route Planning for Ground Vehicles.

2018 & 2019 Visiting Professor Park (NCA&T)
Sponsor: NASA JPL
Collaboration with Robotics Group PI Ono
5/2018 – 8/2018, 5/2019 – 8/2019

Research Outputs

DRONETIM: Dynamic Routing of Unmanned-aerial and Emergency Team Incident Management. J.Darko, Y. Acquaah, L. Folsom, H. Park, A. Alden. Proceedings of the The 99th Annual Meeting of TRB2020, #20-02283, 2020.


Travelers’ rationality in anticipatory online emergency response.

Sole PI Park (NCA&T)
Sponsor: USDOT Tier 1 CATM
2/2018 – 8/2019 
Exhibit F   

Deep Learning Software for Traffic State Prediction

PI, Co-PI Park (NCA&T)
Sponsor: NC Transportation Center of Excellence in Advanced Technology Mobility and Congestion.
8/2020 – 7/2022

Information-theoretic Route Planning for Heli.

2019 Visiting Professor Park (NCA&T)
Sponsor: NASA JPL
Collaboration with Robotics Group PI Ono
5/2019 – 8/2019

Roadside Truck Placard Readers for Advanced Notice and Response at Safety- Critical Facilities

Co-PI Park (NCA&T)
PI: Alden (VTTI)
VTRC 114591, Sponsor: VDOT
11/2018 – 9/2019 

Advanced Traffic Analysis of Aerial Video Data.

PI Park (NCA&T), Co-PI: Yi (NCA&T)
Sponsor: NCDOT, 3/2019 – 8/2019  

Research Output

  • Simulation-Based Optimization of Emergency Response Considering Rationality of Travelers, D. Waddell, N. Pugh, K. Shirzad, H. Park, Proceedings of the The 98th Annual Meeting of TRB2019, #19-05975, 2019.
  • Online Optimization with Look-Ahead for Freeway Emergency Vehicle Dispatching considering Availability, H. Park, D., Waddel, A. Haghani, Transportation Research Part C: Emerging Technologies, in Production.
  • Real-time crash prediction and avoidance under unexpected traffic congestion, H. Park, A. Haghani, M.A. Knodler, S. Samuel, Accident Analysis & Prevention 112, 39-49, 2018
  • Visualization-based Dynamic Dispatching of First Responders.D. Waddell, N. Pugh, H. Park. Proceedings of theThe 98th Annual Meeting of TRB2019, #19-05569, 2019.
  • Prediction of Secondary Crash Likelihood considering Incident Duration using High Order Markov Model. N. Pugh, H. Park. Proceedings of the IEEE SoutheastCon 2019, Huntsville, AL, April 2019.
  • Stochastic emergency response location model considering secondary incidents on freeways. H. Park. Ali Shafahi, Ali Haghani, IEEE Transactions on Intelligent Transportation Systems, 17 (9), 2528-2540, 2016.
  • Real-time prediction of secondary incident occurrences using vehicle probe data. H. Park. Ali Haghani, Transportation Research Part C: Emerging Technologies, 70, 69–85, 2016.
  • Stochastic Capacity Adjustment Considering Secondary Incidents. H. Park. Ali Haghani, IEEE Transactions on Intelligent Transportation Systems, 17 (10), 2843 – 2853, 2016.
  • Interpretation of Bayesian neural networks for predicting the duration of detected incidentsl. H. Park, Ali Haghani, Xin Zhang, Journal of Intelligent Transportation Systems, 20 (4), 385-400, 2016.