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NASA Develops Pod to Help Autonomous Aircraft Operators 


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A white helicopter with blue stripe and NASA logo sits inside of an aircraft hangar with grey cement floors and white roofing with metal beams. The helicopter has four grey blades and has a black base. A white cube is attached to the black base and holds wires and cameras. No one sits inside the helicopter, but the door is open, and a grey seat is shown along with four black, tinted windows. There is an American flag on the helicopter’s tail.
The NASA Airborne Instrumentation for Real-world Video of Urban Environments (AIRVUE) sensor pod is attached to the base of a NASA helicopter at NASA’s Kennedy Space Center in Cape Canaveral, Florida in April 2024 before a flight to test the pod’s cameras and sensors. The AIRVUE pod will be used to collect data for autonomous aircraft like air taxis, drones, or other Advanced Air Mobility aircraft.
NASA/Isaac Watson

For self-flying aircraft to take to the skies, they need to learn about their environments to avoid hazards. NASA aeronautics researchers recently developed a camera pod with sensors to help with this challenge by advancing computer vision for autonomous aviation.  

This pod is called the Airborne Instrumentation for Real-world Video of Urban Environments (AIRVUE). It was developed and built at NASA’s Armstrong Flight Research Center in Edwards, California. Researchers recently flew it on a piloted helicopter at NASA’s Kennedy Space Center in Cape Canaveral, Florida for initial testing.  

The team hopes to use the pod to collect large, diverse, and accessible visual datasets of weather and other obstacles. They will then use that information to create a data cloud for manufacturers of self-flying air taxis or drones, or other similar aircraft, to access. Developers can use this data to evaluate how well their aircraft can “see” the complex world around them.  

A woman with brown hair pulled into a bun, wearing a white, collared shirt with black lines, stands in the foreground of the photo. She is working on a grey laptop computer with black screen with computer coding shown. Behind her, on the left side, is the side of a man’s head and he is wearing a red polo. On the right side, behind her computer, is a white cube with wires and the man is placing his hand inside.
NASA researchers Elizabeth Nail (foreground) and A.J. Jaffe (background) prepare the NASA Airborne Instrumentation for Real-world Video of Urban Environments (AIRVUE) sensor pod for testing at NASA’s Kennedy Space Center in Cape Canaveral, Florida, in April 2024.
NASA/Isaac Watson

“Data is the fuel for machine learning,” said Nelson Brown, lead NASA researcher for the AIRVUE project. “We hope to inspire innovation by providing the computer vision community with realistic flight scenarios. Accessible datasets have been essential to advances in driver aids and self-driving cars, but so far, we haven’t seen open datasets like this in aviation.” 

The computer algorithms that will enable the aircraft to sense the environment must be reliable and proven to work in many flight circumstances. NASA data promises that fidelity, making this an important resource for industry. When a company conducts data collection on their own, it’s unlikely they share it with other manufacturers. NASA’s role facilitates this accessible dataset for all companies in the Advanced Air Mobility industry, ensuring the United States stays at the forefront of innovation. 

Once the design is refined, through evaluation and additional testing, the team hopes to make more pods that ride along on various types of aircraft to collect more visuals and grow the digital repository of data.

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      Then, when it was given images from three different science instruments on SOHO, the model’s predictions were highly accurate. Out of 21 geoeffective CMEs, the model correctly predicted all 21 of them; of 7 non-geoeffective ones, it correctly predicted 5 of them.
      “The algorithm shows promise,” said heliophysicist Jack Ireland of NASA’s Goddard Space Flight Center in Greenbelt, Maryland, who was not involved in the study. “Understanding if a CME will be geoeffective or not can help us protect infrastructure in space and technological systems on Earth. This paper shows machine learning approaches to predicting geoeffective CMEs are feasible.”
      The white cloud expanding outward in this image sequence is a coronal mass ejection (CME) that erupted from the Sun on April 21, 2023. Two days later, the CME struck Earth and produced a surprisingly strong geomagnetic storm. The images in this sequence are from a coronagraph on the NASA/ESA (European Space Agency) SOHO (Solar and Heliospheric Observatory) spacecraft. The coronagraph uses a disk to cover the Sun and reveal fainter details around it. The Sun’s location and size are indicated by a small white circle. The planet Jupiter appears as a bright dot on the far right. NASA/ESA/SOHO Earlier Warnings
      During a severe geomagnetic storm in May 2024 — the strongest to rattle Earth in over 20 years — NASA’s STEREO (Solar Terrestrial Relations Observatory) measured the magnetic field structure of CMEs as they passed by.
      When a CME headed for Earth hits a spacecraft first, that spacecraft can often measure the CME and its magnetic field directly, helping scientists determine how strong the geomagnetic storm will be at Earth. Typically, the first spacecraft to get hit are one million miles from Earth toward the Sun at a place called Lagrange Point 1 (L1), giving us only 10 to 60 minutes advanced warning.
      By chance, during the May 2024 storm, when several CMEs erupted from the Sun and merged on their way to Earth, NASA’s STEREO-A spacecraft happened to be between us and the Sun, about 4 million miles closer to the Sun than L1.
      A paper published March 17, 2025, in the journal Space Weather reports that if STEREO-A had served as a CME sentinel, it could have provided an accurate prediction of the resulting storm’s strength 2 hours and 34 minutes earlier than a spacecraft could at L1.
      According to the paper’s lead author, Eva Weiler of the Austrian Space Weather Office in Graz, “No other Earth-directed superstorm has ever been observed by a spacecraft positioned closer to the Sun than L1.”
      Earth’s Lagrange points are places in space where the gravitational pull between the Sun and Earth balance, making them relatively stable locations to put spacecraft. NASA By Vanessa Thomas
      NASA’s Goddard Space Flight Center, Greenbelt, Md.
      View the full article
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