While flying cars have been a science fiction mainstay for decades, new sensors, software and other technology put personal air travel vehicles within reach. MEMS and Sensors Industry Group (MSIG) interviewed Dr. Alberto Speranzon, Fellow at Honeywell Aerospace Advanced Technology, about his upcoming keynote Sensors and Software Enabling Autonomy for Urban Air Mobility at the MEMS and Sensors Technical Congress (MSTC 2021) virtual event, April 13-15. Dr. Speranzon will discuss Honeywell’s challenges in enabling air taxis and the path forward to building out the necessary infrastructure.
SEMI: People have dreamed of flying cars for decades. What has changed recently that makes them a near-term reality?
Speranzon: There certainly are multiple factors that have contributed to pushing both new startups and established aerospace companies to make this dream a reality. Advances in battery technology are bringing electric aviation closer to being viable. There is still more to be done to achieve higher energy density in batteries, but already with today’s technology we can have vehicles that can fly from the suburbs to the downtown of a large U.S. city.
At the same time, urbanization has created a lot of congestion, so finding new, efficient ways to move people and goods across megacities is becoming a critical need.
Undeniably, the autonomous car industry has contributed to demonstrating that it is possible to achieve levels of automation and autonomy that were unimaginable just a few decades ago. The advances in sensing and computation required to make self-driving cars a reality is certainly going to help the aviation industry to develop autonomy in the air.
SEMI: An air taxi must be highly sensorized. What types of sensors pose your biggest development challenge?
Speranzon: Aircraft based on today’s battery systems simply cannot accommodate the size, power requirements and weight of sensors used by standard airliners. So there still is work to be done to reduce the SWaP (Size, Weight and Power) of these systems.
Honeywell, for example, has developed a multipurpose radar, the IntuVue RDR-84K, that is only about the size of a paperback book. It can detect traffic, terrain and even weather, and was specially designed for air taxis and cargo UAS (unmanned aerial systems).
Today, however, we still rely on human pilots to make the very complex decisions and, despite all the limitations that human eyes have, we do rely on them in a multitude of complex situations.
There is also growing interest in integrating cameras into autonomous air taxis and similar platforms. Cameras can’t work alone, because they are affected by foggy, rainy and dim conditions. But they are lightweight, inexpensive, and require little power. That can make them very useful when combined with a compatible radar system like the RDR-84K.
While these sensors bring new opportunities to the aerospace industry, they also pose some big challenges.
For example, today’s state-of-the-art algorithms for image processing are machine learning algorithms called deep neural networks. They’re capable of extracting high-level information from pixels.
But when it comes to aircraft certification, these algorithms face major hurdles. There is no software of this kind in any certified air vehicle, and it is unclear how regulators would certify neural network-based software components.
Developers could avoid these new machine-learning algorithms and use standard computer vision methods instead. But they still face the challenge of deciding the type and quantities of images sufficient to declare the software “bug-free.”
A similar set of questions will be also be true for radars, as they will be used to feed data more directly into the autonomy modules of future air taxis.
So in the short term, we need to tackle the challenge of reducing the size and weight of sensors. But in parallel, we need to develop new ways to take advantage of machine learning: utilizing cameras and radars for autonomous decision making while still ensuring the highest standards of safety.
SEMI: How is autonomous air mobility more or less challenging than autonomous ground vehicles?
Speranzon: They are both challenging in their own ways.
Autonomy on the ground – and I am thinking specifically of autonomous cars – is challenging as their “normal” behavior is very complex.
We humans can drive from point A to point B over the public road network without a second thought. But to a machine, the heterogeneity of the people driving on the road, their sometimes unpredictable behavior, the changing weather conditions and shifting environments pose huge challenges. These things make what we call “normal driving” a very difficult problem to solve.
At the same time, however, “off-nominal” scenarios in ground autonomy, while complex, are not orders of magnitude more complex than “nominal” scenarios. Ground vehicles can brake and stop, change lanes or move to the side of the road to avoid a crash or manage a malfunction.
For autonomous air vehicles, the difference between nominal and off-nominal scenarios is more extreme.
“Nominal” flying can rely on some of the existing aviation infrastructure, like communication between air traffic control and other aircraft. Air taxis can follow predefined paths and long-established aviation procedures as they move from vertiport A to vertiport B. This results in more automation than autonomy: everything is prescribed in advance and the onboard computer will follow what is pre-defined. Thus, nominal conditions will be fairly simple.
However, in case of accidents or emergencies, aircraft face situations that are orders of magnitude more complex than nominal scenarios. An air vehicle cannot easily just “stop.” It could be 1,000-2,000 ft above ground, possibly above a bustling city.
Human pilots go through rigorous training to be able to deal with emergencies like these. Consider the split-second judgments and airmanship behind the 2009 “miracle on the Hudson” landing. Asking autonomy to make the right decisions and execute emergency behaviors is a huge challenge. And these systems will need to be certified to the aviation industry’s very high standards. At present, we do not even have a well-established set of certification rules that an autonomous flying vehicle should comply with.
SEMI: How soon might I be able to take an air taxi ride?
Speranzon: Initial deployment of air taxis will happen around 2025. They will have human pilots but will use simpler interfaces than today’s cockpits. This first step will provide technologies that make it easier to take off and land, and to avoid traffic. That will reduce the need for highly experienced pilots and should help alleviate the overall shortage of pilots in the aviation industry.
Fully autonomous air taxis are likely not going to show up until after 2030. In the beginning, they will likely fly only in regions where the weather is good most of the time. The autonomous car industry has already adopted this strategy, mostly deploying their technology in regions where the weather is dry and sunny. Soon, however, we’ll start seeing operations in “all-weather” scenarios and an increasing number of air vehicles within the same airspace.
There is one critical stepping stone on the way to fully autonomous passenger aircraft: the success of fully autonomous cargo drones. For light parcels we will see initial deployments in 2022 or 2023, followed by larger UAS capable of transporting heaver cargo in 2024-2025.
But whatever the timing, these are very exciting times. The aviation industry is witnessing a revolution with new vehicle manufacturers, new technologies and, likely, new applications we have not even dreamt of yet.
Learn more about Honeywell’s work in urban air mobility and unmanned aircraft at aerospace.honeywell.com/uam.
Alberto Speranzon is a Fellow within Honeywell Aerospace Advanced Technology. He received a Ph.D. in Electrical Engineering from the Royal Institute of Technology (KTH), Sweden in 2006. Since joining Honeywell, Alberto has been working on various aspects of autonomous systems for urban air mobility, leading such research areas as program manager and principal investigator. He is an IEEE Senior Member and a member of the Board of Governors of the IEEE Control Systems Society.
Nishita Rao is product marketing manager at SEMI.