Tag Archives: transportation

GPS Manipulation

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/11/gps_manipulatio.html

Long article on the manipulation of GPS in Shanghai. It seems not to be some Chinese military program, but ships who are stealing sand.

The Shanghai “crop circles,” which somehow spoof each vessel to a different false location, are something new. “I’m still puzzled by this,” says Humphreys. “I can’t get it to work out in the math. It’s an interesting mystery.” It’s also a mystery that raises the possibility of potentially deadly accidents.

“Captains and pilots have become very dependent on GPS, because it has been historically very reliable,” says Humphreys. “If it claims to be working, they rely on it and don’t double-check it all that much.”

On June 5 this year, the Run 5678, a river cargo ship, tried to overtake a smaller craft on the Huangpu, about five miles south of the Bund. The Run avoided the small ship but plowed right into the New Glory (Chinese name: Tong Yang Jingrui), a freighter heading north.

Boing Boing article.

Self-Driving Cars Learn About Road Hazards Through Augmented Reality

Post Syndicated from Yiheng Feng original https://spectrum.ieee.org/transportation/self-driving/selfdriving-cars-learn-about-road-hazards-through-augmented-reality

For decades, anyone who wanted to know whether a new car was safe to drive could simply put it through its paces, using tests established through trial and error. Such tests might investigate whether the car can take a sharp turn while keeping all four wheels on the road, brake to a stop over a short distance, or survive a collision with a wall while protecting its occupants.

But as cars take an ever greater part in driving themselves, such straightforward testing will no longer suffice. We will need to know whether the vehicle has enough intelligence to handle the same kind of driving conditions that humans have always had to manage. To do that, automotive safety-assurance testing has to become less like an obstacle course and more like an IQ test.

One obvious way to test the brains as well as the brawn of autonomous vehicles would be to put them on the road along with other traffic. This is necessary if only because the self-driving cars will have to share the road with the human-driven ones for many years to come. But road testing brings two concerns. First, the safety of all concerned can’t be guaranteed during the early stages of deployment; self-driving test cars have already been involved in fatal accidents. Second is the sheer scale that such direct testing would require.

That’s because most of the time, test vehicles will be driven under typical conditions, and everything will go as it normally does. Only in a tiny fraction of cases will things take a different turn. We call these edge cases, because they concern events that are at the edge of normal experience. Example: A truck loses a tire, which hops the median and careens into your lane, right in front of your car. Such edge cases typically involve a concurrence of failures that are hard to conceive of and are still harder to test for. This raises the question, How long must we road test a self-driving, connected vehicle before we can fairly claim that it is safe?

The answer to that question may never be truly known. What’s clear, though, is that we need other strategies to gauge the safety of self-driving cars. And the one we describe here—a mixture of physical vehicles and computer simulation—might prove to be the most effective way there is to evaluate self-driving cars.

A fatal crash occurs only once in about 160 million kilometers of driving, according to statistics compiled by the U.S. National Highway Traffic Safety Administration [PDF]. That’s a bit more than the distance from Earth to the sun (and 10 times as much as has been logged by the fleet of Google sibling Waymo, the company that has the most experience with self-driving cars). To travel that far, an autonomous car driving at highway speeds for 24 hours a day would need almost 200 years. It would take even longer to cover that distance on side streets, passing through intersections and maneuvering around parking lots. It might take a fleet of 500 cars 10 years to finish the job, and then you’d have to do it all over again for each new design.

Clearly, the industry must augment road testing with other strategies to bring out as many edge cases as possible. One method now in use is to test self-driving vehicles in closed test facilities where known edge cases can be staged again and again. Take, as an example, the difficulties posed by cars that run a red light at high speed. An intersection can be built as if it were a movie set, and self-driving cars can be given the task of crossing when the light turns green while at the same time avoiding vehicles that illegally cross in front of them.

While this approach is helpful, it also has limitations. Multiple vehicles are typically needed to simulate edge cases, and professional drivers may well have to pilot them. All this can be costly and difficult to coordinate. More important, no one can guarantee that the autonomous vehicle will work as desired, particularly during the early stages of such testing. If something goes wrong, a real crash could happen and damage the self-driving vehicle or even hurt people in other vehicles. Finally, no matter how ingenious the set designers may be, they cannot be expected to create a completely realistic model of the traffic environment. In real life, a tree’s shadow can confuse an autonomous car’s sensors and a radar reflection off a manhole cover can make the radar see a truck where none is present.

Computer simulation provides a way around the limitations of physical testing. Algorithms generate virtual vehicles and then move them around on a digital map that corresponds to a real-world road. If the data thus generated is then broadcast to an actual vehicle driving itself on the same road, the vehicle will interpret the data exactly as if it had come from its own sensors. Think of it as augmented reality tuned for use by a robot.

Although the physical test car is driving on empty roads, it “thinks” that it is surrounded by other vehicles. Meanwhile, it sends information that it is gathering—both from augmented reality and from its sensing of the real-world surroundings—back to the simulation platform. Real vehicles, simulated vehicles, and perhaps other simulated objects, such as pedestrians, can thus interact. In this way, a wide variety of scenarios can be tested in a safe and cost-effective way.

The idea for automotive augmented reality came to us by the back door: Engineers had already improved certain kinds of computer simulations by including real machines in them. As far back as 1999, Ford Motor Co. used measurements of an actual revving engine to supply data for a computer simulation of a power train. This hybrid simulation method was called hardware-in-the-loop, and engineers resorted to it because mimicking an engine in software can be very difficult. Knowing this history, it occurred to us that it would be possible to do the opposite—generate simulated vehicles as part of a virtual environment for testing actual cars.

In June 2017, we implemented an augmented-reality environment in Mcity, the world’s first full-scale test bed for autonomous vehicles. It occupies 32 acres on the North Campus of the University of Michigan, in Ann Arbor. Its 8 lane-kilometers (5 lane-miles) of roadway are arranged in sections having the attributes of a highway, a multilane arterial road, or an intersection.

Here’s how it works. The autonomous test car is equipped with an onboard device that can broadcast vehicle status, such as location, speed, acceleration, and heading, doing so every tenth of a second. It does this wirelessly, using dedicated short-range communications (DSRC), a standard similar to Wi-Fi that has been earmarked for mobile users. Roadside devices distributed around the testing facility receive this information and forward it to a traffic-simulation model, one that can simulate the testing facility by boiling it down to an equivalent network geometry that incorporates the actions of traffic signals. Once the computer model receives the test car’s information, it creates a virtual twin of that car. Then it updates the virtual car’s movements based on the movements of the real test car.

Feeding data from the real test vehicle into the computer simulation constitutes only half of the loop. We complete the other half by sending information about the various vehicles the computer has simulated to the test car. This is the essence of the augmented-reality environment. Every simulated vehicle also generates vehicle-status messages at a frequency of 10 hertz, which we forward to the roadside devices, which in turn broadcast it in real time. When the real test car receives that data, its vehicle-control system uses it to “see” all the virtual vehicles. To the car, these simulated entities are indistinguishable from the real thing.

By having vehicles pass messages through the roadside devices—that is, by substituting “vehicle-to-infrastructure” connections for direct “vehicle-to-vehicle” links—real vehicles and virtual vehicles can sense one another and interact accordingly. In the same fashion, traffic-signal status is also synchronized between the real and the simulated worlds. That way, real and virtual vehicles can each “look” at a given light and see whether it is green or red.

The status messages passed between real and simulated worlds include, of course, vehicle positions. This allows actual vehicles to be mapped onto the simulated road network, and simulated vehicles to be mapped into the actual road network. The positions of actual vehicles are represented with GPS coordinates—latitude, longitude, and elevation—and those of simulated vehicles with local coordinates—x, y, and z. An algorithm transforms one system of coordinates into the other.

But that mathematical transformation isn’t all that’s needed. There are small GPS and map errors, and they sometimes prevent a GPS position, forwarded from the actual test car and translated to the local system of coordinates, from appearing on a simulated road. We correct these errors with a separate mapping algorithm. Also, when the test car stops, we must lock it in place in the simulation, so that fluctuations in its GPS coordinates do not cause it to drift [PDF] out of position in the simulation.

Everything here depends on wireless communication. To ensure that it was reliable, we installed four roadside radios in Mcity, enough to cover the entire testing facility. The DSRC wireless standard, which operates in the 5.9-gigahertz band, gives us high data-transmission rates and very low latency. These are critical to safety at high speeds and during stop-on-a-dime maneuvers. DSRC is in wide use in Japan and Europe; it hasn’t yet gained much traction in the United States, although Cadillac is now equipping some of its cars with DSRC devices.

Whether DSRC will be the way cars communicate with one another is uncertain, though. Some people have argued that cellular communications, particularly in the coming 5G implementation, might offer equally low latency with a greater range. Whichever standard wins out, the communications protocols used in our system can easily be adapted to it.

We expect that the software framework we used to build our system will also endure, at least for a few years. We constructed our simulation with PTV Vissim, a commercial package developed in Germany to model traffic flow “microscopically,” that is, by simulating the behavior of each individual vehicle.

One thing that can be expected to change is the test vehicle, as other companies begin to use our system to put their own autonomous vehicles through their paces. For now, our one test vehicle is a Lincoln MKZ Hybrid, which is equipped with DSRC and thus fully connected. Drive-by-wire controls that we added to the car allow software to command the steering wheel, throttle, brake, and transmission. The car also carries multiple radars, lidars, cameras, and a GPS receiver with real-time kinematic positioning, which improves resolution by referring to a signal from a ground-based radio station.

We have implemented two testing scenarios. In the first one, the system generates a virtual train and projects it into the augmented reality perceived by the test car as the train approaches a mock-up of a rail crossing in Mcity. The point is to see whether the test car can stop in time and then wait for the train to pass. We also throw in other virtual vehicles, such as cars that follow the test car. These strings of cars—actual and virtual—can be formally arranged convoys (known as platoons) or ad hoc arrangements: perhaps cars queuing to get onto an entry ramp.

The second, more complicated testing scenario involves the case we mentioned earlier—running a red light. In the United States, cars running red lights cause more than a quarter of all the fatalities that occur at an intersection, according to the American Automobile Association. This scenario serves two purposes: to see how the test car reacts to traffic signals and also how it reacts to red-light-running scofflaws.

Our test car should be able to tell whether the signal is red or green and decide accordingly whether to stop or to go. It should also be able to notice that the simulated red-light runner is coming, predict its trajectory, and calculate when and where the test car might be when it crosses that trajectory. The test car ought to be able to do all these things well enough to avoid a collision.

Because the computer running the simulation can fully control the actions of the red-light runner, it can generate a wide variety of testing parameters in successive iterations of the experiment. This is precisely the sort of thing a computer can do much more accurately than any human driver. And of course, the entire experiment can be done in complete safety because the lawbreaker is merely a virtual car.

There is a lot more of this kind of edge-case simulation that can be done. For example, we can use the augmented-reality environment to evaluate the ability of test cars to handle complex driving situations, like turning left from a stop sign onto a major highway. The vehicle needs to seek gaps in traffic going in both directions, meanwhile watching for pedestrians who may cross at the sign. The car can decide to make a stop in the median first, or instead simply drive straight into the desired lane. This involves a decision-making process of several stages, all while taking into account the actions of a number of other vehicles (including predicting how they will react to the test car’s actions).

Another example involves maneuvers at roundabouts—entering, exiting, and negotiation for position with other cars—without help from a traffic signal. Here the test car needs to predict what other vehicles will do, decide on an acceptable gap to use to merge, and watch for aggressive vehicles. We can also construct augmented-reality scenarios with bicyclists, pedestrians, and other road users, such as farm machinery. The less predictable such alternative actors are, the more intelligence the self-driving car will need.

Ultimately, we would like to put together a large library of test scenarios including edge cases, then use the augmented-reality testing environment to run the tests repeatedly. We are now building up such a library with data scoured from reports of actual crashes, together with observations by sensor-laden vehicles of how people drive when they don’t know they’re part of an experiment. By putting together disparate edge conditions, we expect to create artificial edge cases that are particularly challenging for the software running in self-driving cars.

Thus armed, we ought to be able to see just how safe a given autonomous car is without having to drive it to the sun and back.

This article appears in the December 2019 print issue as “Augmented Reality for Robocars.”

About the Authors

Henry X. Liu is a professor of civil and environmental engineering at the University of Michigan, Ann Arbor, and a research professor at the University of Michigan Transportation Research Institute. Yiheng Feng is an assistant research scientist in the university’s Engineering Systems Group.

Should Yellow Traffic Lights Last Longer?

Post Syndicated from Michelle V. Rafter original https://spectrum.ieee.org/cars-that-think/transportation/safety/transportation-engineers-consider-whether-yellow-traffic-lights-should-last-longer

Mats Järlström’s six-year crusade to make yellow traffic lights safer for drivers could finally be paying off.

In mid-October, an Institute of Transportation Engineers appeals panel agreed with the Oregon consultant’s claims that a long-standing, widely used formula for setting the timing of yellow traffic lights doesn’t adequately account for the extra time a driver might need to safely and comfortably make a turn through an intersection.

The three-person ITE panel findings [PDF] didn’t suggest what the timing should be. A separate ITE committee will propose recommended practice for so-called “dilemma-zone situations for left-turn and right-turn movements” that the organization’s board must then approve. According to ITE Chief Technical Director Jeff Lindley, that process is underway and ITE could publish guidelines during the first quarter of 2020.

“It’s a historic moment,” Järlström said of the appeal panel’s decision. “This is a very conservative area of technology. There are many traffic signals that need to be changed. We want to change it so all of them are consistent, not only in the U.S. but through the world.”

NTSB Investigation of Fatal Driverless Car Accident

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/11/ntsb_investigat.html

Autonomous systems are going to have to do much better than this.

The Uber car that hit and killed Elaine Herzberg in Tempe, Ariz., in March 2018 could not recognize all pedestrians, and was being driven by an operator likely distracted by streaming video, according to documents released by the U.S. National Transportation Safety Board (NTSB) this week.

But while the technical failures and omissions in Uber’s self-driving car program are shocking, the NTSB investigation also highlights safety failures that include the vehicle operator’s lapses, lax corporate governance of the project, and limited public oversight.

The details of what happened in the seconds before the collision are worth reading. They describe a cascading series of issues that led to the collision and the fatality.

As computers continue to become part of things, and affect the world in a direct physical manner, this kind of thing will become even more important.

Driving Tests Coming for Autonomous Cars

Post Syndicated from Jeff Hecht original https://spectrum.ieee.org/cars-that-think/transportation/self-driving/driving-tests-coming-for-autonomous-cars

The three laws of robotic safety in Isaac Asimov’s science fiction stories seem simple and straightforward, but the ways the fictional tales play out reveal unexpected complexities. Writers of safety standards for self-driving cars express their goals in similarly simple terms. But several groups now developing standards for how autonomous vehicles will interact with humans and with each other face real-world issues much more complex than science fiction.

Advocates of autonomous cars claim that turning the wheel over to robots could slash the horrific toll of 1.3 million people killed around the world each year by motor vehicles. Yet the public has become wary because robotic cars also can kill. Documents released last week by the U.S. National Transportation Safety Board blame the March 2018 death of an Arizona pedestrian struck by a self-driving Uber on safety failures by the car’s safety driver, the company, and the state of Arizona. Even less-deadly safety failures are damning, like the incident where a Tesla in Autopilot mode wasn’t smart enough to avoid crashing into a stopped fire engine whose warning lights were flashing.

Safety standards for autonomous vehicles “are absolutely critical” for public acceptance of the new technology, says Greg McGuire, associate director of the Mcity autonomous vehicle testing lab at the University of Michigan. “Without them, how do we know that [self-driving cars] are safe, and how do we gain public trust?” Earning that trust requires developing standards through an open process that the public can scrutinize, and may even require government regulation, he adds.

Companies developing autonomous technology have taken notice. Earlier this year, representatives from 11 companies including Aptiv, Audi, Baidu, BMW, Daimler, Infineon, Intel, and Volkswagen collaborated to write a wide-ranging whitepaper titled “Safety First for Automated Driving.” They urged designing safety features into the automated driving function, and using heightened cybersecurity to assure the integrity of vital data including the locations, movement, and identification of other objects in the vehicle environment. They also urged validating and verifying the performance of robotic functions in a wide range of operating conditions.

On 7 November, the International Telecommunications Union announced the formation of a focus group called AI for Autonomous and Assisted Driving. It’s aim: to develop performance standards for artificial intelligence (AI) systems that control self-driving cars. (The ITU has come a long way since its 1865 founding as the International Telegraph Union, with a mandate to standardize the operations of telegraph services.)

ITU intends the standards to be “an equivalent of a Turing Test for AI on our roads,” says focus group chairman Bryn Balcombe of the Autonomous Drivers Alliance. A computer passes a Turing Test if it can fool a person into thinking it’s a human. The AI test is vital, he says, to assure that human drivers and the AI behind self-driving cars understand each other and predict each other’s behaviors and risks.

A planning document says AI development should match public expectations so:

• AI never engages in careless, dangerous, or reckless driving behavior

• AI remains aware, willing, and able to avoid collisions at all times

• AI meets or exceeds the performance of a competent, careful human driver

 

These broad goals for automotive AI algorithms resemble Asimov’s laws, insofar as they bar hurting humans and demand that they obey human commands and protect their own existence. But the ITU document includes a list of 15 “deliverables” including developing specifications for evaluating AIs and drafting technical reports needed for validating AI performance on the road.

A central issue is convincing the public to entrust the privilege of driving—a potentially life-and-death activity—to a technology which has suffered embarrassing failures like the misidentification of minorities that led San Francisco to ban the use of facial recognition by police and city agencies.

Testing how well an AI can drive is vastly complex, says McGuire. Human adaptability makes us fairly good drivers. “We’re not perfect, but we are very good at it, with typically a hundred million miles between fatal traffic crashes,” he says. Racking up that much distance in real-world testing is impractical—and it is but a fraction of the billions of vehicle miles needed for statistical significance. That’s a big reason developers have turned to simulations. Computers can help them run up virtual mileage needed to find potential safety flaws that might arise only rare situations, like in a snowstorm or heavy rain, or on a road under construction.

It’s not enough for an automotive AI to assure the vehicle’s safety, says McGuire. “The vehicle has to work in a way that humans would understand.” Self-driving cars have been rear-ended when they stopped in situations where most humans would not have expected a driver to stop. And a truck can be perfectly safe even when close enough to unnerve a bicyclist.

Other groups are also developing standards for robotic vehicles. ITU is covering both automated driver assistance and fully autonomous vehicles. Underwriters Laboratories is working on a standard for fully-autonomous vehicles. The Automated Vehicle Safety Consortium, a group including auto companies, plus Lyft, Uber, and SAE International (formerly the Society of Automotive Engineers) is developing safety principles for SAE Level 4 and 5 autonomous vehicles. The BSI Group (formerly the British Institute of Standards) developed a strategy for British standards for connected and autonomous vehicles and is now working on the standards themselves.

How long will it take to develop standards? “This is a research process,” says McGuire. “It takes as long as it takes” to establish public trust and social benefit. In the near term, Mcity has teamed with the city of Detroit, the U.S. Department of Transportation, and Verizon to test autonomous vehicles for transporting the elderly on city streets. But he says the field “needs to be a living thing that continues to evolve” over a longer period.

Meet the VoloCity

Post Syndicated from LEMO original https://spectrum.ieee.org/transportation/alternative-transportation/meet-the-volocity

Presented in last August, the VoloCity is the latest solution proposed by Volocopter, the first designed for actual commercial use. All of its features (number of seats, range, speed…) are related to its mission: to be an inner-city flying taxi and nothing else.

The choice of simplicity, for instance (such as direct-drive motors and fixed pitch rotors) makes the solution less costly to manufacture, more reliable (less expensive maintenance and easier to certify), lighter, so more economical and less noisy. Everything is closely linked.

The wide span and a large number of battery-powered engines and rotors (18 of each) reduce the noise level and generate a frequency that is softer and more pleasant on the ear. It also improves safety: the VoloCity is capable of flying even if several engines are inoperative. The aircraft will fly at “only” 110kmph, which is safer (better collision avoidance) and less noisy than rapid eVTOLs.

One passenger and the pilot have access and are seated comfortably (Volocopter’s analyses show that the large majority of intra-urban passengers travel alone). There is space for hand luggages, air conditioning, silence and a stunning view. Once the regulations will authorise it, the VoloCity will also be able to fly autonomously.

The VoloCity embarks 9 Lithium-ion exchangeable battery packs. These are recharged on the vertiports. Whenever the aircraft lands in between two flights, batteries can be changed in five minutes to fresh batteries and can take off. Its 35km range makes it possible to connect the most popular destinations (city centres, airports, business centres …).

Vertical take-off and landing, so no need for wheels nor retractable landing gear. The skids are part of the rationalisation process to reduce weight, breakdowns, production and maintenance costs. Ground operations are ensured by conveyor belts or platforms.

Key numbers

DIAMETER OF THE ROTOR RIM (INCLUDING ROTORS)

11.3 M

DIAMETER OF A SINGLE ROTOR

2.3 M

HEIGHT

2.5 M

NUMBER OF MOTORS & ROTORS

18

SEATS

2

PAYLOAD

200 KG

SPEED

110 KM/H

RANGE

35 KM

LEMO-The Sky Is the Limit

Post Syndicated from LEMO original https://spectrum.ieee.org/transportation/alternative-transportation/lemo-the-sky-is-the-limit

Eight years ago, when Alex Zosel

cofounded
his start-up, the idea of electric air taxis seemed completely crazy. Today, however, some cities are already testing them.

Volocopter’s
founder explains why urban transport should turn to the sky and how his

start-up
wants to become more than just an aircraft manufacturer.

Alex Zosel, how will urban mobility evolve?

Urban mobility will undergo a huge transformation in the next 20 to 30 years. Following an already perceptible trend, private means of transport will be increasingly chosen for environmental reasons (efficiency,

low
emission…) rather than for ego-boosting (power, speed…). Many new devices will appear or co-exist: we will see cable cars, e-scooters, conveyor belts, streets dedicated to fast bikes, etc. This evolution will certainly also depend on the evolution of society and work. New technologies could very well reduce working hours, the workforce, increase

teleworking
– all this will have an impact on traffic.

How does Volocopter position itself in this evolution?

We believe that in

megacities
with saturated roads and major traffic flow issues, the only viable solution will be the sky! We want to be one of the big players, one of the drivers in urban air mobility. This is why we talk a lot about the transformation of cities with architects and infrastructure specialists. On the one hand, we are thinking in the long term – imagining a future with thousands of aircraft in the city skies. On the other hand, in the short term, the first solutions, the first missions: where are the current needs for flying taxis? Where would they really make a positive impact in terms of mobility? It is on these short-term solutions that we will gradually build up the ecosystem of urban air mobility.

Which missions are you focusing on first?

On combating bottlenecks in

megacities
. They represent a highly critical situation that flying taxis could rapidly improve. For instance, by transporting people between airports and city

centres
, or between tourist attractions – an issue in a large number of cities. It wouldn’t be necessary to transport everyone through the skies, but relieving some roads could rapidly help the whole system to flow better.

Short hops and no long-distance commuting, unlike the market targeted by the Uber Elevate system?

We want to fly between cities one day, but we believe that flying in inner cities would fix a more urgent problem. There is also a technological reason for this choice: we want a reliable solution now, using tried and tested, already existing batteries, which do not provide for flying over long distances. The limited range of our aircraft – 35km – is not a problem: our analysis shows that 90% of the

megacities
we are targeting have a major airport within 30km of the city

centre
. Therefore, millions of journeys are within our reach.

Your targeted mission also influences some other design aspects of your solution…

Our aircraft is fully designed for this mission. Its extensive propulsion

system including
18 engines, for example, makes it extremely quiet and reliable (it can fly even if several engines aren’t functioning). Noise and safety are the major criteria for being approved in cities. Our speed of 110km/h is also important: flying twice as fast would be terrifying, dangerous, much

noisier
and, considering the short distances, it wouldn’t really be a time-saver.

Are

eVTOLs
appropriate from the environmental point of view?

They are zero emission which is already great. Obviously, they require more energy for taking off than a car on a flat surface. On the other hand, there is no need to build roads, bridges, tunnels – all these costly infrastructures which then generate huge maintenance costs. If you add the impact of these infrastructures to the impact of vehicles, it is clearly more efficient to go up into the sky! Slowing down or decreasing the development of the road network also makes it possible to save or reintroduce nature into cities.

Launching innovative new means of transport is often hindered by the rules which have to be created or adopted…

Integrating this aspect is part of Volocopter’s DNA: we have been discussing with the authorities for over 7 years about how to integrate

eVTOLs
. In general, the authorities are fairly open: they want to make flights safer, which is exactly what sensors and automated systems built in the heart of

eVTOLs
provide for. We have been partners of the European Aviation Safety Agency (EASA) for two years. We have helped to integrate air taxis flying in inner cities – which could also fly autonomously – in these regulations. Similarly, we have been working with the US Federal Aviation Administration, whose processes are somewhat more complex and extensive. The “Special Condition for VTOL” of the EASA was presented in early July and we are very proud to have contributed to it. The

VoloCity
, our new aircraft, will be the first commercially licensed Volocopter accredited to these high standards and requirements. Simply put, the safety requirements are that flying taxis will have to be as safe as

commer
– cial aircraft. They shall be.

How about integration in air traffic?

This is more of a local thing: the selection of routes, weather conditions,

altitudes
, coexistence with drones and helicopters… Again, we have a lot of experience, namely thanks to our cooperation with the Dubai government’s Roads and Transport Authority. They asked us to participate in a project for autonomous flying devices in the airspace over their city. The feasibility of such a service was proven after a successful test with our Volocopter 2X in September 2017. This was the first-ever public flight of an autonomous urban air taxi. We are currently working with Singapore authorities, who are also interested.

So, it is

city
by city that Volocopter develops its projects?

Yes. Many cities have already asked us to become technical partners to advise on how to develop urban air mobility infrastructures and services. Therefore, they are motivated and very much involved. They dedicate the necessary resources and it can quickly go forward – we have no fear of not getting approvals. We start off with a first route from A to B, as a first step. Whatever happens afterwards, Volocopter can always learn a lot and it helps us to extend our offer.

Do you also work with airport operators, such as Skyport?

Yes, indeed. Not only for the integration of air taxis in their territory, but also for integrating our services with theirs. We could

imagine for


instance that
check-in for an international flight would be possible upon embarking at a

vertiport
in the city. It would relieve traffic in their departure area.

The way you describe it, it would seem that Volocopter will provide services, rather than just aircraft…

You are exactly right. From the beginning, we never wanted to be a manufacturer of vehicles. We want to be a mobility provider, to sell the tickets. Everything beneath that is based on very strong local partnerships, with the authorities, real estate players, airport operators or helicopter companies, whose launch pads we could use. In a nutshell, we have to associate with all of those to ensure the smooth integration of flying taxi services in a city. At the same time, our

vertiport
projects are not exclusive: other

eVTOLs
could also use them.

Volocopter
could even buy or rent aircraft from other manufacturers. We are really open – we wouldn’t be able to create an air-mobility system if we were

exclusive
. Our

eVTOLs
have a head-start, so we will start with them to be the first on the market. Then we will see how to increase the scale.

When will these air taxis be part of our daily

lives
?

They have been part of my daily life for years, since I am one of the test pilots! I think the first regular route will be opened in 2 to 3 years. The speed of their implementation will depend on several factors, including the production capacity of

eVTOL
manufacturers! By the time I retire in 13 years, we should have a system of flying taxis in at least 10 mega– cities. If not, well, in that case I will have to retire five years late!

FROM A SMALL DRONE TO FLYING TAXIS

In 2010, Stephan Wolf, an industrial automation software development specialist, bought a

quadcopter
for his son. It was one of the first ones that even a child could easily control. Impressed

by
its

manoeuvrability
, stability and easy handling, Wolf immediately started dreaming: what if it was scaled up to transport people? After some research and calculations, he developed a project. He contacted Alex Zosel, a childhood friend, who became an entrepreneur. A passionate hobby pilot and

paragliding
teacher himself, he didn’t hesitate for a second. The two men and Thomas Senkel founded e-

Volo
in 2011. The startup, located near Karlsruhe, in Germany, subsequently became Volocopter.

The VC1 proved in October 2011 that an electric

multicopter
was capable of taking off vertically with a passenger. Then, the first prototype, the VC200, was suitable for carrying 2 people. It made its first manned flight – with Zosel himself as the pilot – in March 2016. The VC200 had been financed through an instantly successful online crowdfunding (500,000 euros in 2 hours and 35 minutes!).

This model also marked a major milestone: it got a license to fly all over the country from the German authorities. The Volocopter 2X, first displayed in 2017, has since been built into a miniseries for extensive test flights in manned, unmanned (which includes remote-controlled), and automated (following a preset route with no human interference) configurations.

In only 8 years, the seemingly absurd idea of a “drone for humans” has become a certified aircraft. In the meantime,

eVTOL
aircraft (electric vertical take-off and landing) have generated an industrial race (see page 08).

Volocopter
, a visionary? In any case, that’s how the World Economic Forum seems to consider the startup, as they were among the winners of the 2019 “Technology Pioneer”

award which


recognises
companies that are “poised to have a significant impact on business and society”.

Many investors, large and small, have contributed so far to financing Volocopter, including Daimler and Intel. Lukasz Gadowski, a German serial entrepreneur and founder of Circ (formerly known as Flash) micro mobility electric scooter, is one of the personalities who have been convinced. Last September, Geely, the Chinese

Group owner
of

Volvo
and Lotus cars, as well as Terrafugia (hybrid cars/aircraft) headed the round of private funding amounting to 50 million euros for the C Series.

This summer, Volocopter presented its next model, the VoloCity (for all the details, see page 14). Slightly larger and twice as heavy and powerful as the 2X, this

multicopter
is not a prototype, but rather the first to be marketed.

The startup, with a current staff of about 150, aims at becoming a mobility provider (see the interview with Alex Zosel, page 10). It has been working for several years already with Dubai and Singapore, who have requested a feasibility study on such services in their skies.

At the end of October, Volocopter conducted a public proof of concept flight in Singapore. It also displayed a

vertiport
prototype there, not for launching the

eVTOLs
(the

authorisations
are missing), but for testing and improving various services (reception, booking, check-in). This proof of concept on a one-to-one scale is another step towards the imminent launching of a brand new type of urban mobility.

MEET THE VOLOCITY

Presented in last August, the

VoloCity
is the latest solution proposed by Volocopter, the first designed for actual commercial use. All of its features (number of seats, range, speed…) are related to its mission:

to be
an inner-city flying taxi and nothing else.

The choice of simplicity, for instance (such as direct-drive motors and fixed pitch rotors) makes the solution less costly to manufacture, more reliable (less expensive maintenance and easier to certify), lighter, so more economical and less noisy. Everything is closely linked.

The wide span and a large number of battery-powered engines and rotors (18 of each) reduce the noise level and generate a frequency that is softer and more pleasant on the ear. It also improves safety: the

VoloCity
is capable of flying even if several engines are inoperative. The aircraft will fly at “only” 110kmph, which is safer (better collision avoidance) and less noisy than rapid

eVTOLs
.

One passenger and the pilot have access and are seated comfortably (Volocopter’s analyses show that the large majority of intra-urban passengers travel alone). There is space for hand luggages, air conditioning, silence and a stunning view. Once the regulations will

authorise
it, the

VoloCity
will also be able to fly autonomously.

The

VoloCity
embarks 9 Lithium-ion exchangeable battery packs. These are recharged on the

vertiports
. Whenever the aircraft lands in between two flights, batteries can be changed in five minutes to fresh batteries and can take off. Its 35km range makes it possible to connect the most popular destinations (city

centres
, airports, business

centres



).

Vertical take-off and landing, so no need for wheels nor retractable landing gear. The skids are part of the

rationalisation
process to reduce weight, breakdowns, production and maintenance costs. Ground operations are ensured by conveyor belts or platforms.

Key numbers

DIAMETER OF THE ROTOR RIM (INCLUDING ROTORS)

11.3 M

DIAMETER OF A SINGLE ROTOR

2.3 M

HEIGHT

2.5 M

NUMBER OF MOTORS & ROTORS

18

SEATS

2

PAYLOAD

200 KG

SPEED

110 KM/H

RANGE

35 KM

LEMO-The Sky Is the Limit

Post Syndicated from LEMO original https://spectrum.ieee.org/transportation/alternative-transportation/lemothe-sky-is-the-limit

Eight years ago, when Alex Zosel cofounded his start-up, the idea of electric air taxis seemed completely crazy. Today, however, some cities are already testing them. Volocopter’s founder explains why urban transport should turn to the sky and how his start-up wants to become more than just an aircraft manufacturer. 

Alex Zosel, how will urban mobility evolve?

Urban mobility will undergo a huge transformation in the next 20 to 30 years. Following an already perceptible trend, private means of transport will be increasingly chosen for environmental reasons (efficiency, low emission…) rather than for ego-boosting (power, speed…). Many new devices will appear or co-exist: we will see cable cars, e-scooters, conveyor belts, streets dedicated to fast bikes, etc. This evolution will certainly also depend on the evolution of society and work. New technologies could very well reduce working hours, the workforce, increase teleworking – all this will have an impact on traffic.

How does Volocopter position itself in this evolution?

We believe that in megacities with saturated roads and major traffic flow issues, the only viable solution will be the sky! We want to be one of the big players, one of the drivers in urban air mobility. This is why we talk a lot about the transformation of cities with architects and infrastructure specialists. On the one hand, we are thinking in the long term – imagining a future with thousands of aircraft in the city skies. On the other hand, in the short term, the first solutions, the first missions: where are the current needs for flying taxis? Where would they really make a positive impact in terms of mobility? It is on these short-term solutions that we will gradually build up the ecosystem of urban air mobility.

Which missions are you focusing on first?

On combating bottlenecks in megacities. They represent a highly critical situation that flying taxis could rapidly improve. For instance, by transporting people between airports and city centres, or between tourist attractions – an issue in a large number of cities. It wouldn’t be necessary to transport everyone through the skies, but relieving some roads could rapidly help the whole system to flow better.

Short hops and no long-distance commuting, unlike the market targeted by the Uber Elevate system?

We want to fly between cities one day, but we believe that flying in inner cities would fix a more urgent problem. There is also a technological reason for this choice: we want a reliable solution now, using tried and tested, already existing batteries, which do not provide for flying over long distances. The limited range of our aircraft – 35km – is not a problem: our analysis shows that 90% of the megacities we are targeting have a major airport within 30km of the city centre. Therefore, millions of journeys are within our reach.

Your targeted mission also influences some other design aspects of your solution…

Our aircraft is fully designed for this mission. Its extensive propulsion system including 18 engines, for example, makes it extremely quiet and reliable (it can fly even if several engines aren’t functioning). Noise and safety are the major criteria for being approved in cities. Our speed of 110km/h is also important: flying twice as fast would be terrifying, dangerous, much noisier and, considering the short distances, it wouldn’t really be a time-saver.

Are eVTOLs appropriate from the environmental point of view?

They are zero emission which is already great. Obviously, they require more energy for taking off than a car on a flat surface. On the other hand, there is no need to build roads, bridges, tunnels – all these costly infrastructures which then generate huge maintenance costs. If you add the impact of these infrastructures to the impact of vehicles, it is clearly more efficient to go up into the sky! Slowing down or decreasing the development of the road network also makes it possible to save or reintroduce nature into cities.

Launching innovative new means of transport is often hindered by the rules which have to be created or adopted…

Integrating this aspect is part of Volocopter’s DNA: we have been discussing with the authorities for over 7 years about how to integrate eVTOLs. In general, the authorities are fairly open: they want to make flights safer, which is exactly what sensors and automated systems built in the heart of eVTOLs provide for. We have been partners of the European Aviation Safety Agency (EASA) for two years. We have helped to integrate air taxis flying in inner cities – which could also fly autonomously – in these regulations. Similarly, we have been working with the US Federal Aviation Administration, whose processes are somewhat more complex and extensive. The “Special Condition for VTOL” of the EASA was presented in early July and we are very proud to have contributed to it. The VoloCity, our new aircraft, will be the first commercially licensed Volocopter accredited to these high standards and requirements. Simply put, the safety requirements are that flying taxis will have to be as safe as commer- cial aircraft. They shall be.

How about integration in air traffic?

This is more of a local thing: the selection of routes, weather conditions, altitudes, coexistence with drones and helicopters… Again, we have a lot of experience, namely thanks to our cooperation with the Dubai government’s Roads and Transport Authority. They asked us to participate in a project for autonomous flying devices in the airspace over their city. The feasibility of such a service was proven after a successful test with our Volocopter 2X in September 2017. This was the first-ever public flight of an autonomous urban air taxi. We are currently working with Singapore authorities, who are also interested.

So, it is city by city that Volocopter develops its projects?

Yes. Many cities have already asked us to become technical partners to advise on how to develop urban air mobility infrastructures and services. Therefore, they are motivated and very much involved. They dedicate the necessary resources and it can quickly go forward – we have no fear of not getting approvals. We start off with a first route from A to B, as a first step. Whatever happens afterwards, Volocopter can always learn a lot and it helps us to extend our offer.

Do you also work with airport operators, such as Skyport?

Yes, indeed. Not only for the integration of air taxis in their territory, but also for integrating our services with theirs. We could imagine for instance that check-in for an international flight would be possible upon embarking at a vertiport in the city. It would relieve traffic in their departure area.

The way you describe it, it would seem that Volocopter will provide services, rather than just aircraft…

You are exactly right. From the beginning, we never wanted to be a manufacturer of vehicles. We want to be a mobility provider, to sell the tickets. Everything beneath that is based on very strong local partnerships, with the authorities, real estate players, airport operators or helicopter companies, whose launch pads we could use. In a nutshell, we have to associate with all of those to ensure the smooth integration of flying taxi services in a city. At the same time, our vertiport projects are not exclusive: other eVTOLs could also use them. Volocopter could even buy or rent aircraft from other manufacturers. We are really open – we wouldn’t be able to create an air-mobility system if we were exclusive. Our eVTOLs have a head-start, so we will start with them to be the first on the market. Then we will see how to increase the scale.

When will these air taxis be part of our daily lives?

They have been part of my daily life for years, since I am one of the test pilots! I think the first regular route will be opened in 2 to 3 years. The speed of their implementation will depend on several factors, including the production capacity of eVTOL manufacturers! By the time I retire in 13 years, we should have a system of flying taxis in at least 10 mega– cities. If not, well, in that case I will have to retire five years late!

NTSB Investigation Into Deadly Uber Self-Driving Car Crash Reveals Lax Attitude Toward Safety

Post Syndicated from Mark Harris original https://spectrum.ieee.org/cars-that-think/transportation/self-driving/ntsb-investigation-into-deadly-uber-selfdriving-car-crash-reveals-lax-attitude-toward-safety

The Uber car that hit and killed Elaine Herzberg in Tempe, Ariz., in March 2018 could not recognize all pedestrians, and was being driven by an operator likely distracted by streaming video, according to documents released by the U.S. National Transportation Safety Board (NTSB) this week.

But while the technical failures and omissions in Uber’s self-driving car program are shocking, the NTSB investigation also highlights safety failures that include the vehicle operator’s lapses, lax corporate governance of the project, and limited public oversight.

This week, the NTSB released over 400 pages ahead of a 19 November meeting aimed at determining the official cause of the accident and reporting on its conclusions. The Board’s technical review of Uber’s autonomous vehicle technology reveals a cascade of poor design decisions that led to the car being unable to properly process and respond to Herzberg’s presence as she crossed the roadway with her bicycle.

A radar on the modified Volvo XC90 SUV first detected Herzberg roughly six seconds before the impact, followed quickly by the car’s laser-ranging lidar. However, the car’s self-driving system did not have the capability to classify an object as a pedestrian unless they were near a crosswalk.

For the next five seconds, the system alternated between classifying Herzberg as a vehicle, a bike and an unknown object. Each inaccurate classification had dangerous consequences. When the car thought Herzberg a vehicle or bicycle, it assumed she would be travelling in the same direction as the Uber vehicle but in the neighboring lane. When it classified her as an unknown object, it assumed she was static.

Worse still, each time the classification flipped, the car treated her as a brand new object. That meant it could not track her previous trajectory and calculate that a collision was likely, and thus did not even slow down. Tragically, Volvo’s own City Safety automatic braking system had been disabled because its radars could have interfered with Uber’s self-driving sensors.

By the time the XC90 was just a second away from Herzberg, the car finally realized that whatever was in front of it could not be avoided. At this point, it could have still slammed on the brakes to mitigate the impact. Instead, a system called “action suppression” kicked in.

This was a feature Uber engineers had implemented to avoid unnecessary extreme maneuvers in response to false alarms. It suppressed any planned braking for a full second, while simultaneously alerting and handing control back to its human safety driver. But it was too late. The driver began braking after the car had already hit Herzberg. She was thrown 23 meters (75 feet) by the impact and died of her injuries at the scene.

Four days after the crash, at the same time of night, Tempe police carried out a rather macabre re-enactment. While an officer dressed as Herzberg stood with a bicycle at the spot she was killed, another drove the actual crash vehicle slowly towards her. The driver was able to see the officer from at least 194 meters (638 feet) away.

Key duties for Uber’s 254 human safety drivers in Tempe were actively monitoring the self-driving technology and the road ahead. In fact, recordings from cameras in the crash vehicle show that the driver spent much of the ill-fated trip looking at something placed near the vehicle’s center console, and occasionally yawning or singing. The cameras show that she was looking away from the road for at least five seconds directly before the collision.

Police investigators later established that the driver had likely been streaming a television show on her personal smartphone. Prosecutors are reportedly still considering criminal charges against her.

Uber’s Tempe facility, nicknamed “Ghost Town,” did have strict prohibitions against using drugs, alcohol or mobile devices while driving. The company also had a policy of spot-checking logs and in-dash camera footage on a random basis. However, Uber was unable to supply NTSB investigators with documents or logs that revealed if and when phone checks were performed. The company also admitted that it had never carried out any drug checks.

Originally, the company had required two safety drivers in its cars at all times, with operators encouraged to report colleagues who violated its safety rules. In October 2017, it switched to having just one.

The investigation also revealed that Uber didn’t have a comprehensive policy on vigilance and fatigue. In fact, the NTSB found that Uber’s self-driving car division “did not have a standalone operational safety division or safety manager. Additionally, [it] did not have a formal safety plan, a standardized operations procedure (SOP) or guiding document for safety.”

Instead, engineers and drivers were encouraged to follow Uber’s core values or norms, which include phrases such as: “We have a bias for action and accountability”; “We look for the toughest challenges, and we push”; and, “Sometimes we fail, but failure makes us smarter.”

NTSB investigators found that state of Arizona had a similarly relaxed attitude to safety. A 2015 executive order from governor Doug Ducey established a Self-Driving Vehicle Oversight Committee. That committee met only twice, with one of its representatives telling NTSB investigators that “the committee decided that many of the [laws enacted in other states] stifled innovation and did not substantially increase safety. Further, it felt that as long as the companies were abiding by the executive order and existing statutes, further actions were unnecessary.”

When investigators inquired whether the committee, the Arizona Department of Transportation, or the Arizona Department of Public Safety had sought any information from autonomous driving companies to monitor the safety of their operations, they were told that none had been collected.

As it turns out, the fatal collision was far from the first crash that Uber’s 40 self-driving cars in Tempe had been involved in. Between September 2016 and March 2018, the NTSB learned there had been 37 other crashes and incidents involving Uber’s test vehicles in autonomous mode. Most were minor rear-end fender-benders, but on one occasion, a test vehicle drove into a bicycle lane bollard. Another time, a safety driver had been forced to take control of the car to avoid a head-on collision. The result: the car struck a parked vehicle.

An Electric Motor That Works in Any Classic Car

Post Syndicated from Lawrence Ulrich original https://spectrum.ieee.org/cars-that-think/transportation/advanced-cars/an-electric-motor-that-works-in-any-classic-car

Anyone who’s owned a vintage car can tell you—and boy, will they tell you—how much time, money, and maintenance is required to keep their baby running. And don’t forget the gasoline, garage oil puddles, or tailpipe pollution involved. 

A California startup may have the answer: A plug-and-play innovative motor to convert that finicky old gas-guzzler into an  electric car. Eric Hutchison and Brock Winberg first gained attention by rescuing a moldering, V-8-powered 1978 Ferrari 308—you may know it as the model that “Magnum: P.I.” drove on TV—and transforming it into an electric marvel.  Now, the co-founders of Electric GT have developed a DIY, electric “crate motor” that will let traditional gearheads or EV fans do the same.

“A lot of guys go out for a weekend in a classic car that’s 40 or 50 years old, but they get a ride home with AAA; it ends up being a one-way trip,” Hutchison says. “Here, you’re taking out 95 percent of the maintenance, which is the biggest problem with classic cars. So this is for enthusiasts who love their cars, but want a fun, reliable car that’s good for 100 or 125 miles on a weekend drive.”

Air Taxi Takes to the Sky(scrapers)

Post Syndicated from Sandy Ong original https://spectrum.ieee.org/cars-that-think/transportation/alternative-transportation/bringing-air-taxis-to-skyscrapers

Beneath a gray, rainy sky, the normally vibrant business district of Singapore looked listless. The glass skyscrapers didn’t glitter and no sunlight dappled across the waves in the bay. But that didn’t matter much because the crowd gathered amid the tall buildings today had come to gawk at something else. 

At the stroke of noon, from a promontory across the bay, a speck of white rose into the air. With a lawnmower-like hum, a flying taxi that looked like the love child of a helicopter and a drone approached, drawing a swell of cheers from the crowd. 

Volocopter’s three-minute test flight was not the first time the German aircraft manufacturer has flown its full-scale prototype publicly. But today’s demonstration was momentous in other ways. It marks the first official test flight in Asia, and the first time the aircraft was put through its paces in an urban environment. That’s big news because big cities are the places where the company hopes its air taxis will ultimately find a niche.

“In the next 10 years, we hope to see Volocopter integrated as an addition to existing mobility methods in mega cities,” says Christian Bauer, who is in charge of the firm’s business development. Volocopter is aiming to be the first company in the world to offer commercial air taxi services to the masses.

Air taxis, part of a category called electric vertical takeoff and landing (eVTOL) aircraft, form a rapidly growing market—one that is expected to reach $1.5 trillion by 2040. More than 215 such aircraft are being developed worldwide, and with varying designs. Volocopter operates on drone technology with 18 motors, while others such as Lilium Jet have fixed wings. But only a handful of Volocopter’s competitors have actually built flying prototypes.

Volocopter, which was founded in 2011 and counts Intel, Daimler AG, and the Geely Holding Group (which owns automaker Volvo) among its investors, has raised close to US $95 million to date. That cash and access to a broad array of expertise have allowed Volocopter to present its third generation of lithium battery–operated, two-seater air taxis. Its next prototype, VoloCity, to be launched by 2022, promises improved specs over the current 2x series. The VoloCity expected to debut with an estimated range of 35 kilometers and a top speed of close to 110 kilometers per hour.

“Volocopter is focused on serving the inner-city mission,” says CEO Florian Reuter. With fares expected to be in the “hundreds rather than thousands of dollars,” Reuter says the airborne taxi service’s expected customers fall into three categories: businessmen looking to get quickly from point A to B, commuters seeking ways to beat rush hour traffic, and tourists.

“I believe eVTOLs will play a significant part in the future of mobility,” says Roei Ganzarski, CEO of magniX, an Australian firm developing motors for electric planes. “I don’t think we will see thousands of these flying around each city as some companies would like the public to believe, but I do think we will see shuttle models, movement between nearby airports, movement of cargo between main depots and last mile distribution [hubs], corporate use between campuses, and more.” 

However, it could take 10 to 15 years for this to become reality, says Ganzarski, because there are “many other things that need to be solved first.” Among the hurdles he cites are battery power, regulatory issues, and the ability of autonomous aircrafts to handle emergencies. Other experts, such as aviation professor Jason Middleton from the University of New South Wales, voice concerns about hardware and software safety, the need to build supporting infrastructure, the challenges of navigating in bad weather, and how to manage air traffic control.

Pilots act as a fail-safe in many respects, says Middleton, who has been flying for nearly 50 years. “In an urban environment with lots of skyscrapers, you’re going to have gusts and you can’t predict where they’re going to be. Weather is unpredictable; it can quickly develop from nothing into a raging thunderstorm,” he says. “Who’s going to predict where [air taxis] can or can’t fly? And what happens when they’re in the air and can’t go to their destination?”

He adds that, “At least if you have a pilot, they’re going to look out the front and see what’s going on and take necessary action.”

One of the answers to those concerns is unmanned aircraft system traffic management platforms, or UTMs for short. Volocopter is looking to use them to govern its air taxis. “You can take most of the airspace management techniques we use in drones and apply it to air taxis,” says Pamir Sevincel, who leads urban air mobility strategy at AirMap, one of the UTM companies Volocopter is working with. Drones, which usually fly below 400 feet, are subject to different air traffic management protocols than those applied to helicopters and other aircraft.

AirMap has developed numerous UTM capabilities, all of which can theoretically be used for eVTOLs as well. These include the digital submission and approval of flight plans, surveilling an aircraft and sending alerts if it veers off track, monitoring traffic and sending real-time updates, as well as providing dynamic rerouting during emergencies. In the future, the California-based company wants to enable pilots or ground-based fleet managers of drones and air taxis to update flight trajectories based on an automated assessment of risk as a function of pedestrian and car densities, as well as other potential safety issues along planned routes. It also plans to equip flying craft with “sequencing, scheduling, and spacing” capabilities, which would allow the safe and efficient scheduling of operations in and out of vertiports and within the urban air mobility network as a whole.

“This capability is really going to enable scale in a safe way…because if you don’t, you won’t be able to integrate many flights into urban airspace” says Sevincel.

Building infrastructure to support air taxis—vertiports with passenger lounges, check-in and security facilities, as well as battery charging and aircraft maintenance stations—is another issue that must be addressed before air taxis can become a commercial reality. To that end, Volocopter has partnered with Skyports, a British infrastructure firm that has just unveiled the first prototype of its VoloPort— the air taxi equivalent of a helipad—in Singapore. 

Volocopter’s Reuter says his firm is also working closely with global aviation authorities to ensure that its next-generation air taxi rises to “the same safety level airliners are built to.” He’s also well aware that gaining public acceptance is key when it comes to autonomous transport, which is why he says Volocopter’s first stage of commercial operations, scheduled within five years, will likely involve piloted flights, with the eventual aim of moving towards full autonomy. 

“We, as a global society, have to feel our way into this technology…and try it out in a safe and secure environment,” says Reuter. 

“Many people picture the skies becoming dark and aircraft whizzing around the city without any control or rules. That’s a very negative and chaotic image,” he says. “But let’s take it step by step and evaluate how it goes.” 

Automotive Radar

Post Syndicated from IEEE Spectrum Recent Content full text original https://spectrum.ieee.org/whitepaper/automotive-radar

Download or order our beautifully designed automotive radar poster. It provides an overview of the technologies that make new cars safer and more convenient, including a snapshot of radar technology from 24 to 81 GHz.

Get into the fast lane with the latest technical resources on autonomous driving. Learn about the design and test of complex sensor and communication technologies being built into autonomous vehicles from our white paper and posters.

poster

Envisioning the Future of Urban Transportation

Post Syndicated from University of Maryland original https://spectrum.ieee.org/transportation/alternative-transportation/envisioning-the-future-of-urban-transportation

Growing urbanization around the globe is creating increasingly difficult challenges in areas of transportation and energy, but engineers at the University of Maryland (UMD) think there are solutions in the promise of electric vertical takeoff and landing (eVTOL) aircraft.

Just a decade ago, the idea of air taxies and cityscapes equipped with “verti-port” stations may have seemed like the latest science fiction, but with the technical advances and commercial success of electric vehicles, eyes are turning to the sky to see how similar ideas of electric power and propulsion could create a new generation of lightweight air vehicles capable of moving people quietly, safely, and efficiently in dense urban environments.

“eVTOL has many advantages over traditional helicopters,” explains Anubhav Datta, an associate professor in UMD’s A. James Clark School of Engineering. “They don’t cause the pollution of traditional engines, have no engine noise, require fewer mechanical parts, and depending on the design could be easier to fly and more responsive to autonomy.”

While eVTOL technology is in its infancy, Datta has been involved from the start. He published the first peer-reviewed journal article demonstrating the viability of eVTOL by presenting conceptual designs for three all-electric options for a manned ultralight utility helicopter, and anticipating growth of the field. Since then, he has been instrumental in spearheading efforts to expand basic research in eVTOL, create pools of technical knowledge, and develop multidisciplinary education and outreach programs.

At Maryland, Datta and his graduate students are pursuing several projects addressing some of the principal barriers that prevent eVTOL from becoming a day-to-day reality.

One major barrier to eVTOL success is developing lightweight on-board electrical energy storage systems that would allow these aircraft to fly for longer periods with adequate reserves. According to Datta, lithium-ion batteries built for consumer electronics and automobiles are too low-energy to be a long-term solution. Batteries must be built to meet VTOL requirements or alternative sources of power explored, such as the work of Ph.D. student Emily Fisler, who is trying to quantify these requirements and explore more advanced chemistries for future batteries.

Datta, along with students Wanyi Ng and Mrinal Patil, are also exploring the application of hydrogen in fuel cells as a renewable and clean energy source. Hydrogen gas can store four to five times as much energy as current batteries, but the high power fuel stacks are heavy—so Datta’s team is looking at ways to maximize the energy benefits of hydrogen by using supplemental batteries to boost output during high-power loads, such as takeoff and landing.

Since 2017, UMD’s Department of Aerospace Engineering has won two multi-year research tasks on eVTOL funded jointly by the U.S. Army, NASA, and the U.S. Navy. As part of this work, Ph.D. student Brent Mills has built a unique hybrid-electric engine—capable of powering a scaled-down 50-pound VTOL aircraft to test and acquire data on electro-aero-mechanical behavior of the engine. Aircraft designers of the future can use this data in conceptualizing and building vehicles.

A key advantage of electric drives is that they do not require heavy, interconnecting mechanical shafts to drive more than one rotor. While multiple rotors are less efficient, they make an aircraft more stable and maneuverable, which could possibly reduce training times for future pilots and make them safer to operate in urban environments. In addition, being easier to control makes them more receptive to autonomous operation.

According to Datta, one critical advantage to UMD’s eVTOL research is the university’s historic Glenn L. Martin Wind Tunnel. Constructed in 1949, it is one of only a few tunnels its size on a university campus. This facility enables them to acquire truth-data from direct observation that is critical to the safe design of advanced rotorcraft, yet far beyond what the best computational tools can predict. Special-purpose rigs are needed to carry out model tests in the tunnel.

One such rig is the Maryland Tiltrotor Rig (MTR). Designed to study the aeromechanics of advanced prop-rotors and wing combinations, the MTR has a direct electric drive on the pylon so that data collected in the course of the project can also be applied to eVTOL. The MTR can test up to 4.75-foot diameter Mach-scaled rotors, features interchangeable blades and hubs turning at 2,500 revolutions per minute, and has interchangeable spars that can change the wing behavior.

“It is the only test rig of its kind on a university campus,” says Datta, “and the Ph.D. students who developed it are laying the foundations of the future of tiltrotor and eVTOL research at Maryland for the next decade.”

Datta was a member of the American Helicopter Society’s (AHS) inaugural eVTOL workshop in 2014, chaired the NASA Aeronautics Research Institute’s (NARI) Transformative Vertical Flight working group on intra-city Urban Air Mobility in 2016, and led the AHS in establishing eVTOL as a distinct technical discipline by founding the eVTOL Technical Committee in 2019. Chaired by Datta, this committee includes technical leaders from across industry, government, and several UMD alumni who have become leaders in the field of rotorcraft.

As part of these efforts, Datta, with support from the Vertical Flight Society (VFS, formerly AHS) and NASA, created the first formal education course in eVTOL now taught annually at the VFS Forum and the American Institute of Aeronautics and Astronautics (AIAA) Aviation forum.

Datta believes that the promise of better utilization of airspace through eVTOL advancements could bring about more energy efficient transportation solutions, but there is a lot of research and expertise that still needs to be developed to propel this new field forward.  

“Through research efforts here at Maryland, we are not just building the future of eVTOL,” Datta says, “but we are providing opportunities for students to become the next generation of engineers that will have the knowledge and hands-on expertise to go out and be major contributors to that field.”

Formula Student: The Crucial Role of the IMU/GNSS

Post Syndicated from Julie Laveissiere original https://spectrum.ieee.org/transportation/sensors/formula-student-the-crucial-role-of-the-imugnss

The Formula Student is an international educational engineering competition in which teams of students from around the world design, build, and race their own formula race cars. The competition includes 3 categories: Electric, Driverless, and Combustible cars. The challenge is not only to build the fastest race car, but also to show the best behavior in endurance, acceleration, or skid pad for example.  

As an expert in Inertial Navigation Systems and partner of several teams, SBG Systems interviewed various teams of engineers using SBG Inertial Measurement Unit (IMU) combined with Global Navigation Satellite System (GNSS) to understand what the key elements to success are.

The Importance of the IMU/GNSS for Precise Car Dynamics

The IMU/GNSS provides decisive information on the car state such as position, speed, yaw rate, slip angle, acceleration and orientation to the competing teams’ cars, as stated by D. Kiesewalter, from AMZ Racing: “We required an IMU for several reasons. Primarily to determine the position state of our car. We also needed to have efficient dynamics control & a reliable and accurate determination of Euler Angles (roll, pitch, and heading).” This way, engineers of electric and combustible cars can understand what to improve by comparing the actual state to the theoretical one.

Mastering acceleration is primordial during Formula races. When the car accelerates too much, it can drift, which causes the wheels to wear out. To minimize tire wear and get the most of the engine’s power and performance, acceleration has to be checked.

Tracking the race car trajectory is essential. A circuit analysis is conducted thanks to the IMU/GNSS data, especially position, and helps determine if the car is well positioned inside the circuit or when turning.

Let’s not forget that the Formula Student is a race. One of the competition goals is to go faster on the track than the other teams. Speed is therefore a crucial factor to study, thanks to the IMU/GNSS. But it is even more important for electric race cars, as they need to track the consumed energy.

Driverless Race Cars: Taking the Best of Heading and Navigation out of the IMU/GNSS

If a single-antenna GPS based heading is enough for racing cars, driverless vehicles require a more precise heading provided by a dual-antenna GNSS/IMU. It allows faster initialization and delivers true heading even in stationary position. J. Liberal Huarte from UPC Driverless (ETSEIB) explains that heading and localization are essential for other parts of the equipment to function properly: When we operate with LiDAR technologies, the fact that you are headed 1 degree to one side or the other influences a lot the position. So, precise heading is a big requirement. And also, localization and mapping: it is very important to localize yourself in the X, Y.” Therefore, implementing a Dual GNSS/IMU in this type of race car is the best solution, as it provides true heading and position, which also helps stabilize the LiDAR.

Heading is as important as precise navigation for driverless race cars. Real Time Kinematic (RTK) allows an extremely accurate estimation of the position (1-2 cm). The more accurate the IMU/GNSS is, the more the car is able to stay in the circuit lane without drifting.

The IMU/GNSS also helps conduct a circuit analysis that determines if the car is well positioned and so optimizes the trajectory.

Less Implementation Time = More Time for the Whole Project

We have very small test time, so if it goes fast, we can go faster on the track and test more”, states A. Kopp, Vehicle Dynamics Control, TUfast Racing. Teams don’t have much time to integrate the different parts of the vehicle and to test them. As CAN and ROS framework are mainly used by automobile engineers, IMU/GNSS that can be part of such workflows can save tremendous time of development. A clean C library provided with examples is another way to help teams with their integration.

About SBG Systems IMU/GNSS

SBG Systems is an international company which develops Inertial Measurement Unit with embedded GNSS, from miniature to high accuracy ranges. Combined with cutting-edge calibration techniques and advanced embedded algorithms, SBG Systems manufactures inertial solutions for industrial & research projects such as unmanned vehicle control (land, marine, and aerial), antenna tracking, camera stabilization, and surveying applications.

  • SBG Systems supports new ways to design cars. Students are welcome to send their sponsorship application through our website.

This Inside-Out Motor for EVs Is Power Dense and (Finally) Practical

Post Syndicated from Daan Moreels original https://spectrum.ieee.org/transportation/alternative-transportation/this-insideout-motor-for-evs-is-power-dense-and-finally-practical

The world is electrifying fast. Manufacturing processes, cars, trucks, motorcycles, and now airplanes are making the move to electrons that Edison predicted more than a century ago. And they are all doing so for much the same reasons: quieter operation, reduced maintenance requirements, better performance and efficiency, and a more flexible use of energy sources.

At the heart of this great process of electrification stands the electric machine, filling either the role of a generator, for turning mechanical energy into electricity, or that of a motor, for doing the opposite.

For a long time, electric machines have hewed to a standard design, which has had the advantage of being very easy to manufacture. However, our startup, Magnax, based in Belgium, has taken another design that in theory can wring much more power and torque from a given mass and has made it commercially practical. We believe this new design can supplant the old one in many applications, notably in electric vehicles, in which it is now being tested.

One of our designs has a peak power density of around 15 kilowatts per kilogram. Compare that with today’s motors, such as the one in the all-electric BMW i3, which delivers a peak power density of 3 kW/kg—or just one-fifth as much. And the Magnax machine is also more efficient.

We believe that we can scale the design to whatever size carmakers (and other customers) may demand. If so, then there is every reason to believe that this design will push aside the traditional one. If it does, it will help to improve performance, save on energy and overall operating costs, and reduce carbon emissions for a better world.

The concept of an electric machine is simple. You start with a housing, which is called a stator because it remains stationary. Then you add a rotor, which spins, usually inside the stator but sometimes outside, an idea we’ll discuss later. When the machine is functioning as a motor, the magnetic fields of the stator and the rotor interact: Strategically placed magnets around the circumference of the rotor and stator repel or attract each other in a sequence to sustain the rotor’s spin and create torque. In this way, the machine converts electrical energy to mechanical energy. When the machine functions as a generator, the process operates in reverse.

Such a rotating machine today generally uses permanent magnets rather than electromagnets in the rotor and is thus called a permanent-magnet synchronous machine (PMSM). When operating as a motor, it passes alternating current to structures in the stator known as teeth. The result is a rotating magnetic field in the stator that acts on the permanent magnets of the rotor, spinning it.

The big advantage here is that permanent magnets don’t need energy to create a magnetic field. That makes this design more efficient and more powerful for a given weight and volume than a machine that uses electromagnets in the rotor.

There are many compelling reasons why PMSMs began to dominate in the 1980s, but the most important one was the development of a much more powerful breed of permanent magnet, based on neodymium. Nevertheless, because there was no change in the overall layout of the machine, the new magnet could provide only an incremental improvement. To further reduce the weight, size, and cost of the machine, the electromagnetic interaction had to be fundamentally rethought. That’s what we’ve done. We call our product a yokeless axial-flux permanent-magnet machine.

It’s a mouthful, and we’ll explain it in a moment. First, though, it’s important to understand that people already knew that the axial-flux topology had intrinsic advantages. It’s just that there seemed to be no way to exploit those advantages commercially, mainly because a design based on them would be hard to mass-produce using automated procedures.

Before we could begin designing our motor, we had to overcome a fundamental problem: There was no commercially available software that could accurately and simultaneously model the electromagnetic and thermodynamic properties of an axial-flux motor. However, Peter Sergeant and Hendrik Vansompel of Ghent University, in Belgium, have been working on this problem since 2008. Their efforts, combined with several years of R&D and prototyping by Magnax, led to our design and our manufacturing methods.

A traditional, radial-flux machine puts the rotor inside the stator. Here the stator consists of a supporting part, called the yoke, which is fitted with teeth that contain electromagnet coils. The teeth thus function as magnetic poles. As the rotor turns, its own poles transmit flux every time they sweep past a stator tooth, and the stator carries the flux elsewhere—closing what’s called the flux loop. The flux is routed from the rotor’s permanent magnet through the air gap and the stator teeth, taking a 180-degree bend through the yoke and back to another magnet. Meanwhile, of course, the interaction between the permanent magnets and the rotating electromagnetic field in the stator teeth keeps the rotor spinning.

For highest efficiency, the design should minimize the distance—the air gap—between the rotor and the stator teeth, because air transports magnetic flux poorly.

Our axial-flux machine turns that traditional arrangement inside out. It uses not one but two rotors, on either side of the stator, bracketing it. In this arrangement, the stator merely functions as the bearer of the electromagnetic teeth, not as the support—or yoke—for the rotor. In other words, it creates the possibility of a stator that is yokeless—hence the inclusion of this word in the name.

Eliminating the yoke—basically a steel cylinder that composes about two-thirds of the stator iron—saves an enormous amount of weight. As a result, yokelessness more than doubles the machine’s power density, compared with that of the older, yoked axial motors, and quadruples it compared with that of a traditional motor (like the one in the BMW i3). It also improves efficiency by reducing a bane of electric machines: iron loss.

Iron loss is mainly the result of two phenomena. First, there is the energy consumed when alternating current repeatedly magnetizes and demagnetizes cores in the stator—a process called hysteresis loss. Second are the losses to eddy currents, which are created by the varying magnetic flux through the cores.

There are other reasons why the design is so power dense. In this design, the magnetic flux goes from the permanent magnets on the first rotor disk, through the stator core to the permanent magnets on the second rotor disk—a relatively short and straight path.

Thanks to that unidirectionality, Magnax can further decrease the flux losses in the iron by 85 percent by using a material that’s perfect for conducting flux in one direction only—grain-oriented steel. Such steel couldn’t go into a traditional, radial-flux motor or generator because such machines route the flux from the rotor through the stator and back to the rotor—a multidirectional route. Magnax closely collaborated with Thyssenkrupp Electrical Steel on the design of the laminated grain-oriented cores.

Other advantages: In our yokeless axial-flux design the stator needs only about 60 percent as much copper and the rotor needs about 80 percent as much magnetic material than would a radial-flux motor of comparable power and torque.

In theory, all of these advantages make possible a relatively inexpensive and lightweight machine that delivers a lot of torque. But actually building such a machine meant facing down several serious engineering challenges.

The most obvious involve finding ways to replace the traditional functions of a yoke. In a conventional motor, the yoke holds the stator teeth in place and provides a thermal path for transporting the heat from the coils to the motor casing. It also serves as a path that closes the loop along which the magnetic flux flows when returning to its original source.

First, Magnax had to solve the mechanical challenges. Because there is no yoke to connect the individual stator teeth, another solution had to be found to create a stator with sufficient strength and stiffness to hold the teeth firmly in place even as they are wrenched by powerful electromagnetic forces.

Next came the thermal challenges. Because the windings are buried deep inside the stator and between the two rotor discs, the heat they generate can be hard to disperse. Better cooling lets you increase a machine’s nominal power—that is, the actual mechanical power it puts out. Older axial-flux concepts—those that use a yoke—cool the coils by integrating a cooling channel in the yoke. However, that arrangement makes the heat flow through the yoke, and iron is not particularly good at transporting heat. Because the Magnax design has no yoke, we needed to find another way to directly cool the coils.

Manufacturing was yet another challenge. Existing axial-flux machines have always been hard to manufacture because the stator and the windings are complex. That’s why until now such machines generally didn’t lend themselves to automated production. These challenges translate to higher cost and very poor scaling, which can be seen in most of the axial-flux designs that are now commercially available.

Yokeless concepts, however, have a simpler winding scheme, which saves on labor. So cooling emerged as one of the biggest challenges. YASA, in England, another developer of yokeless axial-flux motors, has a manufacturable motor concept; the company uses oil cooling and is building its own factory for volume production in the United Kingdom. Magnax’s design uses a different, and more flexible, cooling scheme.

Magnax has one that can use a number of coolants, notably air, water-glycol, and oil. Air cooling is preferred for use in drones and in two- and three-wheel electric vehicles (popular in India, for instance). It’s also good in big machines, such as wind-turbine generators. Liquid cooling is better for maximum power densities, in combination with gearboxes. Thus, it is often used in automotive applications.

We start by laminating aluminum or copper heat sinks in close thermal contact with the windings. The heat sinks transport the heat to the outer perimeter, where it can be carried away by cooling fins or a water-cooling jacket. This not only gives the machine a much higher capacity to evacuate heat, making it possible to produce greater nominal torque and power, it also allows for a very stiff and completely solid stator construction. That means that the machine can handle a lot of torque and still last for a long time.

At the moment, our focus is on custom motor designs for automotive original-equipment manufacturers and their suppliers. Because axial-flux motors have a short axial length, they can help keep the power train short. That proves useful to automakers that integrate the motor, the transmission, and the electronics into an electric vehicle’s axle, an assembly called an eAxle. These motors are also very useful in a hybrid design, where the combination of an engine and an electric drive system usually leaves little room for the motor.

Our design is also suited for in-wheel applications, where the motor goes right inside the wheel assembly. That configuration has many advantages—for instance, you can help to steer the car by varying the torque at each wheel, a trick known as torque vectoring. However, putting the motor in the wheel increases the unsprung mass—the part of a car that’s between the suspension and the road—and that can make the ride bumpier. Every gram of weight saved on an in-wheel motor is therefore golden.

A European carmaker is now track-testing an in-wheel car concept that uses four Magnax motors, all made in the “outrunner” configuration. That’s where the spinning part of the motor is on the outside (rather than on the inside, on a shaft), making the machine ideal for integration inside the very tight spaces within a wheel assembly. Here, too, the result is a power density that’s twice as high as a conventional motor’s, with higher efficiency to boot.

Although most car designs don’t put motors right inside the wheels, many do use more than one motor in the vehicle. In fact, any car that uses multiple motors will benefit particularly from our product. The more motors you carry, the more important it is that they be light and compact. We have calculated that the absence of a yoke and its associated iron losses can increase the range an EV could travel by 7 percent in a car with a single motor and up to 20 percent in a car with two motors. Imagine the further effects on the battery, which is the most expensive part of an EV.

The main challenge now is to bring the concept into series production; Magnax will organize this together with production partners. We have invested a lot of time in the design for manufacturing our machines. As a result, we can prove that our machines can be produced. This capability, together with the savings we can realize on materials, makes our concept competitive on price—a key point for graduating from the niche markets to the original-equipment manufacturers.

The assembly line we are building will be capable of producing motors of several diameters. We plan to begin producing 25,000 motors per year by 2022 and to scale to hundreds of thousands later on.

Over the past two years we’ve had inquiries from hundreds of companies that are interested in motors of widely varying diameters for use in electric motorcycles, trucks, and other EV applications. In addition, we still receive requests from makers of wind turbines and industrial equipment. These particular markets are not our highest priority, but the widespread demand shows that our technology has what many companies need: compactness, power, and efficiency.

Our design can cut costs substantially in a high-volume business—for instance, the production in China of millions of motors of between 1 and 10 kW. When producing in large quantities, what counts is limiting the cost of the raw materials, which as we’ve shown is significantly lower than for traditional motors.

Tens, even hundreds of millions of electric motors were sold in 2017, for a total of some US $97 billion. Their average efficiency remains below 90 percent.

In tests at the University of Ghent on the first prototype, our yokeless axial-flux motor reached efficiencies from 91 to 96 percent. And that was just the prototype.

Motors and motor systems account for approximately 53 percent of global electricity consumption. We estimate that improving the efficiency of all the world’s motors by just 1 percent would reduce the motors’ power consumption by 94.5 terawatt-hours and shrink their carbon dioxide footprint by the equivalent of 60 million metric tons.

If yokeless axial-flux machines replaced only a fraction of the older machines, we would save our customers some money and make the planet more livable while we’re at it.

This article appears in the October 2019 print issue as “Turning the Electric Motor Inside Out.”

About the Authors

Daan Moreels is a cofounder of Magnax, in Kortrijk, Belgium, and Peter Leijnen is the company’s founder.

Not Too Hot, Not Too Cold: An Automatic Climate Control System

Post Syndicated from Michelle Hampson original https://spectrum.ieee.org/tech-talk/transportation/advanced-cars/not-too-hot-not-too-cold-an-automatic-climate-control-system

Journal Watch report logo, link to report landing page

Many drivers are familiar with the irritation of being stuck in traffic on a sweltering summer day. Two researchers at the University of Michigan are working to make uncomfortable situations like this a bit more bearable, by developing a system that will automatically control the climate within a car to optimize both the passengers’ comfort level and the efficiency of the HVAC system.

Over the past few years, Mohamed Abouelenien and Mihai Burzo have been developing approaches to analyze and detect various human behaviors, including lying, feeling stressed, remaining alert at the wheel, and expressing affection, among others. Their latest effort has been to develop a system for cars and homes that automatically detects a person’s thermal discomfort and adjusts accordingly, without any human input.

Choosing an Optical Measurement Sensor for Non-contact Displacement, Dimension and Thickness Measurement

Post Syndicated from IEEE Spectrum Recent Content full text original https://spectrum.ieee.org/webinar/choosing_an_optical_measurement_sensor_for_non-contact_displacement_dimension_and_thickness_measurement

Learn the operating principles of optical measuring sensors for displacement, position, thickness, gap, profile and 2D/3D dimension with just one sensor