GPS/GNSS Backup eDLoran System Delivers 5-Meter Accuracy

As reported by GPS World: Durk van Willigen, René Kellenbach, and Cees Dekker of the Dutch consulting firm Reelektronika, and Wim van Buuren of the Dutch Pilots’ Corporation authored the ENC presentation about enhanced differential Loran (eDLoran), with results that greatly — and pleasantly — surprised many in the audience. A full technical article by these authors, describing the equipment, methodology, and test results of eDLoran, will appear in the July issue of GPS World. 

The new Loran project arose from the need of harbor pilots responsible for bringing large and super-large freight ships into dock. These pilots require GNSS-level acuracies of 5 meters for such work, and all parties concerned — pilots, captains, ship owners, harbor management — need some form of robustness, that is, back-up for the GNSS systems in case of jamming, unintentional interference, system failure, or other disruption.

As extensive research had established that 5-meter accuracy cannot be met by the currently tested DLoran system, which cannot get better than 10-meter accuracy. Reelektronika developed a new differential Loran system called enhanced differential Loran, or eDLoran. A full prototype eDLoran system was built and extensively tested in the Europort (Rotterdam) area. The tests achieved accuracies of 5 meters.

For maritime applications, eLoran is considered as the most promising backup for GNSS in case the use of satellite-based navigation signals is denied. The Dutch Pilots’ Corporation askedReelektronika to investigate whether differential Loran could meet the pilots’ 5-meter accuracy requirement for a harbor navigation. This proved to be an enormous challenge as preliminary tests showed that even 10 meters was difficult to achieve with differential Loran (DLoran) as promoted by the UK’s Trinity House/General Lighthouse Authority (see item below about Harwich UK tests by GLA and ACCESS). The challenge had led to a thorough investigation of all possible error sources of a complete differential Loran system.

Differential techniques developed and implemented for Loran are comparable with differential GPS. Although the error sources of GPS and Loran are quite different, the major common error source in both systems is the lack of accurate propagation models.

This led to a new research project to find a more accurate differential Loran technique. All possible error sources have been investigated again where possible, which resulted in some unexpected results regarding accuracies and costs.

Enhanced Differential Loran: eDLoran
The new concept of differential Loran had to fulfill two important primary improvements. The first is a significant reduction in the latency of the data in the data channel; the second is that a large number of reference stations should be capable of receiving the data channel, without saturating the data channel. The simple conclusion was that Eurofix could not meet these two improvements. However, Eurofix is still the prime GNSS backup candidate for distributing accurate UTC over very large parts of Europe. Further, Eurofix has the capability to send short messages that might be encrypted for secure communication purposes which might then form a terrestrial backup, for example, Galileo PRS.

Instead of using the Eurofix channel, eDLoran uses the public mobile GSM (Global System for Mobile) network to send the differential corrections to users. eDLoran receivers therefore contain a simple modem for connection to the GSM network. The eDLoran reference stations are also connected to the Internet which may be implemented via a cabled access or also via a GSM modem.Fortunately, today many GSM networks are robust in respect of GPS outages.

The eDLoran infrastructure is not connected with any eLoran transmitter station and operates completely autonomously. An eDLoran reference station is connected to a central eDLoran server by its connection to the network.

eDLoran Results
Both static and dynamic tests have been carried out. Here, only the final result of the dynamic test is presented. For full details on both sets of tests, see the upcoming full-length technical article in the July issue of GPS World magazine.

The results have been demonstrated to the harbor authorities in real-time on the laptop of the pilots on which the GPS-RTK and the eDLoran position were simultaneously shown. The logged GPS-RTK data is plotted on a Google Earth map shown in the accompanying figure. The track was widened to 10 meters as the accuracy requirements are 5 meters on either side of the track. The raw eLoran track is also shown, as well as the final white eDLoran track.

The red track is based on raw eLoran data without any corrections. The transparent blue line is made by GPS-RTK and is widened to 10 metres giving the required ± 5 meter limits of eDLoran. The white line is output from the eDLoran receiver which stays within the borders of the 10-meter-wide transparent blue line.

Conclusions

The outcome of the research opens some new and quite surprising possibilities for multiple applications. Only a few of the authors’ conclusions appear here:

1. eDLoran offers the best possible eLoran accuracy as it does not suffer from swaying wire antennas, sub-optimal timing control of the transmitter station and differential data latency.
2. There is no need to replace older Loran-C stations with eLoran transmitters saving large amounts of money. The existing Loran stations have a proven reliability track record. Further savings may be obtained by containerising the transmitter and operating the stations unmanned.
3. Installing eDLoran reference stations is fast, simple and very cost effective.
4. As there is no data channel bandwidth limitation, multiple reference stations can be installed which offers increased reliability and makes the system more robust against terrorism and lightning damage.
5. A single or multiple eDLoran servers can be installed in a protected area. There is hardly a practical limit in the number of differential reference stations to serve.

The Self-Driving Car Of Tomorrow May Be Programmed To Hit You

As reported by Wired: Suppose that an autonomous car is faced with a terrible decision to crash into one of two objects. It could swerve to the left and hit a Volvo sport utility vehicle (SUV), or it could swerve to the right and hit a Mini Cooper. If you were programming the car to minimize harm to others–a sensible goal–which way would you instruct it go in this scenario?

As a matter of physics, you should choose a collision with a heavier vehicle that can better absorb the impact of a crash, which means programming the car to crash into the Volvo. Further, it makes sense to choose a collision with a vehicle that’s known for passenger safety, which again means crashing into the Volvo.

But physics isn't the only thing that matters here. Programming a car to collide with any particular kind of object over another seems an awful lot like a targeting algorithm, similar to those for military weapons systems. And this takes the robot-car industry down legally and morally dangerous paths.

Even if the harm is unintended, some crash-optimization algorithms for robot cars would seem to require the deliberate and systematic discrimination of, say, large vehicles to collide into. The owners or operators of these targeted vehicles would bear this burden through no fault of their own, other than that they care about safety or need an SUV to transport a large family. Does that sound fair?

What seemed to be a sensible programming design, then, runs into ethical challenges. Volvo and other SUV owners may have a legitimate grievance against the manufacturer of robot cars that favor crashing into them over smaller cars, even if physics tells us this is for the best.

Is This a Realistic Problem?

Some road accidents are unavoidable, and even autonomous cars can’t escape that fate. A deer might dart out in front of you, or the car in the next lane might suddenly swerve into you. Short of defying physics, a crash is imminent. An autonomous or robot car, though, could make things better.

While human drivers can only react instinctively in a sudden emergency, a robot car is driven by software, constantly scanning its environment with unblinking sensors and able to perform many calculations before we’re even aware of danger. They can make split-second choices to optimize crashes–that is, to minimize harm. But software needs to be programmed, and it is unclear how to do that for the hard cases.

In constructing the edge cases here, we are not trying to simulate actual conditions in the real world. These scenarios would be very rare, if realistic at all, but nonetheless they illuminate hidden or latent problems in normal cases. From the above scenario, we can see that crash-avoidance algorithms can be biased in troubling ways, and this is also at least a background concern any time we make a value judgment that one thing is better to sacrifice than another thing.

In previous years, robot cars have been quarantined largely to highway or freeway environments. This is a relatively simple environment, in that drivers don’t need to worry so much about pedestrians and the countless surprises in city driving. But Google recently announced that it has taken the next step in testing its automated car in exactly city streets. As their operating environment becomes more dynamic and dangerous, robot cars will confront harder choices, be it running into objects or even people.

Ethics Is About More Than Harm

The problem is starkly highlighted by the next scenario, also discussed by Noah Goodall, a research scientist at the Virginia Center for Transportation Innovation and Research. Again, imagine that an autonomous car is facing an imminent crash. It could select one of two targets to swerve into: either a motorcyclist who is wearing a helmet, or a motorcyclist who is not. What’s the right way to program the car?

In the name of crash-optimization, you should program the car to crash into whatever can best survive the collision. In the last scenario, that meant smashing into the Volvo SUV. Here, it means striking the motorcyclist who’s wearing a helmet. A good algorithm would account for the much-higher statistical odds that the biker without a helmet would die, and surely killing someone is one of the worst things auto manufacturers desperately want to avoid.

But we can quickly see the injustice of this choice, as reasonable as it may be from a crash-optimization standpoint. By deliberately crashing into that motorcyclist, we are in effect penalizing him or her for being responsible, for wearing a helmet. Meanwhile, we are giving the other motorcyclist a free pass, even though that person is much less responsible for not wearing a helmet, which is illegal in most U.S. states.

Not only does this discrimination seem unethical, but it could also be bad policy. That crash-optimization design may encourage some motorcyclists to not wear helmets, in order to not stand out as favored targets of autonomous cars, especially if those cars become more prevalent on the road. Likewise, in the previous scenario, sales of automotive brands known for safety may suffer, such as Volvo and Mercedes Benz, if customers want to avoid being the robot car’s target of choice.

The Role of Moral Luck

An elegant solution to these vexing dilemmas is to simply not make a deliberate choice. We could design an autonomous car to make certain decisions through a random-number generator. That is, if it’s ethically problematic to choose which one of two things to crash into–a large SUV versus a compact car, or a motorcyclist with a helmet versus one without, and so on–then why make a calculated choice at all?

A robot car’s programming could generate a random number; and if it is an odd number, the car will take one path, and if it is an even number, the car will take the other path. This avoids the possible charge that the car’s programming is discriminatory against large SUVs, responsible motorcyclists, or anything else.
This randomness also doesn’t seem to introduce anything new into our world: luck is all around us, both good and bad. A random decision also better mimics human driving, insofar as split-second emergency reactions can be unpredictable and are not based on reason, since there’s usually not enough time to apply much human reason.

Yet, the random-number engine may be inadequate for at least a few reasons. First, it is not obviously a benefit to mimic human driving, since a key reason for creating autonomous cars in the first place is that they should be able to make better decisions than we do. Human error, distracted driving, drunk driving, and so on are responsible for 90 percent or more of car accidents today, and 32,000-plus people die on U.S. roads every year.

Second, while human drivers may be forgiven for making a poor split-second reaction–for instance, crashing into a Pinto that’s prone to explode, instead of a more stable object–robot cars won’t enjoy that freedom. Programmers have all the time in the world to get it right. It’s the difference between premeditated murder and involuntary manslaughter.

Third, for the foreseeable future, what’s important isn’t just about arriving at the “right” answers to difficult ethical dilemmas, as nice as that would be. But it’s also about being thoughtful about your decisions and able to defend them–it’s about showing your moral math.  In ethics, the process of thinking through a problem is as important as the result.  Making decisions randomly, then, evades that responsibility. Instead of thoughtful decisions, they are thoughtless, and this may be worse than reflexive human judgments that lead to bad outcomes.

Can We Know Too Much?

A less drastic solution would be to hide certain information that might enable inappropriate discrimination–a “veil of ignorance”, so to speak. As it applies to the above scenarios, this could mean not ascertaining the make or model of other vehicles, or the presence of helmets and other safety equipment, even if technology could let us, such as vehicle-to-vehicle communications. If we did that, there would be no basis for bias.

Not using that information in crash-optimization calculations may not be enough. To be in the ethical clear, autonomous cars may need to not collect that information at all. Should they be in possession of the information, and using it could have minimized harm or saved a life, there could be legal liability in failing to use that information. Imagine a similar public outrage if a national intelligence agency had credible information about a terrorist plot but failed to use it to prevent the attack.

A problem with this approach, however, is that auto manufacturers and insurers will want to collect as much data as technically possible, to better understand robot-car crashes and for other purposes, such as novel forms of in-car advertising. So it’s unclear whether voluntarily turning a blind eye to key information is realistic, given the strong temptation to gather as much data as technology will allow.

So, Now What?

In future autonomous cars, crash-avoidance features alone won’t be enough. Sometimes an accident will be unavoidable as a matter of physics, for myriad reasons–such as insufficient time to press the brakes, technology errors, misaligned sensors, bad weather, and just pure bad luck. Therefore, robot cars will also need to have crash-optimization strategies.

To optimize crashes, programmers would need to design cost-functions–algorithms that assign and calculate the expected costs of various possible options, selecting the one with the lowest cost–that potentially determine who gets to live and who gets to die. And this is fundamentally an ethics problem, one that demands care and transparency in reasoning.

It doesn't matter much that these are rare scenarios. Often, the rare scenarios are the most important ones, making for breathless headlines. In the U.S., a traffic fatality occurs about once every 100 million vehicle-miles traveled. That means you could drive for more than 100 lifetimes and never be involved in a fatal crash. Yet these rare events are exactly what we’re trying to avoid by developing autonomous cars, as Chris Gerdes at Stanford’s School of Engineering reminds us.

Again, the above scenarios are not meant to simulate real-world conditions anyway, but they’re thought-experiments–something like scientific experiments–meant to simplify the issues in order to isolate and study certain variables. In those cases, the variable is the role of ethics, specifically discrimination and justice, in crash-optimization strategies more broadly.

The larger challenge, though, isn't thinking through ethical dilemmas. It’s also about setting accurate expectations with users and the general public who might find themselves surprised in bad ways by autonomous cars. Whatever answer to an ethical dilemma the car industry might lean towards will not be satisfying to everyone.


Ethics and expectations are challenges common to all automotive manufacturers and tier-one suppliers who want to play in this emerging field, not just particular companies. As the first step toward solving these challenges, creating an open discussion about ethics and autonomous cars can help raise public and industry awareness of the issues, defusing outrage (and therefore large lawsuits) when bad luck or fate crashes into us.

Google Blesses Uber With iOS, Android Maps Integration

As reported by CNET: Google brought Uber into its mobile family Tuesday with an update to its iOS and Android Maps applications, integrating the transportation and ride-sharing app as one of its various travel options.  

If you have Uber installed and are in a city with Uber service, it will show up alongside walking, driving, and public transportation options to let users see estimated travel times using the car service. The update will also allow you to jump directly into Uber with one tap on the icon within Maps.

This comes as no surprise. After all, Google has had a longstanding relationship with Uber, an app at the forefront of the on-demand and sharing economy movements that has turned urban travel and various metropolitan taxi industries on their heads.

In 2013, Google's investment arm, Google Ventures, sunk $258 million into Uber's last mammoth funding round of $361.2 million. The investment from Google Ventures constituted 86 percent of the fund's yearly spending cap of $300 million. That funding round left Uber with a $3.5 billion valuation.

With Uber, users set their location on a map and choose one of multiple tiered driving services, from regular car drivers using their own vehicle and the Uber app to professional drivers that will ferry you around in a window-tinted black car or hulking SUV. The driver picks you up, drops you off, and transactions are all done via the app and your credit card. Uber is now in 35 countries, with a majority of its service available across 56 cities in North America.

The Maps update had a few other perks as well. If you sign into Maps with your Gmail account, you can now save maps for offline use and pull up saved places for quick searching. There are also better location filters for things like dining that brings Maps more in line with Yelp, and Google Maps' turn-by-turn navigation mode now displays arrival times and distance alongside lane information and alternative routes.

While offline map saving is a huge plus, the Uber integration is likely to draw a good deal of attention considering the Google Venture connection. At this time, it's unclear whether Google is planning on keeping integration with Maps exclusive to Uber, or whether it will expand it to competing services like Sidecar and Lyft.

Google declined to comment regarding any plans to expand its ride-sharing and transportation app integration to other platforms or services beyond Uber at this time.

SpaceX's Falcon 9R Reusable Rocket Soars to 1,000 Meters and Back

As reported by NBC News: SpaceX has pushed its new reusable rocket prototype to record heights, just a few short weeks after the vehicle's maiden flight.

A spectacular new SpaceX video shows the company's Falcon 9 Reusable rocket (Falcon 9R for short) soaring to 3,300 feet (1,000 meters), about four times as high as the rocket went during its first flight test last month.

The stunning footage, which was released on Friday, was captured by a flying drone, providing a bird's-eye view of the action. The video shows the Falcon 9R taking off from SpaceX's rocket-development facility in McGregor, Texas, scaring some nearby cows and then touching down as planned back at the pad about two minutes after launch.

During the May 1 test flight, the Falcon 9R took off with its landing legs extended. In future tests of the rocket, the legs will lie against the side of the rocket initially, then deploy in time for landing, SpaceX representatives have said.

SpaceX is developing reusable rockets in an attempt to dramatically reduce the costs of launching satellites and people into space. Fully and rapidly reusable launch vehicles could make spaceflight 100 times cheaper, company founder and CEO Elon Musk has said.

Such rockets could therefore help make a Mars colony much more feasible — a big priority for Musk, who has said that he started SpaceX primarily to make humanity a multiplanet species.

The F9R is very close in design to the Falcon 9 rocket that SpaceX already uses to launch its unmanned Dragon cargo spacecraft to the International Space Station. The Falcon 9's first stage is fueled by oxygen and kerosene and has nine Merlin rocket engines.

SpaceX has been ramping up its reusable-rocket tests lately. During the latest Dragon resupply launch, which took place on April 18, the company succeeded in bringing the Falcon 9's first stage back to Earth for a soft ocean splashdown, a world first. SpaceX also completed tests of its Grasshopper reusable-rocket program last year after a number of successful flights.

Will The GM 'Switchgate' Recall Kill The Old Ignition Key For Good?

As reported by the Car Connection: General Motors' ignition switch problems have been linked to at least 13 deaths and numerous injuries and complaints.

The company has recalled nearly 2.6 million vehicles, and it's facing a slew of lawsuits from customers, plus investigations from the National Highway Traffic Safety Administration and the Justice Department.


But as terrible news as all that news may be, it could have one positive outcome: the end of the ignition key.

IS THE END IN SIGHT?
As Bloomberg notes, GM's CEO, Mary Barra, recently told a Congressional committee that the "Switchgate" recall may force GM to ditch ignition keys altogether in favor of push-button systems. If so, the move would end decades of complaints from consumers.

How many complaints have been lodged? Its hard to say, because for the first two decades of the ignition key's history, there wasn't an efficient way to track those complaints.

However, Bloomberg approximates at least 18,000 have been filed since NHTSA was formed in 1970, peaking at more than 2000 in the year 2000. Those complaints resulted in roughly 21 million vehicles being recalled.

It's worth noting here that GM's current ignition switch problem isn't unusual -- in fact, it's more-or-less par for the course. Since ignition keys became standard features on cars in the late 1940s, consumers have complained about cars being hard to start, cars starting on their own, and cars switching off with no warning.

It's also worth mentioning that GM doesn't hold the record for ignition-switch complaints. That dubious honor goes to the Ford Focus, which received over 2,000 complaints from owners during the first five years of this century. The problem seemed to be that when the vehicle was parked, drivers often had trouble removing the key. Though it was a significant problem, Ford never issued a recall because it wasn't a safety hazard, just an inconvenience.

(That said, Ford also holds the record for the most vehicles recalled for ignition switch glitches: a cumulative 8.8 million, well ahead of GM's 5.5 million.)

BETTER TECHNOLOGY?
The push-start ignition isn't perfect. Because it triggers from a fob that owners carry in their pockets, drivers may need to be reminded to turn off their vehicles. (Also, that fob means that our keychains aren't going to get any lighter or slimmer in the immediate future.) And in emergency situations, turning off a key can be much faster than holding down the start/stop button for up to three seconds.

Our unofficial office poll reveals that, while many of us have a nostalgic fondness for keys, we know that on the tech front, electrical (i.e. start buttons) trumps mechanical (i.e. keys) more often than not. For some, though, it'll take a bit of getting used to.

Nokia Connected Car Fund Pumps Cash Into High-Tech Automotive

As reported by SlashGear: Nokia will pump $100m into Connected Car technologies, chasing everything from high-tech dashboards that can communicate with smartphones and the cloud, through to self-driving car systems that leverage its HERE brand. 

 The move, managed by Nokia Growth Partners (NGP), will aim to "identify and invest in companies whose innovations will be important for a world of connected and intelligent vehicles," Nokia says, as it tries to reinvent itself after selling off its phone business to Microsoft.  

That decision to divest itself of smartphone hardware leaves Nokia focusing on software and services for the most part, with HERE being one of its three key divisions.



HERE technology - which includes core maps, as well as traffic data, points-of-interest, and high-resolution street view photography - is already being licensed to car companies, manufacturers of PNDs, and used online in services like Bing Maps and Yahoo Maps. Nokia's new goal is to make Connected Cars more prevalent, not to mention push the limits of what they can achieve.

All that will likely include taking advantage of the new "Map HD" higher-resolution data Nokia has been collecting, which dramatically increases the amount of data each roadway and junction contains. Rather than simply treating roads as intersections of lines, it maps to the level of individual lanes, with gradient and other information.  

Nokia has also used LIDAR lasers to grab 3D images of surrounding areas, using them to pull out doorways and other details for pedestrian navigation.

The company has already worked with Mercedes-Benz on a self-driving car project, that saw an autonomous car drive itself across Germany last year. However, it's HERE Auto, Nokia's connected dashboard and infotainment system, which is more likely to get broad industry adoption first.

HERE Auto supports functionality like sharing destination points-of-interest automatically between a smartphone and the car, as well as intelligently routing drivers to not just their programmed destination but nearby locations that make more sense, such as the closest or cheapest parking garage.  

The Nokia Connected Car fund will be managed by Nokia Growth Partners, the Finnish company's independent VC arm, and expects to make investments in the US, Europe, China, and India.

Zipcar To Launch One-Way Rides

As reported by GigaOm: There have generally been two options for car-sharing companies throughout the world: allow one-trips or don’t.

Later this year, Zipcar plans to join the former camp, and on Friday announced it will launch one-way trips in select markets in the fall using the Honda Fit cars.

One-way driving options can be a pretty convenient perk, enabling drivers to pick up a car in the city and drop it off at, say, the airport, before a big trip. It just adds flexibility, particular in a city with good public transportation and other mobility options.

BMW’s car sharing service DriveNow has offered one-way trips since it launched using BMW’s all-electric ActiveEs. Daimler’s car2go network also offers one-way trips with its fleet of Smart fortwos and electric fortwos.

Because most electric cars have shorter ranges than gas cars and need a designated charging station (rather than any old gas station you can find), one-way trips can offer a more bite-sized, and more predictable path, which can be a good fit for using EVs.

Zipcar’s new one-way cars will use the logos you see above to indicate they can be dropped off at a different location than they were picked up.

Zipcar is the largest car sharing network in the U.S. and was bought by rental car company Avis at the beginning of 2013.