self-driving cars

Waymo launches robotaxi app on Google Play

Posted by | Android, Apps, automotive, electric vehicles, Google, Lyft, robotaxi, self-driving car, self-driving cars, transport, Transportation, Uber, waymo | No Comments

Waymo is making its ride-hailing app more widely available by putting it on the Google Play store as the self-driving car company prepares to open its service to more Phoenix residents.

The company, which spun out to become a business under Alphabet, launched a limited commercial robotaxi service called Waymo One in the Phoenix area in December. The Waymo One self-driving car service, and accompanying app, was only available to Phoenix residents who were part of its early rider program, which aimed to bring vetted regular folks into its self-driving minivans.

Technically, Waymo has had Android and iOS apps for some time. But interested riders would only gain access to the app after first applying on the company’s website. Once accepted to the early rider program, they would be sent a link to the app to download to their device.

The early rider program, which launched in April 2017, had more than 400 participants the last time Waymo shared figures. Waymo hasn’t shared information on how many people have moved over to the public service, except to say “hundreds of riders” are using it.

Now, with Waymo One launching on Google Play, the company is cracking the door a bit wider. However, there will be still be limitations to the service.

Interested customers with Android devices can download the app. Unlike a traditional ride-hailing service, like Uber or Lyft, this doesn’t mean users will get instant access. Instead, potential riders will be added to a waitlist. Once accepted, they will be able to request rides in the app.

These new customers will first be invited into Waymo’s early rider program before they’re moved to the public service. This is an important distinction, because early rider program participants have to to sign non-disclosure agreements and can’t bring guests with them. These new riders will eventually be moved to Waymo’s public service, the company said. Riders on the public service can invite guests, take photos and videos and talk about their experience.

“These two offerings are deeply connected, as learnings from our early rider program help shape the experience we ultimately provide to our public riders,” Waymo said in a blog post Tuesday.

Waymo has been creeping toward a commercial service in Phoenix since it began testing self-driving Chrysler Pacifica minivans in suburbs like Chandler in 2016.

The following year, Waymo launched its early rider program. The company also started testing empty self-driving minivans on public streets that year.

Waymo began in May 2018 to allow some early riders to hail a self-driving minivan without a human test driver behind the wheel. More recently, the company launched a public transit program in Phoenix focused on delivering people to bus stops and train and light-rail stations.

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This self-driving AI faced off against a champion racer (kind of)

Posted by | artificial intelligence, Audi, automotive, Gadgets, hardware, robotics, science, self-driving cars, stanford, Stanford University, Transportation | No Comments

Developments in the self-driving car world can sometimes be a bit dry: a million miles without an accident, a 10 percent increase in pedestrian detection range, and so on. But this research has both an interesting idea behind it and a surprisingly hands-on method of testing: pitting the vehicle against a real racing driver on a course.

To set expectations here, this isn’t some stunt, it’s actually warranted given the nature of the research, and it’s not like they were trading positions, jockeying for entry lines, and generally rubbing bumpers. They went separately, and the researcher, whom I contacted, politely declined to provide the actual lap times. This is science, people. Please!

The question which Nathan Spielberg and his colleagues at Stanford were interested in answering has to do with an autonomous vehicle operating under extreme conditions. The simple fact is that a huge proportion of the miles driven by these systems are at normal speeds, in good conditions. And most obstacle encounters are similarly ordinary.

If the worst should happen and a car needs to exceed these ordinary bounds of handling — specifically friction limits — can it be trusted to do so? And how would you build an AI agent that can do so?

The researchers’ paper, published today in the journal Science Robotics, begins with the assumption that a physics-based model just isn’t adequate for the job. These are computer models that simulate the car’s motion in terms of weight, speed, road surface, and other conditions. But they are necessarily simplified and their assumptions are of the type to produce increasingly inaccurate results as values exceed ordinary limits.

Imagine if such a simulator simplified each wheel to a point or line when during a slide it is highly important which side of the tire is experiencing the most friction. Such detailed simulations are beyond the ability of current hardware to do quickly or accurately enough. But the results of such simulations can be summarized into an input and output, and that data can be fed into a neural network — one that turns out to be remarkably good at taking turns.

The simulation provides the basics of how a car of this make and weight should move when it is going at speed X and needs to turn at angle Y — obviously it’s more complicated than that, but you get the idea. It’s fairly basic. The model then consults its training, but is also informed by the real-world results, which may perhaps differ from theory.

So the car goes into a turn knowing that, theoretically, it should have to move the wheel this much to the left, then this much more at this point, and so on. But the sensors in the car report that despite this, the car is drifting a bit off the intended line — and this input is taken into account, causing the agent to turn the wheel a bit more, or less, or whatever the case may be.

And where does the racing driver come into it, you ask? Well, the researchers needed to compare the car’s performance with a human driver who knows from experience how to control a car at its friction limits, and that’s pretty much the definition of a racer. If your tires aren’t hot, you’re probably going too slow.

The team had the racer (a “champion amateur race car driver,” as they put it) drive around the Thunderhill Raceway Park in California, then sent Shelley — their modified, self-driving 2009 Audi TTS — around as well, ten times each. And it wasn’t a relaxing Sunday ramble. As the paper reads:

Both the automated vehicle and human participant attempted to complete the course in the minimum amount of time. This consisted of driving at accelerations nearing 0.95g while tracking a minimum time racing trajectory at the the physical limits of tire adhesion. At this combined level of longitudinal and lateral acceleration, the vehicle was able to approach speeds of 95 miles per hour (mph) on portions of the track.

Even under these extreme driving conditions, the controller was able to consistently track the racing line with the mean path tracking error below 40 cm everywhere on the track.

In other words, while pulling a G and hitting 95, the self-driving Audi was never more than a foot and a half off its ideal racing line. The human driver had much wider variation, but this is by no means considered an error — they were changing the line for their own reasons.

“We focused on a segment of the track with a variety of turns that provided the comparison we needed and allowed us to gather more data sets,” wrote Spielberg in an email to TechCrunch. “We have done full lap comparisons and the same trends hold. Shelley has an advantage of consistency while the human drivers have the advantage of changing their line as the car changes, something we are currently implementing.”

Shelley showed far lower variation in its times than the racer, but the racer also posted considerably lower times on several laps. The averages for the segments evaluated were about comparable, with a slight edge going to the human.

This is pretty impressive considering the simplicity of the self-driving model. It had very little real-world knowledge going into its systems, mostly the results of a simulation giving it an approximate idea of how it ought to be handling moment by moment. And its feedback was very limited — it didn’t have access to all the advanced telemetry that self-driving systems often use to flesh out the scene.

The conclusion is that this type of approach, with a relatively simple model controlling the car beyond ordinary handling conditions, is promising. It would need to be tweaked for each surface and setup — obviously a rear-wheel-drive car on a dirt road would be different than front-wheel on tarmac. How best to create and test such models is a matter for future investigation, though the team seemed confident it was a mere engineering challenge.

The experiment was undertaken in order to pursue the still-distant goal of self-driving cars being superior to humans on all driving tasks. The results from these early tests are promising, but there’s still a long way to go before an AV can take on a pro head-to-head. But I look forward to the occasion.

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Mobileye CEO clowns on Nvidia for allegedly copying self-driving car safety scheme

Posted by | artificial intelligence, automotive, autonomous vehicles, Gadgets, hardware, Intel, Mobileye, nvidia, robotics, self-driving cars, TC, Transportation | No Comments

While creating self-driving car systems, it’s natural that different companies might independently arrive at similar methods or results — but the similarities in a recent “first of its kind” Nvidia proposal to work done by Mobileye two years ago were just too much for the latter company’s CEO to take politely.

Amnon Shashua, in a blog post on parent company Intel’s news feed cheekily titled “Innovation Requires Originality, openly mocks Nvidia’s “Safety Force Field,” pointing out innumerable similarities to Mobileye’s “Responsibility Sensitive Safety” paper from 2017.

He writes:

It is clear Nvidia’s leaders have continued their pattern of imitation as their so-called “first-of-its-kind” safety concept is a close replica of the RSS model we published nearly two years ago. In our opinion, SFF is simply an inferior version of RSS dressed in green and black. To the extent there is any innovation there, it appears to be primarily of the linguistic variety.

Now, it’s worth considering the idea that the approach both seem to take is, like many in the automotive and autonomous fields and others, simply inevitable. Car makers don’t go around accusing each other of using the similar setup of four wheels and two pedals. It’s partly for this reason, and partly because the safety model works better the more cars follow it, that when Mobileye published its RSS paper, it did so publicly and invited the industry to collaborate.

Many did, and as Shashua points out, including Nvidia, at least for a short time in 2018, after which Nvidia pulled out of collaboration talks. To do so and then, a year afterwards, propose a system that is, if not identical, then at least remarkably similar, and without crediting or mentioning Mobileye is suspicious to say the least.

The (highly simplified) foundation of both is calculating a set of standard actions corresponding to laws and human behavior that plan safe maneuvers based on the car’s own physical parameters and those of nearby objects and actors. But the similarities extend beyond these basics, Shashua writes (emphasis his):

RSS defines a safe longitudinal and a safe lateral distance around the vehicle. When those safe distances are compromised, we say that the vehicle is in a Dangerous Situation and must perform a Proper Response. The specific moment when the vehicle must perform the Proper Response is called the Danger Threshold.

SFF defines identical concepts with slightly modified terminology. Safe longitudinal distance is instead called “the SFF in One Dimension;” safe lateral distance is described as “the SFF in Higher Dimensions.”  Instead of Proper Response, SFF uses “Safety Procedure.” Instead of Dangerous Situation, SFF replaces it with “Unsafe Situation.” And, just to be complete, SFF also recognizes the existence of a Danger Threshold, instead calling it a “Critical Moment.”

This is followed by numerous other close parallels, and just when you think it’s done, he includes a whole separate document (PDF) showing dozens of other cases where Nvidia seems (it’s hard to tell in some cases if you’re not closely familiar with the subject matter) to have followed Mobileye and RSS’s example over and over again.

Theoretical work like this isn’t really patentable, and patenting wouldn’t be wise anyway, since widespread adoption of the basic ideas is the most desirable outcome (as both papers emphasize). But it’s common for one R&D group to push in one direction and have others refine or create counter-approaches.

You see it in computer vision, where for example Google boffins may publish their early and interesting work, which is picked up by FAIR or Uber and improved or added to in another paper 8 months later. So it really would have been fine for Nvidia to publicly say “Mobileye proposed some stuff, that’s great but here’s our superior approach.”

Instead there is no mention of RSS at all, which is strange considering their similarity, and the only citation in the SFF whitepaper is “The Safety Force Field, Nvidia, 2017,” in which, we are informed on the very first line, “the precise math is detailed.”

Just one problem: This paper doesn’t seem to exist anywhere. It certainly was never published publicly in any journal or blog post by the company. It has no DOI number and doesn’t show up in any searches or article archives. This appears to be the first time anyone has ever cited it.

It’s not required for rival companies to be civil with each other all the time, but in the research world this will almost certainly be considered poor form by Nvidia, and that can have knock-on effects when it comes to recruiting and overall credibility.

I’ve contacted Nvidia for comment (and to ask for a copy of this mysterious paper). I’ll update this post if I hear back.

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Gates-backed Lumotive upends lidar conventions using metamaterials

Posted by | accelerator, automotive, autonomous vehicles, Bill Gates, Gadgets, hardware, Intellectual Ventures, lasers, Lidar, Lumotive, robotics, science, self-driving cars, TC, Transportation | No Comments

Pretty much every self-driving car on the road, not to mention many a robot and drone, uses lidar to sense its surroundings. But useful as lidar is, it also involves physical compromises that limit its capabilities. Lumotive is a new company with funding from Bill Gates and Intellectual Ventures that uses metamaterials to exceed those limits, perhaps setting a new standard for the industry.

The company is just now coming out of stealth, but it’s been in the works for a long time. I actually met with them back in 2017 when the project was very hush-hush and operating under a different name at IV’s startup incubator. If the terms “metamaterials” and “Intellectual Ventures” tickle something in your brain, it’s because the company has spawned several startups that use intellectual property developed there, building on the work of materials scientist David Smith.

Metamaterials are essentially specially engineered surfaces with microscopic structures — in this case, tunable antennas — embedded in them, working as a single device.

Echodyne is another company that used metamaterials to great effect, shrinking radar arrays to pocket size by engineering a radar transceiver that’s essentially 2D and can have its beam steered electronically rather than mechanically.

The principle works for pretty much any wavelength of electromagnetic radiation — i.e. you could use X-rays instead of radio waves — but until now no one has made it work with visible light. That’s Lumotive’s advance, and the reason it works so well.

Flash, 2D and 1D lidar

Lidar basically works by bouncing light off the environment and measuring how and when it returns; this can be accomplished in several ways.

Flash lidar basically sends out a pulse that illuminates the whole scene with near-infrared light (905 nanometers, most likely) at once. This provides a quick measurement of the whole scene, but limited distance as the power of the light being emitted is limited.

2D or raster scan lidar takes an NIR laser and plays it over the scene incredibly quickly, left to right, down a bit, then does it again, again and again… scores or hundreds of times. Focusing the power into a beam gives these systems excellent range, but similar to a CRT TV with an electron beam tracing out the image, it takes rather a long time to complete the whole scene. Turnaround time is naturally of major importance in driving situations.

1D or line scan lidar strikes a balance between the two, using a vertical line of laser light that only has to go from one side to the other to complete the scene. This sacrifices some range and resolution but significantly improves responsiveness.

Lumotive offered the following diagram, which helps visualize the systems, although obviously “suitability” and “too short” and “too slow” are somewhat subjective:

The main problem with the latter two is that they rely on a mechanical platform to actually move the laser emitter or mirror from place to place. It works fine for the most part, but there are inherent limitations. For instance, it’s difficult to stop, slow or reverse a beam that’s being moved by a high-speed mechanism. If your 2D lidar system sweeps over something that could be worth further inspection, it has to go through the rest of its motions before coming back to it… over and over.

This is the primary advantage offered by a metamaterial system over existing ones: electronic beam steering. In Echodyne’s case the radar could quickly sweep over its whole range like normal, and upon detecting an object could immediately switch over and focus 90 percent of its cycles tracking it in higher spatial and temporal resolution. The same thing is now possible with lidar.

Imagine a deer jumping out around a blind curve. Every millisecond counts because the earlier a self-driving system knows the situation, the more options it has to accommodate it. All other things being equal, an electronically steered lidar system would detect the deer at the same time as the mechanically steered ones, or perhaps a bit sooner; upon noticing this movement, it could not just make more time for evaluating it on the next “pass,” but a microsecond later be backing up the beam and specifically targeting just the deer with the majority of its resolution.

Just for illustration. The beam isn’t some big red thing that comes out.

Targeted illumination would also improve the estimation of direction and speed, further improving the driving system’s knowledge and options — meanwhile, the beam can still dedicate a portion of its cycles to watching the road, requiring no complicated mechanical hijinks to do so. Meanwhile, it has an enormous aperture, allowing high sensitivity.

In terms of specs, it depends on many things, but if the beam is just sweeping normally across its 120×25 degree field of view, the standard unit will have about a 20Hz frame rate, with a 1000×256 resolution. That’s comparable to competitors, but keep in mind that the advantage is in the ability to change that field of view and frame rate on the fly. In the example of the deer, it may maintain a 20Hz refresh for the scene at large but concentrate more beam time on a 5×5 degree area, giving it a much faster rate.

Meta doesn’t mean mega-expensive

Naturally one would assume that such a system would be considerably more expensive than existing ones. Pricing is still a ways out — Lumotive just wanted to show that its tech exists for now — but this is far from exotic tech.

CG render of a lidar metamaterial chip.The team told me in an interview that their engineering process was tricky specifically because they designed it for fabrication using existing methods. It’s silicon-based, meaning it can use cheap and ubiquitous 905nm lasers rather than the rarer 1550nm, and its fabrication isn’t much more complex than making an ordinary display panel.

CTO and co-founder Gleb Akselrod explained: “Essentially it’s a reflective semiconductor chip, and on the surface we fabricate these tiny antennas to manipulate the light. It’s made using a standard semiconductor process, then we add liquid crystal, then the coating. It’s a lot like an LCD.”

An additional bonus of the metamaterial basis is that it works the same regardless of the size or shape of the chip. While an inch-wide rectangular chip is best for automotive purposes, Akselrod said, they could just as easily make one a quarter the size for robots that don’t need the wider field of view, or a larger or custom-shape one for a specialty vehicle or aircraft.

The details, as I said, are still being worked out. Lumotive has been working on this for years and decided it was time to just get the basic information out there. “We spend an inordinate amount of time explaining the technology to investors,” noted CEO and co-founder Bill Colleran. He, it should be noted, is a veteran innovator in this field, having headed Impinj most recently, and before that was at Broadcom, but is perhaps is best known for being CEO of Innovent when it created the first CMOS Bluetooth chip.

Right now the company is seeking investment after running on a 2017 seed round funded by Bill Gates and IV, which (as with other metamaterial-based startups it has spun out) is granting Lumotive an exclusive license to the tech. There are partnerships and other things in the offing, but the company wasn’t ready to talk about them; the product is currently in prototype but very showable form for the inevitable meetings with automotive and tech firms.

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Lyft’s self-driving pilot with nuTonomy begins rolling out in Boston

Posted by | automotive, Lyft, Mobile, NuTonomy, self-driving cars, Startups, TC | No Comments

 Lyft is beginning its self-driving car pilot with self-driving car company nuTonomy in Boston as it looks to ramp up its self-driving efforts, and is now matching its riders with self-driving vehicles in parts of Boston, nuTonomy said today. Lyft announced its partnership with nuTonomy in June this year, indicating that the pilots would begin in the coming months. Read More

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Uber shows off its autonomous driving program’s snazzy visualization tools

Posted by | artificial intelligence, automotive, autonomous vehicles, Gadgets, self-driving cars, TC, Transportation, Uber | No Comments

 Uber’s engineering blog has just posted an interesting piece on the company’s web-based tool for exploring and visualizing data from self-driving car research. It’s a smart look at an impressive platform, and definitely has nothing to do with a long piece published last week lauding a similar platform in use by one of Uber’s most serious rivals, Waymo. Read More

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Test your ethics around killer AI cars with this Moral Machine game

Posted by | artificial intelligence, automotive, ethics, Gaming, Opinion, self-driving cars, TC, Transportation | No Comments

what-should-the-self-driving-car-do Should a self-driving car full of old folks crash to avoid puppies in the cross-walk? Is it OK to run over two criminals if you save one doctor? Whose lives are worth more, seven-year-olds or senior citizens?
This new game called the “Moral Machine” from MIT’s researchers lets you make the calls in the famous “trolley problem” and see analytics about your ethics. Read More

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Autonomous buses take to the busy streets of Helsinki

Posted by | artificial intelligence, automotive, autonomous vehicles, Easymile, Europe, finland, Gadgets, robotics, self-driving cars, TC, transit, Transportation | No Comments

easymile ez10 Make way for robot! Two driverless buses will be hitting the streets in Helsinki for some real-world testing for the next month or so. The EasyMile minibuses have previously been tested on public roads in Finland and elsewhere, but this is the first time they’ll be mixing it up with everyday traffic there. Read More

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In-car cameras let autonomous vehicles track passengers as well as pedestrians

Posted by | AI, artificial intelligence, automotive, autonomous vehicles, Computer Vision, fraunhofer, Gadgets, self-driving cars, TC | No Comments

A member of the media test drives a Tesla Motors Inc. Model S car equipped with Autopilot in Palo Alto, California, U.S., on Wednesday, Oct. 14, 2015. Tesla Motors Inc. will begin rolling out the first version of its highly anticipated "autopilot" features to owners of its all-electric Model S sedan Thursday. Autopilot is a step toward the vision of autonomous or self-driving cars, and includes features like automatic lane changing and the ability of the Model S to parallel park for you. Photographer: David Paul Morris/Bloomberg via Getty Images Powerful sensors and software have made autonomous cars impressively aware of their surroundings — but it isn’t just obstacles, pedestrians, and other cars that a vehicle’s AI needs to watch out for. It also needs to know what’s going on inside itself, and researchers from Fraunhofer are hard at work making that happen. Read More

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