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February 24, 2019
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Aurora cofounder and CEO Chris Urmson on the company’s new investor, Amazon, and much more

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You might not think of self-driving technologies and politics having much in common, but at least in one way, they overlap meaningfully: yesterday’s enemy can be tomorrow’s ally.

Such was the message we gleaned Thursday night, at a small industry event in San Francisco, where we had the chance to sit down with Chris Urmson, the cofounder and CEO of Aurora, a company that (among many others) is endeavoring to make self-driving technologies a safer and more widely adopted alternative to human drivers.

It was a big day for Urmson. Earlier the same day, his two-year-old company announced a whopping $530 million in Series B funding, a round that was led by top firm Sequoia Capital and that included “significant investment” from T. Rowe Price and Amazon.

The financing for Aurora — which is building what it calls a “driver” technology that it expects to eventually integrate into cars built by Volkswagen, Hyundai, and China’s Byton, among others —  is highly notable, even in a sea of giant fundings. Not only does it represent Sequoia’s biggest bet yet on any kind of self-driving technology, it’s also an “incredible endorsement” from T. Rowe Price, said Urmson Thursday night, suggesting it demonstrates that the money management giant “thinks long term and strategically [that] we’re the independent option to self-driving cars.”

Even more telling, perhaps, is the participation of Amazon, which is in constant competition to be the world’s most valuable company, and whose involvement could lead to variety of scenarios down the road, from Aurora powering delivery fleets overseen by Amazon, to Amazon acquiring Aurora outright. Amazon has already begun marketing more aggressively to global car companies and Tier 1 suppliers that are focused on building connected products, saying its AWS platform can help them speed their pace of innovation and lower their cost structures. In November, it also debuted a global, autonomous racing league for 1/18th scale, radio-controlled, self-driving four-wheeled race cars that are designed to help developers learn about reinforcement learning, a type of machine learning. Imagine what it could learn from Aurora.

Indeed, at the event, Urmson said that as Aurora had “constructed our funding round, [we were] very much thinking strategically about how to be successful in our mission of building a driver. And one thing that a driver can do is move people, but it can also move goods. And it’s harder to think of a company where moving goods is more important than Amazon.” Added Urmson, “Having the opportunity to have them partner with us in this funding round, and [talk about] what we might build in the future is awesome.” (Aurora’s site also now features language about “transforming the way people and goods move.”)

The interest of Amazon, T. Rowe, Sequoia and Aurora’s other backers isn’t surprising. Urmson was the formal technical lead of Google’s self-driving car program (now Waymo) . One of his cofounders, Drew Bagnell, is a machine learning expert who still teaches at Carnegie Mellon and was formerly the head of Uber’s autonomy and perception team. Aurora’s third cofounder is Sterling Anderson, the former program manager of Tesla’s Autopilot team.

Aurora’s big round seemingly spooked Tesla investors, in fact, with shares in the electric car maker dropping as a media outlets reported on the details. The development seems like just the type of possibility that had Tesla CEO Elon Musk unsettled when Aurora got off the ground a couple of years ago, and Tesla almost immediately filed a lawsuit against it, accusing Urmson and Anderson of trying poach at least a dozen Tesla engineers and accusing Anderson of taking confidential information and destroying the  evidence “in an effort to cover his tracks.”

That suit was dropped two and a half weeks later in a settlement that saw Aurora pay $100,000. Anderson said at the time the amount was meant to cover the cost of an independent auditor to scour Aurora’s systems for confidential Tesla information. Urmson reiterated on Thursday night that it was purely an “economic decision” meant to keep Aurora from getting further embroiled in an expansive spat.

But Urmson, who has previously called the lawsuit “classy,” didn’t take the bait on Thursday when asked about Musk, including whether he has talked in the last two years with Musk (no), and whether Aurora might need Tesla in the future (possibly). Instead of lord Aurora’s momentum over the company, Urmson said that Aurora and Tesla “got off on the wrong foot.” Laughing a bit, he went on to lavish some praise on the self-driving technology that lives inside Tesla cars, adding that “if there’s an opportunity to work them in the future, that’d be great.”

Aurora, which is also competing for now against the likes of Uber, also sees Uber as a potential partner down the line, said Urmson. Asked about the company’s costly self-driving efforts, whose scale has been drastically downsized in the eleven months since one of its vehicles struck and killed a pedestrian in Arizona, Urmson noted simply that Aurora is “in the business of delivering the driver, and Uber needs a lot of drivers, so we think it would be wonder to partner with them, to partner with Lyft, to partner [with companies with similar ambitions] globally. We see those companies as partners in the future.”

He’d added when asked for more specifics that there’s “nothing to talk about right now.”

Before Thursday’s event, Aurora had sent us some more detailed information about the four divisions that currently employ the 200 people that make up the company, a number that will obviously expand with its new round, as will the testing it’s doing, both on California roads and in Pittsburgh, where it also has a sizable presence. We didn’t have a chance to run them during our conversation with Urmson, but we thought they were interesting and that you might think so, too.

Below, for example, is the “hub” of the Aurora Driver. This is the computer system that powers, coordinates and fuses signals from all of the vehicle’s sensors, executes the software and controls the vehicle. Aurora says it’s designing the Aurora Driver to seamlessly integrate with a wide variety of vehicle platforms from different makes, models and classes with the goal of delivering the benefits of its technology broadly.

Below is a visual representation of Aurora’s perception system, which the company says is able to understand complex urban environments where vehicles need to safely navigate amid many moving objects, including bikes, scooters, pedestrians, and cars.

It didn’t imagine it would at the outset, but Aurora is building its own mapping system to ensure what it (naturally) calls the highest level of precision and scalability, so vehicles powered by the company can understand where they are and update the maps as the world changes.

We asked Urmson if, when the tech is finally ready to go into cars, they will white-label the technology or else use Aurora’s brand as a selling point. He said the matter hasn’t been decided yet but seemed to suggest that Aurora is leaning in the latter direction. He also said the technology would be installed on the carmakers’ factory floors (with Aurora’s help).

One of the ways that Aurora says it’s able to efficiently develop a robust “driver” is to build its own simulation system. It uses its simulator to test its software with different scenarios that vehicles encounter on the road, which it says enables repeatable testing that’s impossible to achieve by just driving more miles.

Aurora’s motion planning team works closely with the perception team to create a system that both detects the important objects on and around the road, and tries to accurately predict how they will move in the future. The ability to capture, understand, and predict the motion of other objects is critical if the tech is going to navigate real world scenarios in dense urban environments, and Urmson has said in the past that Aurora has crafted its related workflow in a way that’s superior to competitors that send the technology back and forth.

Specifically, he told The Atlantic last year: “The classic way you engineer a system like this is that you have a team working on perception. They go out and make it as good as they can and they get to a plateau and hand it off to the motion-planning people. And they write the thing that figures out where to stop or how to change a lane and it deals with all the noise that’s in the perception system because it’s not seeing the world perfectly. It has errors. Maybe it thinks it’s moving a little faster or slower than it is. Maybe every once in a while it generates a false positive. The motion-planning system has to respond to that.

“So the motion-planning people are lagging behind the perception people, but they get it all dialed in and it’s working well enough—as well as it can with that level of perception—and then the perception people say, ‘Oh, but we’ve got a new push [of code].’ Then the motion-planning people are behind the eight ball again, and their system is breaking when it shouldn’t.”

We also asked Urmson about Google, whose self-driving unit was renamed Waymo as it spun out from the Alphabet umbrella as its own company. He was highly diplomatic, saying only good things about the company and, when asked if they’d ever challenged him on anything since leaving, answering that they had not.

Still, he told as one of the biggest advantage that Aurora enjoys is that it was able to use the learnings of its three founders and to start from scratch, whereas the big companies from which each has come cannot completely start over.

As he told TechCrunch in a separate interview last year when asked how Aurora tests its technology, then it comes to self-driving tech, size matters less than one might imagine. “There’s this really easy metric that everyone is using, which is number of miles driven, and it’s one of those things that was really convenient for me in my old place [Google] because we’re out there and we were doing a hell of a lot more than anybody else was at the time, and so it was an easy number to talk about. What’s lost in that, though, is it’s not really the volume of the miles that you drive.” It’s about the quality of the data, he’d continued, suggesting that, for now, at least, Aurora’s is hard to beat.

News Source = techcrunch.com

Transportation Weekly: Amazon’s secret acquisition and all the AV feels

in Amazon/Anthony Levandowski/Argo AI/aurora/Automotive/carsharing/Chris Urmson/Craft Ventures/Daimler/david sacks/Delhi/Elon Musk/Google/India/Kirsten Korosec/Lead Edge Capital/Maxwell Technologies/Megan Rose Dickey/mike volpi/myTaxi/Politics/self-driving cars/Sequoia/starship technologies/t.rowe price/TC/TechCrunch/Tesla/Transportation/Tusk Ventures/Uber/valor equity partners/waymo by

Welcome to Transportation Weekly; I’m your host Kirsten Korosec, senior transportation reporter at TechCrunch. I cover all the ways people and goods move from Point A to Point B — today and in the future — whether it’s by bike, bus, scooter, car, train, truck, robotaxi or rocket. Sure, let’s include hyperloop and eVTOLs, or air taxis, too.

Yup, another transportation newsletter. But I promise this one will be different. Here’s how.

Newsletters can be great mediums for curated news — a place that rounds up all the important articles a reader might have missed in any given week. We want to do a bit more.

We’re doubling down on the analysis and adding a heaping scoop of original reporting and well, scoops. You can expect Q&As with the most interesting people in transportation, insider tips, and data from that white paper you didn’t have time to read. This isn’t a lone effort either. TechCrunch senior reporter Megan Rose Dickey, who has been writing about micro mobility since before the scooter boom times of 2017, will be weighing in each week in our “Tiny But Mighty Mobility” section below. Follow her @meganrosedickey.

Consider this a soft launch. There might be content you like or something you hate. Feel free to reach out to me at kirsten.korosec@techcrunch.com to share those thoughts, opinions, or tips.

Eventually, we’ll have a way for readers to sign up and have Transportation Weekly delivered each week via email. For now, follow me on Twitter @kirstenkorosec to ensure you see it each week.

Now, let’s get to the good stuff.


ONM …

There are OEMs in the automotive world. And here, (wait for it) there are ONMs — original news manufacturers.


This is where investigative reporting, enterprise pieces and analysis on transportation will live.

We promised scoops in Transportation Weekly and here is one. If you don’t know journalist Mark Harris, you should. He’s an intrepid gumshoeing reporter who TechCrunch has been lucky enough to hire as a freelancer. Follow him @meharris.

Amazon quietly acquired robotics company Dispatch to build Scout

dispatch-amazon-scout
Remember way back in January when Amazon introduced Scout, their autonomous delivery bot? There was speculation at the time that Amazon had bought the Estonian-based company Starship Technologies. Harris did some investigating and discovered some of the intellectual property and technology behind Scout likely came from a small San Francisco startup called Dispatch that Amazon stealthily acquired in 2017.

It’s time to stop thinking about Amazon as just an e-commerce company. It’s a gigantic logistics company, probably the biggest on the planet, with a keen interest — and the cash to pursue those interests — in automation. Think beyond Scout. In fact, wander on down this post to the deal of the week.


Dig In

Each week, transportation weekly will spend a little extra time on an approach, policy, tech or the people behind it in our ‘Dig In” section. We’ll run the occasional column here, too.

This week features a conversation with Dmitri Dolgov, the CTO and VP of engineering at Waymo, the former Google self-driving project that spun out to become a business under Alphabet.

waymo-google-10-years

Ten years ago, right around now, about a dozen engineers started working on Project Chauffeur, which would turn into the Google self-driving project and eventually become an official company called Waymo. Along the way, the project would give rise to a number of high-profile engineers who would go on to create their own companies. It’s a list that includes Aurora co-founder Chris Urmson, Argo AI co-founder Bryan Salesky and Anthony Levandowski, who helped launch Otto and more recently Pronto.ai.

What might be less known is that many of those in the original dozen are still at Waymo, including Dolgov, Andrew Chatham, Dirk Haehnel, Nathaniel Fairfield and Mike Montemerlo.

Dolgov and I talked about the early days, challenges and what’s next. A couple of things that stood out during our chat.

There is a huge difference between having a prototype that can do something once or twice or four times versus building a product that people can start using in their daily lives. And it is, especially in this field, very easy to make progress on these kinds of one-off challenges.

Dolgov’s take on how engineers viewed the potential of the project 10 years ago …

I also use our cars every day to get around, this is how I got to work today. This is how I run errands around here in Mountain View and Palo Alto.


A little bird …

We hear a lot. But we’re not selfish. Let’s share.
blinky-cat-birdAn early investor, or investors, in Bird appear to be selling some of their shares in the scooter company, per a tip backed up by data over at secondary trading platform EquityZen. That’s not crazy considering the company is valued at $2 billion-ish. Seed investors should take some money off the table once a company reaches that valuation.

We’ve heard that David Sacks at Craft Ventures hasn’t sold a single Bird share. We hear Tusk Ventures hasn’t sold, either. That leaves a few others, including Goldcrest Capital, which was the lone seed investor, and then Series A participants Lead Edge Capital, M13, and Valor Equity Partners.

Got a tip or overheard something in the world of transportation? Email me or send a direct message to @kirstenkorosec.

While you’re over at Twitter, check out this cheeky account @SDElevator. We can’t guarantee how much of the content is actually “overheard” and how much is manufactured for the laughs, but it’s a fun account to peruse from time to time.

Another new entrant to the mobility parody genre is @HeardinMobilty.


Deal of the week

There’s so much to choose from this week, but Aurora’s more than $530 million Series B funding round announced Thursday morning is the winner.

The upshot? It’s not just that Aurora is now valued at more than $2.5 billion. The primary investors in the round — Sequoia as lead and “significant” investments from Amazon and T. Rowe Price — suggests Aurora’s full self-driving stack is headed for other uses beyond shuttling people around in autonomous vehicles. Perhaps delivery is next.

And believe it or not, the type of investor in this round tells me that we can expect another capital raise. Yes, Aurora has lots of runway now as well as three publicly named customers. But investors like Sequoia, which led the round and whose partner Carl Eschenbach is joining Aurora’s board, T. Rowe Price and Amazon along with repeaters like Index Ventures (general partner Mike Volpi is also on the board) have patience, access to cash and long-term strategic thinking. Expect more from them.

Other deals that got our attention this week:


Snapshot

Speaking of deals and Tesla … the automaker’s $218 million acquisition this month of Maxwell Technologies got me thinking about companies it has targeted in the past.

So, we went ahead and built a handy chart to provide a snapshot view of some of Tesla’s noteworthy acquisitions. tesla-acquisitions-chart1

One note: Tesla CEO Elon Musk tweeted in 2018 that the company had acquired trucking carrier companies to help improve its delivery logistics. We’ve dug in and have yet to land on the company, or companies, Tesla acquired.

The deals that got away are just as interesting. That list includes a reported $325 million offer to buy Simbol Materials, the startup that was extracting small amounts of lithium near the Salton Sea east of San Diego.


Tiny but mighty mobility

Between Lime’s $310 million Series D round and the seemingly never-ending battle to operate electric scooters in San Francisco, it’s clear that micro mobility is not so micro.

Lime, a shared electric scooter and bikeshare startup, has now raised north of $800 million in total funding, surpassing key competitor Bird’s total funding of $415 million. Thanks to this week’s round of funding, Lime’s micromobility business is now worth $2.4 billion.

Lime currently operates its bikes and scooters in more than 100 cities worldwide. Over in San Francisco, however, Lime has yet to deploy any of its modes of transportation. Since last March, there’s been an ongoing battle among scooter operators to deploy their services in the city. The city ultimately selected Skip and Scoot for the pilot programs, leaving the likes of Lime, Uber’s JUMP and Spin to appeal the decision.

A neutral hearing officer has since determined SF’s process for determining scooter operators was fair, but the silver lining for the likes of JUMP, Spin and most likely, Lime, is that the city may open up its pilot program to allow additional operators beginning in April.


Notable reads

Two recent studies got my attention.

The first is from Bike Pittsburgh, an advocacy group and partner of Uber, that published the findings from its latest AV survey based on responses from local residents. The last time they conducted a similar survey was in 2017.

The takeaway: people there, who are among the most exposed to autonomous vehicles due to all the AV testing on public roads, are getting used to it. A bit more than 48 percent of respondents said they approve of public AV testing in Pittsburgh, down slightly from 49 percent approval rating in 2017. 

  • 21.21% somewhat approve
  • 11.62% neutral
  • 10.73% somewhat disapprove
  • 8.73% disapprove

One standout result was surrounding responses about the fatal accident in Tempe, Arizona involving a self-driving Uber that struck and killed pedestrian Elaine Herzberg in March 2018. Survey participants were asked “As a pedestrian or a bicyclist how did this change event and it’s outcome change your opinion about sharing the road with AVs?”

Some 60 percent of respondents claimed no change in their opinion, with another 37 percent claiming that it negatively changed their opinion. Nearly 3 percent claimed their opinion changed positively toward the technology.

Bike Pittsburgh noted that the survey elicited passionate open-ended responses. 

“The incident did not turn too many people off of AV technology in general,” according to Bike Pittsburgh. “Rather it did lead to a growing distrust of the companies themselves, specifically with Uber and how they handled the fatality.”

The other study, Securing the Modern Vehicle: A Study of Automotive Industry Cybersecurity Practices, was released by Synopsys, Inc.and SAE International.

The results, based on a survey of global automotive manufacturers and suppliers conducted by Ponemon Institute, doesn’t assuage my concerns. If anything, it puts me on alert.

  • 84% of automotive professionals have concerns that their organizations’ cybersecurity practices are not keeping pace with evolving technologies
  • 30% of organizations don’t have an established cybersecurity program or team
  • 63% test less than half of the automotive technology they develop for security vulnerabilities.

Testing and deployments

Pilots, pilots everywhere. A couple of interesting mobility pilots and deployments stand out.

Optimus Ride, the Boston-based MIT spinoff, has made a deal with Brookfield Properties to provide rides in its small self-driving vehicles at Halley Rise – a new $1.4 billion mixed-use development in Virginia. 

This is an example of where we see self-driving vehicles headed — for now. Small deployments that are narrowly focused in geography with a predictable customer base are the emerging trend of 2019. Expect more of them.

And there’s a reason why, these are the kinds of pilots that will deliver the data needed to improve their technology, as well as test out business models —gotta figure out how to money with AVs eventually — hone in fleet operational efficiency, placate existing investors while attracting new ones, and recruit talent.

Another deployment in the more conventional ride-hailing side of mobility is with Beat, the startup that has focused its efforts on Latin America.

Beat was founded by Nikos Drandakis in 2011 initially as Taxibeat. The startup acquired by Daimler’s mytaxi in February 2017 and Drandakis still runs the show. The company was focused on Europe but shifted to Latin America, and it’s made all the difference. (Beat is still available in Athens, Greece.) Beat has launched in Lima, Peru, Santiago, Chile and Bogota, Colombia and now boasts 200,000 registered drivers. 

Now it’s moving into Mexico, where more competitors exist. The company just started registering and screening drivers in Mexico City as it prepares to offer rides for passengers this month. 

TechCrunch spoke at length with Drandakis. Look out for a deeper dive soon.

Until next week, nos vemos.

News Source = techcrunch.com

Waymo CTO on the company’s past, present and what comes next

in Anthony Levandowski/Argo AI/Artificial Intelligence/aurora/Automotive/avs/Chris Urmson/Delhi/Google/India/Larry Page/Politics/sebastian thrun/self-driving car/TC/TechCrunch/Transportation/waymo by

A decade ago, about a dozen or so engineers gathered at Google’s main Mountain View campus on Charleston Road to work on Project Chauffeur, a secret endeavor housed under the tech giant’s moonshot factory X.

Project Chauffeur — popularly know as the “Google self-driving car project” — kicked off in January 2009. It would eventually graduate from its project status to become a standalone company called Waymo in 2016.

The project, originally led by Sebastian Thrun, would help spark an entire ecosystem that is still developing today. Venture capitalists took notice and stampeded in, auto analysts shifted gears, regulators, urban planners and policy wonks started collecting data and considering the impact of AVs on cities.

The project would also become a springboard for a number of engineers who would go on to create their own companies. It’s a list that includes Aurora  co-founder Chris Urmson,  Argo AI co-founder Bryan Salesky as well as Anthony Levandowski, who helped launch Otto and more recently Pronto.ai.

What might be less known is that many who joined in those first weeks are still at Waymo, including Andrew Chatham, Dmitri Dolgov, Dirk Haehnel, Nathaniel Fairfield and Mike Montemerlo. Depending on how one defines “early days,” there are others like Hy Murveit, Phil Nemec, and Dan Egnor, who have been there for eight or nine years.

Dolgov, Waymo’s CTO and VP of engineering, chatted recently with TechCrunch about the early days, its 10-year anniversary, and what’s next.

Below is an excerpt of an interview with Dolgov, which has been edited for clarity and length.

TC: Let’s go back to the beginning of how you got started. Take me to those first days at the Google self-driving project.

DOLGOV: When I think about what drew me to this field, it’s always been three main things: the impact of the technology, the technology itself, and the challenges as well as the people you get to work with. It’s pretty obvious, at this point, that it can have huge implications on safety, but beyond that, it can impact efficiency and remove friction from transportation for people and things.

There is this sense of excitement that never seems to die off. I remember the first time I got to work on a self-driving car. And it was the first time when the car drove itself using software that I had written, you know, just earlier in the day. So this was back in 2007. And that completely blew my mind. (Dolgov participated in the DARPA Urban Challenge in November 2007 before the Google project launched)

TC: What were these 10, 100-mile challenges that (Google co-founder) Larry Page came up with? Can you describe that to me a little bit?

DOLGOV: This was probably the main milestone that we created for ourselves when we started this project at Google in 2009. And the challenge was to drive 10 routes, each one was 100 miles long. And you had to drive each one from beginning to end without any human intervention.

These were really well defined very clearly, crisply defined routes. So in the beginning, you’d engage the self driving mode of a car, and then had to finish the whole 100 miles on its own.

The routes were intentionally chosen to sample the full complexity of the task. In those early days, for us, it was all about understanding the complexity of the problem. All of the routes were in the Bay Area. We had some driving in urban environments, around Palo Alto,  we had one that spent a lot of time on the freeways and went to all of the bridges in the Bay Area. We had one that went from Mountain View to San Francisco, including driving through Lombard Street. We had one that went around Lake Tahoe.

We tried to cover as much of the complexity of the environment as possible. And what’s really great about that task is that it really helped us very quickly understand the core complexity of the space.

TC: How long did it take to complete these challenges?

DMITRI: It took us until the fall of 2010.

TC: It’s kind of amazing to think that the project was able to complete these challenges in 2010, and yet, there still seems to be so much more work to complete on this task.

DOLGOV: Right. But I think this is the nature of the problem. There is a huge difference between having a prototype that can do something once or twice or a handful of times versus building a product that people can start using in their daily lives. And it is, especially in this field, when we started, it’s very easy to make progress on these kinds of one-off challenges.

But what really makes it hard is an incredible level of performance that you need from your system in order to make it into a product. And that’s number one. And number two, is the very long tail of complexity of the types of problems that you encounter. Maybe you don’t see them 99% of the time, but you still have to be ready for that 1% or 1.1%.

TC:  When you think back to those early days — or maybe even more recently — was there ever a moment when where there was a software problem, or even a hardware problem that seemed insurmountable and that maybe the tech just wasn’t quite there yet?

DOLGOV: In the early days, we had all kinds of problems that we faced. In the early history of this project, we only set out to solve some problems without really knowing how we were going to get there.

You start working on the problem, and you make progress towards this. Thinking back to how these past few years have felt to me: It’s been much less of a here’s one problem, or a small number of really hard problems and we kind of hit a wall.

Instead, it’s been more like hundreds of really hard problems. None of them feel like a brick wall because, you know, the team is amazing, the technology is really powerful, and you make progress on them.

But you’re always juggling like, hundreds of these types of really complex problems, where the further you get into solving each one of them, the more you realize just how hard it really is.

So it’s been a really interesting mix: on one hand, the problem getting more difficult, the more you learn about it. But on the other hand, technology making more rapid progress and breakthroughs happening at a higher rate than you would have originally anticipated.

TC: When did you realize that this project had changed (beyond the official announcements)? When did you realize it could be a business, that it was something that could be a lot more than just solving this problem?

DOLGOV: I would describe it as more of an evolution of our thinking and investing more effort into more clearly defining the product and commercial applications of this technology.

When we started, in that very first phase, the question was, “is this even feasible? Is technology going to work?” I think it was pretty clear to everybody that if the technology succeeded then there was going to be tremendous impact.

It wasn’t exactly clear what commercial application or what product would deliver that impact. But there was just so many ways that this technology would transform the world that we didn’t spend much time worrying about that aspect of it.

When you think about it, what we’re building here is a driver: our software, our hardware —the software that runs in the car, the software that runs in the cloud. We look at the entirety of our technology stack as a driver.

There are about 3 trillion miles in the U.S. that are driven by people. In some cases, they drive themselves, in some cases, they drive other people, in some cases, they drive goods. Once you have the technology that is “the driver,” you can deploy it in all these situations. But they have their pros and cons.

Over time, our thinking on ‘what are the most attractive ones?’ and ‘in what order do we tackle them?’ has matured.

This is what they’re doing today as a result of all of that work. Ride hailing is the first commercial application that we’re pursuing. Beyond that we are working on long-haul trucking, long range deliveries. We’re interested, at some point, deploying the technology in personally owned cars, local deliveries, public transportation, so forth and so on.

TC: What application are you most excited about? The one that you think maybe is overlooked or one you’re personally the most excited about?

DOLGOV: I’m super excited about seeing the technology and the driver being deployed in, you know, across the globe and across different commercial applications. But I think the one that I am the most excited about is the one we’re pursuing as our number one target right now, which is ride hailing.

I think it has the potential to affect positively the highest number of people in the shortest amount of time.

I also use our cars every day to get around, this is how I got to work today. This is how I run errands around here in Mountain View and Palo Alto. It’s wonderful to be able to experience these cars and it just removes a lot of the friction out of transportation.

TC: So you you take a self driving car to work every day right now?

DOLGOV: Yes, but in California, they still have people in them. 

TC: How long have you been doing that?

DOLGOV: Awhile. Actually, it seems like forever.

I’ve always spent time in the cars. I think it’s really important to experience the product that you’re building and have direct experience with the technology. This was obviously the case in the early days of the project when there was a small group of us doing everything.

As the team grew, I would still make sure I would experience the technology and go on test rides at least weekly, if not more frequently.

When we started pursuing the ride-hailing application, and we build an app for it, and we built out infrastructure to make it into a user-facing product, I was one of the earlier testers.

That must have been three years ago.

TC: Did you expect it to be at this point that you are right now, 10 years ago, did you expect like 10 years from now, this is where we’re going to be? Or did it happen faster or slower than you anticipated?

DOLGOV: So for me, I think on one hand, I would not have predicted some of the breakthroughs in the technology on the hardware front, on the software and AI and machine learning back in 2009. I think the technology today is much more powerful than I would have probably said in 2009.

On the another hand, the challenge of actually building a real product and deploying it so that people can use it has turned out to be more difficult than I expected. So it’s kind of a mix.

TC: What were some of those technological breakthroughs?

DOLGOV: There were a number of things. LiDARs and radars became much more powerful.

And by powerful, I mean longer range, higher resolution and more features, if you will, in terms of the things that they can measure — richer returns of the properties of the environment. So that’s on the sensing side.

Compute, especially in the hardware-accelerated parallel computation, that’s been very powerful for the advancement of neural networks. That has been a huge boost.

Then there’s deep learning and the neural nets themselves have led to a number of breakthroughs.

TC: Yeah, with the last two examples you gave, I think of those as being breakthroughs more recently, in just the last few years. Is that about the timeframe?

DOLGOV: We’ve always used machine learning on this project, but it was a different kind of machine learning then today.

I think in 2012 is probably when, on our project, there was meaningful effort and when we were working together with Google on both the self-driving technology and deep learning.

Arguably, at the time Google was the only company in the world seriously investing in both the self driving and deep learning.

At that point, we didn’t have the hardware to be able to run those nets on the car, in real time. But there were very interesting things you could do in the cloud.

For deep learning, 2013 was a pretty big year. I think this is when ImageNet won a big competition and it was a breakthrough for deep learning. It outperformed all the other approaches in the computer vision competition.

TC: In 2009, could you imagine a world in 2019, where numerous self-driving vehicle companies would be testing on roads in California? Was that something that seemed plausible?

DOLGOV: No, no that’s not the picture I had in mind in 2009 or 2010.

In those early days of the project, people kind of laughed at us. I think the industry made fun of this project and there were multiple funny spoofs on the Google self-driving car project.

It’s been pretty amazing to go from, ‘oh there is small, group of crazy folks trying to do this science fiction thing at Google’ to this becoming a major industry that we have today with dozens, if not hundreds, of companies pursuing this.

Google’s self-driving Lexus RX 450h

TC: What will be the tipping point that will get folks on board with self driving vehicles in their city? Is it a matter of just pure saturation? Or is it something else that that all the companies, Waymo included, are responsible of helping usher in?

DOLGOV: It seems like there’s always a spectrum of people’s attitudes towards new technology and change. Some of the negative ones are more visible. But actually, my experience over the last 10 years, the positive attitude and the excitement has been overwhelmingly stronger.

What I what I have seen over and over again, in this project that really is very powerful, and that is powerful and changes people’s attitudes from, uncertainty and anxiety to excitement and comfort and trust is being able to experience the technology.

You get people into one of our cars and then go for a ride. Even people who are anxious about getting into a car with nobody behind the wheel, once they experience it and once they understand how useful of a product it is, and how well the car behaves, and they starting trusting it, that really leads to change.

As the technology rolls out and more people get to experience it firsthand, that will help.

TC: Are the biggest challenges in 2009 the same as today? What are the final cruxes that remain?

DOLGOV: In 2009, all the challenges were all about one-off problems we needed to solve and today it’s all about turning it into a product.

It’s about the presentation of this self-driving stack and about building the tools and the framework for evaluation and deployment of the technology. You know, what has stayed true is that it’s all about the speed of iteration and the ability to learn new things and solve new technical problems as we discover them.

News Source = techcrunch.com

Walmart taps startup Udelv to test autonomous grocery deliveries in Arizona

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More autonomous vehicles are poised to descend on Arizona. This time, Walmart has signed a deal with startup Udelv to test the use of autonomous vans to deliver online grocery orders to customers.

Under the agreement, Udelv will provide its second-generation autonomous delivery van, called the Newton, to Walmart to deliver groceries in Surprise, Arizona. The trial is set to begin in February, Udelv announced Tuesday at CES 2019.

The Newton, which is being shown at CES, is based on Baidu’s latest Apollo 3.5 open-source software platform.

The Walmart pilot isn’t the only deal that Udelv has locked in and announced at CES 2019. Up to 100 Udelv ADVs will be deployed in 2019 for last and middle-mile delivery on public roads in several cities throughout the country, the company said.

Udelv announced a contract with automotive aftermarket parts distribution business XL Parts to use self-driving delivery vans in Houston, Texas. Udelv said it will provide up to 10 ADVs to XL Parts, with the first vehicle being delivered in mid 2019. 

The company, which has already completed about 1,200 deliveries on public roads in San Francisco for more than a dozen paying clients, didn’t disclose the amount of the strategic investment from Japanese business giant Marubeni Corporation.  

Udelv said the collaboration between the two companies will serve to fast-track Udelv’s expansion, leveraging the buying power and various other internal resources of the Marubeni Corporation.

The deal with Walmart is small for now, but could prove to be a turning point for Udelv, if it’s successful.

The autonomous delivery vans will operate with safety drivers until both companies, as well as regulators, deem them approved for a safe removal of the safety driver, Udelv said.

These self-driving delivery vans will be able to travel at speeds of up to 60 miles per hour on urban and suburban roads, including highways. The vans are outfitted with a cargo system designed to carry up to 32 customer orders per delivery cycle. 

Walmart’s agreement with Udelv follows Walmart’s pilot program with self-driving company Waymo that launched last year. Waymo is taking its early rider program passengers to and from a Walmart store in Chandler, a suburb of Phoenix. 

News Source = techcrunch.com

Robot couriers scoop up early-stage cash

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Much of the last couple of decades of innovation has centered around finding ways to get what we want without leaving the sofa.

So far, online ordering and on-demand delivery have allowed us to largely accomplish this goal. Just point, click and wait. But there’s one catch: Delivery people. We can never all lie around ordering pizzas if someone still has to deliver them.

Enter robots. In tech-futurist circles, it’s pretty commonplace to hear predictions about how some medley of autonomous vehicles and AI-enabled bots will take over doorstep deliveries in the coming years. They’ll bring us takeout, drop off our packages and displace lots of humans who currently make a living doing these things.

If this vision does become reality, there’s a strong chance it’ll largely be due to a handful of early-stage startups currently working to roboticize last-mile delivery. Below, we take a look at who they are, what they’re doing, who’s backing them and where they’re setting up shop.

The players

Crunchbase data unearthed at least eight companies in the robot delivery space with headquarters or operations in North America that have secured seed or early-stage funding in the past couple of years.

They range from heavily funded startups to lean seed-stage operations. Silicon Valley-based Nuro, an autonomous delivery startup founded by former engineers at Alphabet’s Waymo, is the most heavily funded, having raised $92 million to date. Others have raised a few million.

In the chart below, we look at key players, ranked by funding to date, along with their locations and key investors.

Who’s your backer?

While startups may be paving the way for robot delivery, they’re not doing so alone. One of the ways larger enterprises are keeping a toehold in the space is through backing and partnering with early-stage startups. They’re joining a long list of prominent seed and venture investors also eagerly eyeing the sector.

The list of larger corporate investors includes Germany’s Daimler, the lead investor in Starship Technologies. China’s Tencent, meanwhile, is backing San Francisco-based Marble, while Toyota AI Ventures has invested in Boxbot.

As for partnering, takeout food delivery services seem to be the most active users of robot couriers.

Starship, whose bot has been described as a slow-moving, medium-sized cooler on six wheels, is making particularly strong inroads in takeout. The San Francisco- and Estonia-based company, launched by Skype founders Janus Friis and Ahti Heinla, is teaming up with DoorDash and Postmates in parts of California and Washington, DC. It’s also working with the Domino’s pizza chain in Germany and the Netherlands.

Robby Technologies, another maker of cute, six-wheeled bots, has also been partnering with Postmates in parts of Los Angeles. And Marble, which is branding its boxy bots as “your friendly neighborhood robot,” teamed up last year for a trial with Yelp in San Francisco.

San Francisco Bay Area dominates

While their visions of world domination are necessarily global, the robot delivery talent pool remains rather local.

Six of the eight seed- and early-stage startups tracked by Crunchbase are based in the San Francisco Bay Area, and the remaining two have some operations in the region.

Why is this? Partly, there’s a concentration of talent in the area, with key engineering staff coming from larger local companies like Uber, Tesla and Waymo . Plus, of course, there’s a ready supply of investor capital, which bot startups presumably will need as they scale.

Silicon Valley and San Francisco, known for scarce and astronomically expensive housing, are also geographies in which employers struggle to find people to deliver stuff at prevailing wages to the hordes of tech workers toiling at projects like designing robots to replace them.

That said, the region isn’t entirely friendly territory for slow-moving sidewalk robots. In San Francisco, already home to absurdly steep streets and sidewalks crowded with humans and discarded scooters, city legislators voted to ban delivery robots from most places and severely restrict them in areas where permitted.

The rise of the pizza delivery robot manager

But while San Francisco may be wary of a delivery robot invasion, other geographies, including nearby Berkeley, Calif., where startup Kiwi Campus operates, have been more welcoming.

In the process, they’re creating an interesting new set of robot overseer jobs that could shed some light on the future of last-mile delivery employment.

For some startups in early trial mode, robot wrangling jobs involve shadowing bots and making sure they carry out their assigned duties without travails.

Remote robot management is also a thing and will likely see the sharpest growth. Starship, for instance, relies on operators in Estonia to track and manage bots as they make their deliveries in faraway countries.

For now, it’s too early to tell whether monitoring and controlling hordes of delivery bots will provide better pay and working conditions than old-fashioned human delivery jobs.

At least, however, much of it could theoretically be done while lying on the sofa.

News Source = techcrunch.com

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