Voyage raises $31 million to bring driverless taxis to communities – gpgmail


Voyage, the autonomous vehicle startup that spun out of Udacity, announced Thursday it has raised $31 million in a round led by Franklin Templeton.

Khosla Ventures, Jaguar Land Rover’s InMotion Ventures and Chevron Technology Ventures also participated in the round. The company, which operates a ride-hailing service in retirement communities using self-driving cars supported by human safety drivers, has raised a total of $52 million since launching in 2017. The new funding includes a $3 million convertible note.

Voyage CEO Oliver Cameron has big plans for the fresh injection of capital, including hiring and expanding its fleet of self-driving Chrysler Pacifica minivans, which always have a human safety driver behind the wheel.

Ultimately, the expanded G2 fleet and staff are just the means toward Cameron’s grander mission to turn Voyage into a truly driverless and profitable ride-hailing company.

“It’s not just about solving self-driving technology,” Cameron told gpgmail in a recent interview, explaining that a cost-effective vehicle designed to be driverless is the essential piece required to make this a profitable business.

The company is in the midst of a hiring campaign that Cameron hopes will take its 55-person staff to more than 150 over the next year. Voyage has had some success attracting high-profile people to fill executive-level positions, including CTO Drew Gray, who previously worked at Uber ATG, Otto, Cruise and Tesla, as well as former NIO and Tesla employee Davide Bacchet as director of autonomy.

Funds will also be used to increase its fleet of second-generation self-driving cars (called G2) that are currently being used in a 4,000-resident retirement community in San Jose, Calif., as well as The Villages, a 40-square-mile, 125,000-resident retirement city in Florida. Voyage’s G2 fleet has 12 vehicles. Cameron didn’t provide details on how many vehicles it will add to its G2 fleet, only describing it as a “nice jump that will allow us to serve consumers.”

Voyage used the G2 vehicles to create a template of sorts for its eventual driverless vehicle. This driverless product — a term Cameron has used in a previous post on Medium — will initially be limited to 25 miles per hour, which is the driving speed within the two retirement communities in which Voyage currently tests and operates. The vehicle might operate at a low speed, but they are capable of handling complex traffic interactions, he wrote.

“It won’t be the most cost-effective vehicle ever made because the industry still is in its infancy, but it will be a huge, huge, huge improvement over our G2 vehicle in terms of being be able to scale out a commercial service and make money on each ride,” Cameron said. 

Voyage initially used modified Ford Fusion vehicles to test its autonomous vehicle technology, then introduced in July 2018 Chrysler Pacifica minivans, its second generation of autonomous vehicles. But the end goal has always been a driverless product.

gpgmail previously reported that the company has partnered with an automaker to provide this next-generation vehicle that has been designed specifically for autonomous driving. Cameron wouldn’t name the automaker. The vehicle will be electric and it won’t be a retrofit like the Chrysler Pacifica Hybrid vehicles Voyage currently uses or its first-generation vehicle, a Ford Fusion.

Most importantly, and a detail Cameron did share with gpgmail, is that the vehicle it uses for its driverless service will have redundancies and safety-critical applications built into it.

Voyage also has deals in place with Enterprise rental cars and Intact insurance company to help it scale.

“You can imagine leasing is much more optimal than purchasing and owning vehicles on your balance sheet,” Cameron said. “We have those deals in place that will allow us to not only get the vehicle costs down, but other aspects of the vehicle into the right place as well.”


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Didi Chuxing to launch self-driving rides in Shanghai and expand them beyond China by 2021 – gpgmail


Didi Chuxing will begin picking up ride-hailing passengers with self-driving cars in Shanghai in just a few months, according to company CTO Zhang Bo (via Reuters). The plan is to roll out autonomous pick-ups in Shanghai first, starting in one district of the city, and then expand the program from there – finally culminating in the deployment of self-driving vehicles outside of China by 2021.

Like Uber’s autonomous test vehicles, Didi’s cars will be staffed with a human driver on board during the initial launch period, which awaits a few remaining licenses before it can actually begin serving human passengers. Self-driving rides will be free for customers, and Zhang said that more than 30 different vehicles will be offered for self-driving trips as part of the pilot.

After its initial pilot launch in Shanghai, Didi will look to expand its offerings to Beijing and Shenzhen as well, with hopes to be live in all three cities by 2020.

Didi is the largest ride-hailing company in China, and beat out an attempt by Uber to establish a presence in the market, resulting in Uber selling its Chinese business to Didi and exiting the market in 2016 (in exchange for a minority stake). We spoke to Didi’s CTO (who asked to be identified by as ‘Bob’ at the time, hence the lower-third in the video below) later that same year about why the company believes it has an advantage when it comes to data-driven technology development relative to Uber and other ride-hailing companies.

Aside from a general sense in the industry that autonomy is a likely, if not inevitable end goal for ride-hailing and other mobility services with a technological focus, Didi is also likely motivated by a need for drivers to meet demand – and drivers who can provide a safe and secure experience for passengers. The company revealed in July that it had proved over 300,000 drivers that didn’t meet up to its safety standards after overhauling those standards last year.

Earlier this month, Didi also announced that it was spinning out its autonomous driving unit as a separate company, with Zhang as CEO. It’ll look to develop tech for its own fleet, and work in partnership with automakers, including Toyota, in pursuit of commercializing and deploying autonomous driving.


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Former Google X ecec Mo Gawdat wants to reinvent consumerism – gpgmail


Mo Gawdat, the former Google and Google X executive, is probably best known for his book Solve for Happy: Engineer Your Path to Joy. He left Google X last year. Quite a bit has been written about the events that led to him leaving Google, including the tragic death of his son. While happiness is still very much at the forefront of what he’s doing, he’s also now thinking about his next startup: T0day.

To talk about T0day, I sat down with the Egypt-born Gawdat at the Digital Frontrunners event in Copenhagen, where he gave one of the keynote presentations. Gawdat is currently based in London. He has adopted a minimalist lifestyle, with no more than a suitcase and a carry-on full of things. Unlike many of the Silicon Valley elite that have recently adopted a kind of performative aestheticism, Gawdat’s commitment to minimalism feels genuine — and it also informs his new startup.

“In my current business, I’m building a startup that is all about reinventing consumerism,” he told me. “The problem with retail and consumerism is it’s never been disrupted. E-commerce, even though we think is a massive revolution, it’s just an evolution and it’s still tiny as a fraction of all we buy. It was built for the Silicon Valley mentality of disruption, if you want, while actually, what you need is cooperation. There are so many successful players out there, so many efficient supply chains. We want the traditional retailers to be successful and continue to make money — even make more money.”

What T0day wants to be is a platform that integrates all of the players in the retail ecosystem. That kind of platform, Gawdat argues, never existed before, “because there was never a platform player.”

That sounds like an efficient marketplace for moving goods, but in Gawdat’s imagination, it is also a way to do good for the planet. Most of the fuel burned today isn’t for moving people, he argues, but goods. A lot of the food we buy goes to waste (together with all of the resources it took to grow and ship it) and single-use plastic remains a scourge.

How does T0day fix that? Gawdat argues that today’s e-commerce is nothing but a digital rendering of the same window shopping people have done for ages. “You have to reimagine what it’s like to consume,” he said.

The reimagined way to consume is essentially just-in-time shipping for food and other consumer goods, based on efficient supply chains that outsmart today’s hub and spoke distribution centers and can deliver anything to you in half an hour. If everything you need to cook a meal arrives 15 minutes before you want to start cooking, you only need to order the items you need at that given time and instead of a plastic container, it could come a paper bag. “If I have the right robotics and the right autonomous movements — not just self-driving cars, because self-driving cars are a bit far away — but the right autonomous movements within the enterprise space of the warehouse, I could literally give it to you with the predictability of five minutes within half an hour,” he explained. “If you get everything you need within half an hour, why would you need to buy seven apples? You would buy three.”

Some companies, including the likes of Uber, are obviously building some of the logistics networks that will enable this kind of immediate drop shipping, but Gawdat doesn’t think Uber is the right company for this. “This is going to sound a little spiritual. There is what you do and there is the intention behind why you do it,” he said. “You can do the exact same thing with a different intention and get a very different result.”

That’s an ambitious project, but Gawdat argues that it can be done without using massive amounts of resources. Indeed, he argues that one of the problems with Google X, and especially big moonshot projects like Loon and self-driving cars, was that they weren’t really resource-constrained. “Some things took longer than they should have,” he said. “But I don’t criticize what they did at all. Take the example of Loon and Facebook. Loon took longer than it should have. In my view, it was basically because of an abundance of resources and sometimes innovation requires a shoestring. That’s my only criticism.”

T0day, which Gawdat hasn’t really talked about publicly in the past, is currently self-funded. A lot of people are advising him to raise money for it. “We’re getting a lot of advice that we shouldn’t self-fund,” he said, but he also believes that the company will need some strategic powerhouses on its side, maybe retailers or companies that have already invested in other components of the overall platform.

T0day’s ambitions are massive, but Gawdat thinks that his team can get the basic elements right, be that the fulfillment center design or the routing algorithms and the optimization engines that power it all. He isn’t ready to talk about those, though. What he does think is that T0day won’t be the interface for these services. It’ll be the back end and allow others to build on top. And because his previous jobs have allowed him to live a comfortable life, he isn’t all that worried about margins either, and would actually be happy if others adopted his idea, thereby reducing waste.


10 minutes mail – Also known by names like : 10minemail, 10minutemail, 10mins email, mail 10 minutes, 10 minute e-mail, 10min mail, 10minute email or 10 minute temporary email. 10 minute email address is a disposable temporary email that self-destructed after a 10 minutes. https://tempemail.co/– is most advanced throwaway email service that helps you avoid spam and stay safe. Try tempemail and you can view content, post comments or download something

Former Google X exec Mo Gawdat wants to reinvent consumerism – gpgmail


Mo Gawdat, the former Google and Google X executive, is probably best known for his book Solve for Happy: Engineer Your Path to Joy. He left Google X last year. Quite a bit has been written about the events that led to him leaving Google, including the tragic death of his son. While happiness is still very much at the forefront of what he’s doing, he’s also now thinking about his next startup: T0day.

To talk about T0day, I sat down with the Egypt-born Gawdat at the Digital Frontrunners event in Copenhagen, where he gave one of the keynote presentations. Gawdat is currently based in London. He has adopted a minimalist lifestyle, with no more than a suitcase and a carry-on full of things. Unlike many of the Silicon Valley elite that have recently adopted a kind of performative aestheticism, Gawdat’s commitment to minimalism feels genuine — and it also informs his new startup.

“In my current business, I’m building a startup that is all about reinventing consumerism,” he told me. “The problem with retail and consumerism is it’s never been disrupted. E-commerce, even though we think is a massive revolution, it’s just an evolution and it’s still tiny as a fraction of all we buy. It was built for the Silicon Valley mentality of disruption, if you want, while actually, what you need is cooperation. There are so many successful players out there, so many efficient supply chains. We want the traditional retailers to be successful and continue to make money — even make more money.”

What T0day wants to be is a platform that integrates all of the players in the retail ecosystem. That kind of platform, Gawdat argues, never existed before, “because there was never a platform player.”

That sounds like an efficient marketplace for moving goods, but in Gawdat’s imagination, it is also a way to do good for the planet. Most of the fuel burned today isn’t for moving people, he argues, but goods. A lot of the food we buy goes to waste (together with all of the resources it took to grow and ship it) and single-use plastic remains a scourge.

How does T0day fix that? Gawdat argues that today’s e-commerce is nothing but a digital rendering of the same window shopping people have done for ages. “You have to reimagine what it’s like to consume,” he said.

The reimagined way to consume is essentially just-in-time shipping for food and other consumer goods, based on efficient supply chains that outsmart today’s hub and spoke distribution centers and can deliver anything to you in half an hour. If everything you need to cook a meal arrives 15 minutes before you want to start cooking, you only need to order the items you need at that given time and instead of a plastic container, it could come a paper bag. “If I have the right robotics and the right autonomous movements — not just self-driving cars, because self-driving cars are a bit far away — but the right autonomous movements within the enterprise space of the warehouse, I could literally give it to you with the predictability of five minutes within half an hour,” he explained. “If you get everything you need within half an hour, why would you need to buy seven apples? You would buy three.”

Some companies, including the likes of Uber, are obviously building some of the logistics networks that will enable this kind of immediate drop shipping, but Gawdat doesn’t think Uber is the right company for this. “This is going to sound a little spiritual. There is what you do and there is the intention behind why you do it,” he said. “You can do the exact same thing with a different intention and get a very different result.”

That’s an ambitious project, but Gawdat argues that it can be done without using massive amounts of resources. Indeed, he argues that one of the problems with Google X, and especially big moonshot projects like Loon and self-driving cars, was that they weren’t really resource-constrained. “Some things took longer than they should have,” he said. “But I don’t criticize what they did at all. Take the example of Loon and Facebook. Loon took longer than it should have. In my view, it was basically because of an abundance of resources and sometimes innovation requires a shoestring. That’s my only criticism.”

T0day, which Gawdat hasn’t really talked about publicly in the past, is currently self-funded. A lot of people are advising him to raise money for it. “We’re getting a lot of advice that we shouldn’t self-fund,” he said, but he also believes that the company will need some strategic powerhouses on its side, maybe retailers or companies that have already invested in other components of the overall platform.

T0day’s ambitions are massive, but Gawdat thinks that his team can get the basic elements right, be that the fulfillment center design or the routing algorithms and the optimization engines that power it all. He isn’t ready to talk about those, though. What he does think is that T0day won’t be the interface for these services. It’ll be the back end and allow others to build on top. And because his previous jobs have allowed him to live a comfortable life, he isn’t all that worried about margins either, and would actually be happy if others adopted his idea, thereby reducing waste.


10 minutes mail – Also known by names like : 10minemail, 10minutemail, 10mins email, mail 10 minutes, 10 minute e-mail, 10min mail, 10minute email or 10 minute temporary email. 10 minute email address is a disposable temporary email that self-destructed after a 10 minutes. https://tempemail.co/– is most advanced throwaway email service that helps you avoid spam and stay safe. Try tempemail and you can view content, post comments or download something

The risks of amoral A.I. – gpgmail


Artificial intelligence is now being used to make decisions about lives, livelihoods, and interactions in the real world in ways that pose real risks to people.

We were all skeptics once. Not that long ago, conventional wisdom held that machine intelligence showed great promise, but it was always just a few years away. Today there is absolute faith that the future has arrived.

It’s not that surprising with cars that (sometimes and under certain conditions) drive themselves and software that beats humans at games like chess and Go. You can’t blame people for being impressed.

But board games, even complicated ones, are a far cry from the messiness and uncertainty of real-life, and autonomous cars still aren’t actually sharing the road with us (at least not without some catastrophic failures).

AI is being used in a surprising number of applications, making judgments about job performance, hiring, loans, and criminal justice among many others. Most people are not aware of the potential risks in these judgments. They should be. There is a general feeling that technology is inherently neutral — even among many of those developing AI solutions. But AI developers make decisions and choose tradeoffs that affect outcomes. Developers are embedding ethical choices within the technology but without thinking about their decisions in those terms.

These tradeoffs are usually technical and subtle, and the downstream implications are not always obvious at the point the decisions are made.

The fatal Uber accident in Tempe, Arizona, is a (not-subtle) but good illustrative example that makes it easy to see how it happens.

The autonomous vehicle system actually detected the pedestrian in time to stop but the developers had tweaked the emergency braking system in favor of not braking too much, balancing a tradeoff between jerky driving and safety. The Uber developers opted for the more commercially viable choice. Eventually autonomous driving technology will improve to a point that allows for both safety and smooth driving, but will we put autonomous cars on the road before that happens? Profit interests are pushing hard to get them on the road immediately.

Physical risks pose an obvious danger, but there has been real harm from automated decision-making systems as well. AI does, in fact, have the potential to benefit the world. Ideally, we mitigate for the downsides in order to get the benefits with minimal harm.

A significant risk is that we advance the use of AI technology at the cost of reducing individual human rights. We’re already seeing that happen. One important example is that the right to appeal judicial decisions is weakened when AI tools are involved. In many other cases, individuals don’t even know that a choice not to hire, promote, or extend a loan to them was informed by a statistical algorithm. 

Buyer Beware

Buyers of the technology are at a disadvantage when they know so much less about it than the sellers do. For the most part decision makers are not equipped to evaluate intelligent systems. In economic terms, there is an information asymmetry that puts AI developers in a more powerful position over those who might use it. (Side note: the subjects of AI decisions generally have no power at all.) The nature of AI is that you simply trust (or not) the decisions it makes. You can’t ask technology why it decided something or if it considered other alternatives or suggest hypotheticals to explore variations on the question you asked. Given the current trust in technology, vendors’ promises about a cheaper and faster way to get the job done can be very enticing.

So far, we as a society have not had a way to assess the value of algorithms against the costs they impose on society. There has been very little public discussion even when government entities decide to adopt new AI solutions. Worse than that, information about the data used for training the system plus its weighting schemes, model selection, and other choices vendors make while developing the software are deemed trade secrets and therefore not available for discussion.

Image via Getty Images / sorbetto

The Yale Journal of Law and Technology published a paper by Robert Brauneis and Ellen P. Goodman where they describe their efforts to test the transparency around government adoption of data analytics tools for predictive algorithms. They filed forty-two open records requests to various public agencies about their use of decision-making support tools.

Their “specific goal was to assess whether open records processes would enable citizens to discover what policy judgments these algorithms embody and to evaluate their utility and fairness”. Nearly all of the agencies involved were either unwilling or unable to provide information that could lead to an understanding of how the algorithms worked to decide citizens’ fates. Government record-keeping was one of the biggest problems, but companies’ aggressive trade secret and confidentiality claims were also a significant factor.

Using data-driven risk assessment tools can be useful especially in cases identifying low-risk individuals who can benefit from reduced prison sentences. Reduced or waived sentences alleviate stresses on the prison system and benefit the individuals, their families, and communities as well. Despite the possible upsides, if these tools interfere with Constitutional rights to due process, they are not worth the risk.

All of us have the right to question the accuracy and relevance of information used in judicial proceedings and in many other situations as well. Unfortunately for the citizens of Wisconsin, the argument that a company’s profit interest outweighs a defendant’s right to due process was affirmed by that state’s supreme court in 2016.

Fairness is in the Eye of the Beholder

Of course, human judgment is biased too. Indeed, professional cultures have had to evolve to address it. Judges for example, strive to separate their prejudices from their judgments, and there are processes to challenge the fairness of judicial decisions.

In the United States, the 1968 Fair Housing Act was passed to ensure that real-estate professionals conduct their business without discriminating against clients. Technology companies do not have such a culture. Recent news has shown just the opposite. For individual AI developers, the focus is on getting the algorithms correct with high accuracy for whatever definition of accuracy they assume in their modeling.

I recently listened to a podcast where the conversation wondered whether talk about bias in AI wasn’t holding machines to a different standard than humans—seeming to suggest that machines were being put at a disadvantage in some imagined competition with humans.

As true technology believers, the host and guest eventually concluded that once AI researchers have solved the machine bias problem, we’ll have a new, even better standard for humans to live up to, and at that point the machines can teach humans how to avoid bias. The implication is that there is an objective answer out there, and while we humans have struggled to find it, the machines can show us the way. The truth is that in many cases there are contradictory notions about what it means to be fair.

A handful of research papers have come out in the past couple of years that tackle the question of fairness from a statistical and mathematical point-of-view. One of the papers, for example, formalizes some basic criteria to determine if a decision is fair.

In their formalization, in most situations, differing ideas about what it means to be fair are not just different but actually incompatible. A single objective solution that can be called fair simply doesn’t exist, making it impossible for statistically trained machines to answer these questions. Considered in this light, a conversation about machines giving human beings lessons in fairness sounds more like theater of the absurd than a purported thoughtful conversation about the issues involved.

Image courtesy of gpgmail/Bryce Durbin

When there are questions of bias, a discussion is necessary. What it means to be fair in contexts like criminal sentencing, granting loans, job and college opportunities, for example, have not been settled and unfortunately contain political elements. We’re being asked to join in an illusion that artificial intelligence can somehow de-politicize these issues. The fact is, the technology embodies a particular stance, but we don’t know what it is.

Technologists with their heads down focused on algorithms are determining important structural issues and making policy choices. This removes the collective conversation and cuts off input from other points-of-view. Sociologists, historians, political scientists, and above all stakeholders within the community would have a lot to contribute to the debate. Applying AI for these tricky problems paints a veneer of science that tries to dole out apolitical solutions to difficult questions. 

Who Will Watch the (AI) Watchers?

One major driver of the current trend to adopt AI solutions is that the negative externalities from the use of AI are not borne by the companies developing it. Typically, we address this situation with government regulation. Industrial pollution, for example, is restricted because it creates a future cost to society. We also use regulation to protect individuals in situations where they may come to harm.

Both of these potential negative consequences exist in our current uses of AI. For self-driving cars, there are already regulatory bodies involved, so we can expect a public dialog about when and in what ways AI driven vehicles can be used. What about the other uses of AI? Currently, except for some action by New York City, there is exactly zero regulation around the use of AI. The most basic assurances of algorithmic accountability are not guaranteed for either users of technology or the subjects of automated decision making.

GettyImages 823303786

Image via Getty Images / nadia_bormotova

Unfortunately, we can’t leave it to companies to police themselves. Facebook’s slogan, “Move fast and break things” has been retired, but the mindset and the culture persist throughout Silicon Valley. An attitude of doing what you think is best and apologizing later continues to dominate.

This has apparently been effective when building systems to upsell consumers or connect riders with drivers. It becomes completely unacceptable when you make decisions affecting people’s lives. Even if well-intentioned, the researchers and developers writing the code don’t have the training or, at the risk of offending some wonderful colleagues, the inclination to think about these issues.

I’ve seen firsthand too many researchers who demonstrate a surprising nonchalance about the human impact. I recently attended an innovation conference just outside of Silicon Valley. One of the presentations included a doctored video of a very famous person delivering a speech that never actually took place. The manipulation of the video was completely imperceptible.

When the researcher was asked about the implications of deceptive technology, she was dismissive of the question. Her answer was essentially, “I make the technology and then leave those questions to the social scientists to work out.” This is just one of the worst examples I’ve seen from many researchers who don’t have these issues on their radars. I suppose that requiring computer scientists to double major in moral philosophy isn’t practical, but the lack of concern is striking.

Recently we learned that Amazon abandoned an in-house technology that they had been testing to select the best resumes from among their applicants. Amazon discovered that the system they created developed a preference for male candidates, in effect, penalizing women who applied. In this case, Amazon was sufficiently motivated to ensure their own technology was working as effectively as possible, but will other companies be as vigilant?

As a matter of fact, Reuters reports that other companies are blithely moving ahead with AI for hiring. A third-party vendor selling such technology actually has no incentive to test that it’s not biased unless customers demand it, and as I mentioned, decision makers are mostly not in a position to have that conversation. Again, human bias plays a part in hiring too. But companies can and should deal with that.

With machine learning, they can’t be sure what discriminatory features the system might learn. Absent the market forces, unless companies are compelled to be transparent about the development and their use of opaque technology in domains where fairness matters, it’s not going to happen.

Accountability and transparency are paramount to safely using AI in real-world applications. Regulations could require access to basic information about the technology. Since no solution is completely accurate, the regulation should allow adopters to understand the effects of errors. Are errors relatively minor or major? Uber’s use of AI killed a pedestrian. How bad is the worst-case scenario in other applications? How are algorithms trained? What data was used for training and how was it assessed to determine its fitness for the intended purpose? Does it truly represent the people under consideration? Does it contain biases? Only by having access to this kind of information can stakeholders make informed decisions about appropriate risks and tradeoffs.

At this point, we might have to face the fact that our current uses of AI are getting ahead of its capabilities and that using it safely requires a lot more thought than it’s getting now.


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Watch a Waymo self-driving car test its sensors in a haboob – gpgmail


Waymo, the self-driving car company under Alphabet, has been testing in the suburbs of Phoenix for several years now. And while the sunny metropolis might seem like the ideal and easiest location to test autonomous vehicle technology, there are times when the desert becomes a dangerous place for any driver — human or computer.

The two big safety concerns in this desert region are sudden downpours that cause flash floods and haboobs, giant walls of dust between 1,500 and 3,000 feet high that can cover up to 100 square miles. One record-breaking haboob in July 2011 covered the entire Phoenix valley, an area of more than 517 square miles.

Waymo released Friday a blog post that included two videos showing how the sensors on its self-driving vehicles detect and recognize objects while navigating through a haboob in Phoenix and fog in San Francisco. The vehicle in Phoenix was manually driven, while the one in the fog video was in autonomous mode.

The point of the videos, Waymo says, is to show how, and if, the vehicles recognize objects during these extreme low visibility moments. And they do. The haboob video shows how its sensors work to identify a pedestrian crossing a street with little to no visibility.

Waymo uses a combination of lidar, radar and cameras to detect and identify objects. Fog, rain or dust can limit visibility in all or some of these sensors.

Waymo doesn’t silo the sensors affected by a particular weather event. Instead, it continues to take in data from all the sensors, even those that don’t function as well in fog or dust, and uses that collective information to better identify objects.

The potential is for autonomous vehicles to improve on visibility, one of the greatest performance limitations of humans, Debbie Hersman, Waymo’s chief safety officer wrote in the blog post. If Waymo or other AV companies are successful, they could help reduce one of the leading contributors to crashes. The Department of Transportation estimates that weather contributes to 21% of the annual U.S. crashes.

Still, there are times when even an autonomous vehicle doesn’t belong on the road. It’s critical for any company planning to deploy AVs to have a system that can not only identify, but also take the safest action if conditions worsen.

Waymo vehicles are designed to automatically detect sudden extreme weather changes, such as a snowstorm, that could impact the ability of a human or an AV to drive safely, according to Hersman.

The question is what happens next. Humans are supposed to pull over off the road during a haboob and turn off the vehicle, a similar action when one encounters heavy fog.  Waymo’s self-driving vehicles will do the same if weather conditions deteriorate to the point that the company believes it would affect the safe operation of its cars, Hersman wrote.

The videos and blog post are the latest effort by Waymo to showcase how and where it’s testing. The company announced August 20 that it has started testing how its sensors handle heavy rain in Florida. The move to Florida will focus on data collection and testing sensors; the vehicles will be manually driven for now.

Waymo also tests (or has tested) its technology in and around Mountain View, Calif., Novi, Mich., Kirkland, Wash. and San Francisco. The bulk of the company’s activities have been in suburbs of Phoenix  and around Mountain View.


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Porsche invests in ‘low visibility’ sensor startup TriEye – gpgmail


Porsche’s venture arm has acquired a minority stake in TriEye, an Israeli startup that’s working on a sensor technology to help vehicle driver-assistance and self-driving systems see better in poor weather conditions like dust, fog and rain.

The strategic investment is part of a Series A financing round that has been expanded to $19 million. The round was initially led by Intel Capital and Israeli venture fund Grove Ventures. Porsche has held shares in Grove Ventures since 2017.

TriEye has raised $22 million to date. Terms of Porsche’s investment were not disclosed.

The additional funding will be used for ongoing product development, operations and hiring talent, according to TriEye.

The advanced driver-assistance systems found in most new vehicles today typically rely on a combination of cameras and radar to “see.” Autonomous vehicle systems, which are being developed and tested by dozens of companies such as Argo AI, Aptiv, Aurora, Cruise and Waymo, have a more robust suite of sensors that include light detection and ranging radar (lidar) along with cameras and ultrasonic sensors.

For either of these systems to function properly, they need to be able to see in all conditions. This pursuit of sensor technology has sparked a boom in startups hoping to tap into demand from automakers and companies working on self-driving car systems.

TriEye is one of them. The premise of TriEye is to solve the low visibility problem created by poor weather conditions. The startup’s co-founders argue that fusing existing sensors such as radar, lidar and standard cameras don’t solve this problem.

TriEye, which was founded in 2017, believes the answer is through short-wave infrared (SWIR) sensors. The startup said it has developed an HD SWIR camera that is a smaller size, higher resolution and cheaper than other technologies. The camera is due to launch in 2020.

The technology is based on advanced nano-photonics research by Uriel Levy, a TriEye co-founder and CTO who is also a professor at the Hebrew University of Jerusalem.

The company says its secret sauce is its “unique” semiconductor design that will make it possible to manufacture SWIR HD cameras at a “fraction of their current cost.”

TriEye’s technology was apparently good enough to get Porsche’s attention.

Michael Steiner, a Porsche AG board member focused on R&D, said the technology was promising, as was the team, which is comprised of people with expertise in deep learning, nano-photonics and semiconductor components.

“We see great potential in this sensor technology that paves the way for the next generation of driver assistance systems and autonomous driving functions,” Steiner said in a statement. “SWIR can be a key element: it offers enhanced safety at a competitive price.”


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Waymo self-driving cars head to Florida for rainy season – gpgmail


Waymo is taking some of its autonomous vehicles to Florida just in time for hurricane season to begin testing in heavy rain.

The move to Florida will focus on testing how its myriad of sensors hold up during the region’s rainy season as well as to collect data. All of the vehicles will be manually driven by trained drivers.

Waymo will bring both of its autonomous vehicles, the Chrysler Pacificas and a Jaguar I-Pace, to Naples and Miami, Florida for testing, according to a blog posted Tuesday. Miami is one of the wettest cities in the U.S., averaging 61.9 inches of rain annually.

The self-driving car company, which is a business under Alphabet, began testing its autonomous vehicles in and around Mountain View, Calif., before branching out to other cities and weather, including Novi, Michigan, Kirkland, Washington and San Francisco. But the bulk of the company’s activities have been in suburbs of Phoenix and around Mountain View — two places with lots of sun, and even blowing dust, in the case of Phoenix.

Waymo opened a technical center Chandler, Ariz. and started testing there in 2016. Since then the company has ramped up its testing and launched an early rider program in April 2017 as a step toward commercial deployment.

The company will spend the next several weeks driving on a closed course in Naples to test its sensor suite , which includes lidar, cameras and radar . Later in the month, Waymo plans to bring its vehicles to public roads in Miami. A few Waymo vehicles will be collecting data on highways between Orlando, Tampa, Fort Myers and Miami.

Waymo is hardly the only autonomous vehicle company to take advantage of Florida’s AV friendly regulations. Ford and Argo AI, the self-driving company it backs, have had a presence in the Miami since early 2018. Argo AI began collecting data and mapping and has since expanded to testing in autonomous mode last summer.

Last year, Ford partnered with Walmart and Postmates to test the business of delivering goods like groceries and pet food using self-driving vehicles. The pilot project is focused on Miami-Dade County.

Self-driving trucks startup Starsky Robotics also is testing in Florida.


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UPS takes minority stake in self-driving truck startup TuSimple – gpgmail


UPS said Thursday it has taken a minority stake in self-driving truck startup, TuSimple, just months after the two companies began testing the use of autonomous trucks in Arizona.

The size of minority investment, which was made by the company’s venture arm UPS Ventures, was not disclosed. The investment and the testing comes as UPS looks for new ways to remain competitive, cut costs and boost its bottom line.

TuSimple, which launched in 2015 and has operations in San Diego and Tucson, Arizona, believes it can deliver. The startup says it can cut average purchased transportation costs by 30%.

TuSimple, which is backed by Nvidia, ZP Capital and Sina Corp., is working on a “full-stack solution,” a wonky industry term that means developing and bringing together all of the technological pieces required for autonomous driving. TuSimple is developing a Level 4 system, a designation by the SAE that means the vehicle takes over all of the driving in certain conditions.

An important piece of TuSimple’s approach is its camera-centric perception solution. The system has a vision range of 1,000 meters, the company says.

The days of when highways will be filled with autonomous trucks are years away. But UPS believes it’s worth jumping in at an early stage to take advantage of some of the automated driving such as advanced braking technology that TuSimple can offer today.

“UPS is committed to developing and deploying technologies that enable us to operate our global logistics network more efficiently,” Scott Price, chief strategy officer at UPS said in a statement. “While fully autonomous, driverless vehicles still have development and regulatory work ahead, we are excited by the advances in braking and other technologies that companies like TuSimple are mastering. All of these technologies offer significant safety and other benefits that will be realized long before the full vision of autonomous vehicles is brought to fruition — and UPS will be there, as a leader implementing these new technologies in our fleet.”

UPS initially tapped TuSimple to help it better understand how Level 4 autonomous trucking might function within its network. That relationship expanded in May when the companies began using self-driving tractor trailers to carry freight on a freight route between Tucson and Phoenix to test if service and efficiency in the UPS network can be improved. This testing is ongoing. All of TuSimple’s self-driving trucks operating in the U.S. have a safety driver and an engineer in the cab.

TuSimple and UPS monitor all aspects of these trips, including safety data, transport time and the distance and time the trucks travel autonomously, the companies said Thursday.

UPS isn’t the only company that TuSimple is hauling freight for as part of its testing. TuSimple has said its hauling loads for for several customers in Arizona.  The startup has a post-money valuation of $1.095 billion (aka unicorn status).


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Inside Voyage’s plan to deliver a driverless future – gpgmail


In two years, Voyage has gone from a tiny self-driving car upstart spun out of Udacity to a company able to operate on 200 miles of roads in retirement communities.

Now, Voyage is on the verge of introducing a new vehicle that is critical to its mission of launching a truly driverless ride-hailing service. (Human safety drivers not included.)

This internal milestone, which Voyage CEO Oliver Cameron hinted at in a recent Medium post, went largely unnoticed. Voyage, after all, is just a 55-person speck of a startup in an industry, where the leading companies have amassed hundreds of engineers backed by war chests of $1 billion or more. Voyage has raised just $23.6 million from investors that include Khosla Ventures, CRV, Initialized Capital and the venture arm of Jaguar Land-Rover.

Still, the die has yet to be cast in this burgeoning industry of autonomous vehicle technology. These are the middle-school years for autonomous vehicles — a time when size can be misinterpreted for maturity and change occurs in unpredictable bursts.

The upshot? It’s still unclear which companies will solve the technical and business puzzles of autonomous vehicles. There will be companies that successfully launch robotaxis and still fail to turn their service into a profitable commercial enterprise. And there will be operationally savvy companies that fail to develop and validate the technology to a point where human drivers can be removed.

Voyage wants to unlock both.

Crowded field


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