“Coming off the heels of our recent financing, we’ve re-structured to accelerate expansion of Pace, with a heavier focus on building in-market teams to greatly expand bike fleets, drive higher ridership, and partner with local businesses to sponsor Pace parking,” Zagster CEO Tim Ericson said in a statement to TechCrunch. “We did have to let a number of people go with roles not aligned to our Pace strategy. We’re extremely appreciative of their contributions and are helping them through the transition.”
The shift from docked to dockless bike-sharing is what prompted the layoffs, Ericson said. Within the U.S. dockless market, Ericson said he sees two models emerging: free-floating and lock-to.
“We believe the U.S. market will move to lock-to, with cities regulating and enforcing lock-to parking within three years,” Ericson said. “Zagster has a strong lead in lock-to, with exclusive rights to operate in more than 100 cities and colleges.”
Zagster’s Pace is one of the newer entrants to the bike-share space, which consists of a number of startups and larger companies battling for contracts with cities all over the world. Pace, which launched just a few months ago in December, currently operates in Tallahassee, Florida and Knoxville, Tennessee. With the funding, Zagster plans to launch Pace in additional cities this year.
Zagster also operates a bike-share solution for municipalities looking to offer their own city-specific services. Zagster, which launched in 2007, operates more than 200 bike-shares across 35 states in the U.S.
Zagster’s plan, Ericson said, is to convert its bike-shares to the Pace brand and model, with the ultimate goal of creating a nationwide dockless system across 35 states by the end of 2019. Over the next three months, Zagster plans to quadruple the Pace footprint by launching in six new cities.
This year has been full of bike-share news, between JUMP scoring an exclusive contract to operate its stationless bike-share service in San Francisco to both LimeBike and Spin unveiling their own take on e-bikes.
WASHINGTON — A potential breakthough to an impasse over automobiles has created a new sense of optimism in the NAFTA negotiations, with different players declaring themselves more hopeful of a deal than they have been in some time.
Canada’s ambassador to the U.S., David MacNaughton, suggested his newfound optimism was based on two developments in recent days: progress on the top U.S. priority of auto-parts rules, as well as a more general thawing of the frosty tone in earlier talks.
This comes as the United States appears increasingly keen on securing a quick agreement, with an upcoming round in Washington expected to feature a final push to obtain a deal before election campaigns in Mexico and in the U.S. Congress punt the process into 2019.
MacNaughton said the most recent American proposals could help the U.S. achieve its goal of safeguarding auto production there, potentially without a strict American-made content requirement in every car, an idea that has been a source of friction with Canada and Mexico.
He cautioned that the autos impasse isn’t completely sorted out yet.
“They came back with some ideas that if you take them to their logical conclusion would mean that you wouldn’t need that (American content) requirement,” MacNaughton told reporters after speaking at a Washington gathering of the American Association of Port Authorities.
“They put some interesting ideas on the table … which were actually quite creative. To which we sort of said, ‘Yeah, we can work with that.’… Did we get to somewhere where you could shake hands and say, ‘We’ve got a deal?’ Absolutely not… Whether or not we can get there I don’t know. But I took it as being a positive thing that they had another way of getting at that issue.”
His assessment came after a discreet high-level meeting.
Foreign Affairs Minister Chrystia Freeland did not make any public appearances last week when she was in Washington to meet U.S. trade czar Robert Lighthizer, who has said he is hoping for an agreement in principle within weeks.
While cautioning that the talks can’t be bound by artificial deadlines, MacNaughton said there really is a good-faith effort to get as close to a deal as possible early next month: “We will meet seven days a week, 24 hours a day to make as much progress as we can.”
There are rumours of a lengthy, two-week round planned in Washington starting in early April. In the runup, MacNaughton said the countries have not only been meeting in person, but also in phone discussions.
“I must say that in the last two weeks the talks that we’ve had … have been more positive than I’ve seen them before,” MacNaughton told Tuesday’s conference.
“We still have a long way to go. But certainly the environment is one which is conducive to making a lot more progress in the next short while… I’m optimistic. I am confident that we are going to move forward. … Certainly the environment is conducive to making a lot more progress in the next short while.”
He cited two reasons for optimism. In addition to the autos progress, he lauded the attitude around the table recently: “I was encouraged as much by the tone as by the substance.”
One autos stakeholder said he’s newly hopeful, too.
“We’re optimistic. We’re hopeful about the timeline,” said Flavio Volpe of Canada’s Automotive Parts Manufacturers’ Association, whose group was recently invited by the U.S. to offer ideas for breaking through the impasse.
“Certainly I haven’t said that before.”
The White House on Tuesday said little in response to a public observation from Prime Minister Justin Trudeau that U.S. President Donald Trump seems to be more enthusiastic about completing a deal.
“The president is always enthusiastic about making a good deal, but that would be the key caveat to any conversation, is making sure that whatever deal he makes is good for Americans, and American workers,” said his spokeswomen, Sarah Sanders.
MacNaughton also shared an anecdote about the Trudeau-Trump relationship.
He described the prime minister attending a meeting of U.S. state governors last summer in Rhode Island, where he also met Mike Pence and he said the vice-president told Trudeau: “‘You know, the president really does like you.”‘
MacNaughton joked that the relationship is already good, with common collaboration all over the world, on issues ranging from North Korea to Venezuela, but added: “If they really want to make it an even better relationship we’ll agree on NAFTA.”
The Americans will get to tell their side of the story about the state of the talks. Lighthizer has two days of hearings scheduled before the U.S. Congress starting Wednesday, where he is sure to be asked about the NAFTA negotiations.
Automaker Toyota has temporarily ceases its public road testing of its fully autonomous ‘Chauffeur’ system in the U.S. after an accident earlier this week saw an Uber self-driving test vehicle strike a pedestrian, which ultimately resulted in her death.
Police have stated that initial findings suggest the accident would’ve been extremely difficult to avoid regardless of whether a human or an AV system was in control at the time, because of quickly the victim crossed in front of the moving vehicle (outside of a crosswalk), but Toyota has indicated to Bloomberg that it’s hitting the brakes for now due to the potential “emotional effect on [its] test drivers.”
Toyota spokesperson Brian Lyons noted that the automaker couldn’t speculate on the cause of the cash or its implications for the future of the self-driving industry, which is a fairly standard line I’ve heard across automakers and other involved in the industry thus far, and which suggests a fair reluctance to make any lasting material decisions before all information is available regarding the Uber incident.
Toyota has been working on both its ‘Chauffeur’ fully automated driving system, as well as ‘Guardian,’ an advanced-driver assist system that is designed to institute fail-safes for intervening to prevent accidents when a human driver’s behavior puts themselves or others in danger.
Apple seems to be ramping up its autonomous vehicle efforts, nearing doubling the number of vehicles in its fleet since January.
The company now has 45 autonomous vehicles in California registered with the DMV, according to the Financial Times. This makes Apple’s AV fleet the second largest in the state of California, outsized only by General Motors.
In April 2017, Apple received its first permit to test three autonomous vehicles. By January of this year, the company was testing 27 autonomous vehicles, and in just two months the company has nearly doubled its efforts, with plans to start testing vehicles in Arizona.
That said, regulatory hurdles may be rising. On Sunday night, one of Uber’s autonomous test vehicles was involved in an accident, fatally colliding with a pedestrian in Tempe Arizona.
This is the first time an AV accident has resulted in a human death, and Uber has suspended testing of its fleet in all the cities where it operates.
In the wake of this incident, regulators may take a more measured approach to deployment.
Japan wants to formally encourage domestic startups to pursue the growing opportunities in commercial space. The Japanese government has earmarked around $1 billion in public funds to startups working on space solutions. At the same time, the government revealed that it’s working on a legal path towards establishing commercial development on the moon.
This is a big injection in the private space race, and the funds will be allocated both as investments and as loans, spanning five years and beginning during this fiscal year. The goal is to boost the Japanese space business, hoping to help amplify its growth and propel it to a 2.4 trillion yen ($22.5 billion) market by the beginning of the 2030s, per the Nikkei.
Startups that qualify will be able to receive aid of up to 10 million yen per company, or around $100,000 U.S., for things like research costs and the price of applying for patents.
Japanese space startup ispace applauded the move in a statement provided to TechCrunch.
“Not only will this move improve the competitiveness of the Japanese private space sector, but it will have positive implications for the sector globally. We believe this will be remembered as a turning point for our burgeoning industry,” wrote ispace founder and CEO Takeshi Hakamada. “Further, investments in the space industry ultimately benefit society on Earth through the vast number of innovations that develop as a result.”
Google -owned Waze is growing the footprint of its Waze Carpool product with a market expansion today to cover all of Washington state. That means Carpool is now available in California, Texas, Isreal and Washington following today’s launch. Waze also recently revamped its Carpool experience, which now includes new options for choosing what rides to tag along with, driver gender filters and other convenience tools that are designed to make the experience feel safer and more comfortable overall.
Waze says this expansion shows that it’s increasing its commitment to the Carpool side of its project offering, since it’ll now reach an additional 375,000 commuters in Seattle alone. The company also has the support of local government in Seattle, with officials expressing their support for new solutions hoping to mitigate traffic in a press release announcing the launch.
The Waze Carpool app is different from other offerings including Uber Pool, mainly because it doesn’t pay out all that much to drivers. In fact, the maximum earning a driver can make on any ride is $15 and that’s for longer commutes, as the fee structure is designed to help the driver pay for gas but not use it as a source of income. Waze’s aim is to link up multiple people commuting to work going in the same direction via its platform, per the company. This also means lower costs for riders.
Anyone interested in driving Waze Carpool in Seattle can pick up the standard Waze app now to gain access, and on the rider side you can download the dedicated Waze Rider app on either iOS or Android to get started.
The chief of police in Tempe, Arizona, where an Uber self-driving car just hit and killed a pedestrian, has told the San Francisco Chronicle that “I suspect preliminarily it appears that the Uber would likely not be at fault in this accident.”
Chief Sylvia Moir explained after viewing the car’s own video of the event that “she came from the shadows right into the roadway,” and that “it would have been difficult to avoid this collision in any kind of mode.”
A lighted crosswalk was nearby but the place where the accident occurred was in the dark. The car would almost certainly have been aware of the pedestrian, but it’s also possible that she moved out in front of the car faster than the car could reasonably be stopped.
The details are known only to Uber and the authorities at present and it wouldn’t be right to speculate too far, but Moir certainly seems to suggest that the latter scenario is a possibility.
A self-driving vehicle made by Uber has struck and killed a pedestrian. It’s the first such incident and will certainly be scrutinized like no other autonomous vehicle interaction in the past. But on the face of it it’s hard to understand how, short of a total system failure, this could happen when the entire car has essentially been designed around preventing exactly this situation from occurring.
Something unexpectedly entering the vehicle’s path is pretty much the first emergency event that autonomous car engineers look at. The situation could be many things — a stopped car, a deer, a pedestrian — and the systems are one and all designed to detect them as early as possible, identify them, and take appropriate action. That could be slowing, stopping, swerving, anything.
Uber’s vehicles are equipped with several different imaging systems which work both ordinary duty (monitoring nearby cars, signs, and lane markings) and extraordinary duty like that just described. No less than four different ones should have picked up the victim in this case.
Top-mounted lidar. The bucket-shaped item on top of these cars is a lidar, or light detection and ranging, system that produces a 3D image of the car’s surroundings multiple times per second. Using infrared laser pulses that bounce off objects and return to the sensor, lidar can detect static and moving objects in considerable detail, day or night.
This is an example of a lidar-created imagery, though not specifically what the Uber vehicle would have seen.
Heavy snow and fog can obscure a lidar’s lasers, and its accuracy decreases with range, but for anything from a few feet to a few hundred feet, it’s an invaluable imaging tool and one that is found on practically every self-driving car.
The lidar unit, if operating correctly, should have been able to make out the person in question, if they were not totally obscured, while they were still more than a hundred feet away, and passed on their presence to the “brain” that collates the imagery.
Front-mounted radar. Radar, like lidar, sends out a signal and waits for it to bounce back, but it uses radio waves instead of light. This makes it more resistant to interference, since radio can pass through snow and fog, but also lowers its resolution and changes its range profile.
Tesla’s Autopilot relies mostly on radar.
Depending on the radar unit Uber employed — likely multiple in both front and back to provide 360 degrees of coverage — the range could differ considerably. If it’s meant to complement the lidar, chances are it overlaps considerably, but is built more to identify other cars and larger obstacles.
The radar signature of a person is not nearly so recognizable, but it’s very likely they would have at least shown up, confirming what the lidar detected.
Short and long-range optical cameras. Lidar and radar are great for locating shapes, but they’re no good for reading signs, figuring out what color something is, and so on. That’s a job for visible-light cameras with sophisticated computer vision algorithms running in real time on their imagery.
The cameras on the Uber vehicle watch for telltale patterns that indicate braking vehicles (sudden red lights), traffic lights, crossing pedestrians, and so on. Especially on the front end of the car, multiple angles and types of camera would be used, so as to get a complete picture of the scene into which the car is driving.
Detecting people is one of the most commonly attempted computer vision problems, and the algorithms that do it have gotten quite good. “Segmenting” an image, as it’s often called, generally also involves identifying things like signs, trees, sidewalks and more.
That said, it can be hard at night. But that’s an obvious problem, the answer to which is the previous two systems, which work night and day. Even in pitch darkness, a person wearing all black would show up on lidar and radar, warning the car that it should perhaps slow and be ready to see that person in the headlights. That’s probably why a night-vision system isn’t commonly found in self-driving vehicles (I can’t be sure there isn’t one on the Uber car, but it seems unlikely).
Safety driver. It may sound cynical to refer to a person as a system, but the safety drivers in these cars are very much acting in the capacity of an all-purpose failsafe. People are very good at detecting things, even though we don’t have lasers coming out of our eyes. And our reaction times aren’t the best, but if it’s clear that the car isn’t going to respond, or has responded wrongly, a trained safety driver will react correctly.
Worth mentioning is that there is also a central computing unit that takes the input from these sources and creates its own more complete representation of the world around the car. A person may disappear behind a car in front of the system’s sensors, for instance, and no longer be visible for a second or two, but that doesn’t mean they ceased existing. This goes beyond simple object recognition and begins to bring in broader concepts of intelligence such as object permanence, predicting actions, and the like.
It’s also arguably the most advance and closely guarded part of any self-driving car system and so is kept well under wraps.
It isn’t clear what the circumstances were under which this tragedy played out, but the car was certainly equipped with technology that was intended to, and should have, detected the person and caused the car to react appropriately. Furthermore, if one system didn’t work, another should have sufficed — multiple failbacks are only practical in high stakes matters like driving on public roads.
We’ll know more as Uber, local law enforcement, federal authorities, and others investigate the accident.