Tesla CEO Elon Musk introduces the Tesla Semi truck and an updated version of the Tesla Roadster at a 2017 unveiling. (Tesla via YouTube) Tesla today reported wider-than-expected financial losses in the first quarter — due to what the company said were delivery challenges, a seasonal dip in demand and the unanticipated effects of pricing decisions. Despite the downturn from what had been a profitable couple of quarters, Tesla CEO Elon Musk was bullish on several fronts, including rollouts for the company’s and , plus the opening of Tesla’s Gigafactory in Shanghai, China. Musk is even planning to offer car insurance policies starting next month, with pricing determined by the data that’s received from the company’s cars. “We have direct knowledge of the risk profile of customers and the car,” he explained during today’s teleconference with financial analysts. “If they want to buy Tesla insurance, they have to agree to not drive the car in a crazy way. Or they can, but then the insurance rate is higher.” If it’s done right, in-house insurance could add another revenue stream to Tesla’s bottom line. That could help ease the pain for Tesla’s accountants as well as for investors, who have seen share prices slump due to concerns about long-term profitability. (The price slipped nearly 2 percent during today’s trading, to $258.66 at the close.) Net losses amounted to $702 million, and adjusted net losses per share were $2.90. That’s 13 percent worse than the year-ago figure and . Revenue was $4.5 billion, which was better than the year-ago figure but not as high as analysts thought it would be. In its , Tesla said there was a production jam-up that forced a large number of deliveries to be deferred into the second quarter. “This is the most difficult logistics problem I’ve ever seen, and I’ve seen some tough ones,” Musk said. In all, 63,000 electric cars were delivered during the first quarter, which fell far short of expectations. In addition to the logistical challenges, Tesla said pricing changes for its Model S and Model X cars caused a higher-than-anticipated return rate. One disincentive to sales was the gradual phase-out of federal tax credits for electric vehicles. Previously: The good news is that powertrain improvements have boosted the performance and range of those two models: The maximum range was extended to 370 miles on a full charge for the Model S, and 325 miles for the Model X SUV. For the past two years, Tesla has been focused on ramping up production of the Model 3, which finally . Tesla reported producing 63,000 Model 3 cars during the quarter and is aiming to raise that figure higher. “If our Gigafactory Shanghai is able to reach volume production early in Q4 this year, we may be able to produce as many as 500,000 vehicles globally in 2019,” the company said in its shareholders’ letter. “This is an aggressive schedule, but it is what we are targeting.” Musk said that the Shanghai construction project was “going incredibly well,” and that he was receiving “midnight Gigafactory email” on an almost nightly basis. On other financial fronts: Tesla’s cash on hand fell by $1.5 billion over the course of the quarter, to $2.2 billion. A $920 million convertible bond repayment accounted for most of that reduction, and the delivery snag was an additional factor. Another, linked to Tesla’s SolarCity subsidiary, is said to be due this month. One analyst asked Musk whether he wished that he had persevered with efforts to take Tesla private last year — efforts that ended up getting him in hot water with the Securities and Exchange Commission. “I would prefer that we were private,” Musk replied, “but unfortunately that ship has sailed.” Musk told analysts that “at this point I do think there is some merit to raising capital,” but he didn’t provide further details.
(Xevo Photo) Global automotive giant today that it will acquire Seattle-area connected car startup for $320 million. Xevo, originally founded in 2000 as UIEvolution, develops connected-car software with more than 25 million vehicles on the road today using its proprietary technology. It sells two products, Xevo Journeyware and Xevo Market, that allow drivers to interact with in-car content and connects them with popular food, fuel, parking, hotel, and retail brands via touchscreen and mobile apps. The company, which a partnership with Domino’s Pizza last month, employs 300 across offices in Bellevue, Wash., and Tokyo. The deal is expected to close in the second quarter. “Automakers have embraced the potential of Xevo’s e-commerce platform, as well as the deeply customizable driver experiences made possible by Xevo’s artificial intelligence technology,” Xevo CEO Dan Gittleman said in a statement. “Today, with Lear’s reach, we can scale Xevo’s innovative technology and business model to a global customer base.” Lear, based in Southfield, Mich., specializes in automotive seating and electrical systems and employs nearly 170,000 people across 39 countries. It $21.1 billion in sales last year, up from $20.5 billion in 2017. The company’s stock reached record-highs in mid-2018 but has dropped 30 percent since then, trading at $141 per share on Tuesday. “The acquisition of Xevo broadens Lear’s connectivity portfolio, bringing together Xevo’s leading e-commerce vehicle platform technology with Lear’s expertise in electronic systems,” said John Absmeier, Lear’s Chief Technology Officer. “Xevo’s user interface establishes a connected marketplace for consumers in their vehicles, unlocking previously unrealized value from vehicle data and opening up new revenue streams.” Xevo was founded in 2000 by , who is known as the architect behind Microsoft products like Windows 95 and Internet Explorer 3. It in 2016 after the company acquired Seattle-based machine learning startup Surround.io Corp.
While creating self-driving car systems, it’s natural that different companies might independently arrive at similar methods or results — but the similarities in a recent “first of its kind” Nvidia proposal to work done by two years ago were just too much for the latter company’s CEO to take politely. Amnon Shashua, , openly mocks pointing out innumerable similarities to Mobileye’s “Responsibility Sensitive Safety” paper from 2017. He writes: It is clear Nvidia’s leaders have continued their pattern of imitation as their so-called “first-of-its-kind” safety concept is a close replica of the RSS model we published nearly two years ago. In our opinion, SFF is simply an inferior version of RSS dressed in green and black. To the extent there is any innovation there, it appears to be primarily of the linguistic variety. Now, it’s worth considering the idea that the approach both seem to take is, like many in the automotive and autonomous fields and others, simply inevitable. Car makers don’t go around accusing each other of using the similar setup of four wheels and two pedals. It’s partly for this reason, and partly because the safety model works better the more cars follow it, that when Mobileye published its RSS paper, it did so publicly and invited the industry to collaborate. Many did, and as Shashua points out, including Nvidia, at least for a short time in 2018, after which Nvidia pulled out of collaboration talks. To do so and then, a year afterwards, propose a system that is, if not identical, then at least remarkably similar, and without crediting or mentioning Mobileye is suspicious to say the least. The (highly simplified) foundation of both is calculating a set of standard actions corresponding to laws and human behavior that plan safe maneuvers based on the car’s own physical parameters and those of nearby objects and actors. But the similarities extend beyond these basics, Shashua writes (emphasis his): RSS defines a safe longitudinal and a safe lateral distance around the vehicle. When those safe distances are compromised, we say that the vehicle is in a Dangerous Situation and must perform a Proper Response. The specific moment when the vehicle must perform the Proper Response is called the Danger Threshold. SFF defines identical concepts with slightly modified terminology. Safe longitudinal distance is instead called “the SFF in One Dimension;” safe lateral distance is described as “the SFF in Higher Dimensions.” Instead of Proper Response, SFF uses “Safety Procedure.” Instead of Dangerous Situation, SFF replaces it with “Unsafe Situation.” And, just to be complete, SFF also recognizes the existence of a Danger Threshold, instead calling it a “Critical Moment.” This is followed by numerous other close parallels, and just when you think it’s done, he includes showing dozens of other cases where Nvidia seems (it’s hard to tell in some cases if you’re not closely familiar with the subject matter) to have followed Mobileye and RSS’s example over and over again. Theoretical work like this isn’t really patentable, and patenting wouldn’t be wise anyway, since widespread adoption of the basic ideas is the most desirable outcome (as both papers emphasize). But it’s common for one R&D group to push in one direction and have others refine or create counter-approaches. You see it in computer vision, where for example Google boffins may publish their early and interesting work, which is picked up by FAIR or Uber and improved or added to in another paper 8 months later. So it really would have been fine for Nvidia to publicly say “Mobileye proposed some stuff, that’s great but here’s our superior approach.” Instead there is no mention of RSS at all, which is strange considering their similarity, and the only citation in the SFF whitepaper is “The Safety Force Field, Nvidia, 2017,” in which, we are informed on the very first line, “the precise math is detailed.” Just one problem: This paper doesn’t seem to exist anywhere. It certainly was never published publicly in any journal or blog post by the company. It has no DOI number and doesn’t show up in any searches or article archives. This appears to be the first time anyone has ever cited it. It’s not required for rival companies to be civil with each other all the time, but in the research world this will almost certainly be considered poor form by Nvidia, and that can have knock-on effects when it comes to recruiting and overall credibility. I’ve contacted Nvidia for comment (and to ask for a copy of this mysterious paper). I’ll update this post if I hear back.
A Toyota concept car at CES 2017. (GeekWire File Photo) Since first launched in 1997, a lot has changed in the automotive world. The Seattle-based company has not only remained relevant but is now attracting investor attention from one of the world’s largest car companies. Airbiquity today announced a $15 million investment round from Toyota Motor Corporation, Toyota Tsusho Corporation (Toyota’s trading arm), and DENSO Corporation (a giant automotive parts manufacturer partly owned by Toyota). Airbiquity has been building automotive telematics technology for more than two decades. Its focus is now on Choreo, a cloud-based connected car delivery platform, and , software that lets car manufacturers continuously update in-car software technology. (Airbiquity Photo) The company supports more than eight million vehicles across more than 60 countries and 30 languages. “We are delighted to receive investment from three of the most successful corporations in the automotive industry,” , the Airbiquity CEO who joined the company in 2002, said in a statement. “This is an exciting time for our company, and we look forward to working with our new strategic partners to optimize and leverage OTAmatic for the next generation of connected vehicle.” Investment in self-driving cars and related technology could help boost Airbiquity’s value proposition. Toyota itself has been over the past several years. The market for advanced driver-assistance systems technology could reach $35 billion by 2021, according to .