Nvidia’s Drive PX Pegasus Is A Royal Flush For Self-Driving Cars – NVIDIA Corporation (NASDAQ:NVDA)

The Auto segment of Nvidia (NVDA) is the smallest contributor in terms of quarterly revenue. It only contributed $142 million in Q2 FY18. The Auto segment can probably see a big spurt in growth when Nvidia starts shipping its Drive PX Pegasus next year. Drive PX Pegasus is Nvidia’s ambitious license-plate-sized in-vehicle datacenter-level processor intended for level 5 (or fully autonomous) robotaxis. There are now 25++ companies developing Nvidia Drive PX-based robotaxis.(Source: NVIDIA)

Anything that can help increase the quarterly revenue of Nvidia’s Auto business unit is an important matter that should be discussed here. To gauge just how important self-driving car technology-related products like the Drive PX Pegasus, read on Google’s (GOOG) (NASDAQ:GOOGL) billion-dollar legal suit against Uber allegedly receiving Waymo trade secrets.

The Drive PX Pegasus is 10x more powerful than the Drive PX 2 hardware. It can reportedly match the compute power of a 100-server datacenter rack. I consider this new hardware from Nvidia as the super-sized version of Intel’s (INTC) neuromorphic processor.

A little tweaking from Nvidia and Drive PX Pegasus can also self-learn like a human brain. Simultaneously, it can still deliver over 320 trillion operations per second compute performance without being tethered to the cloud.(Source: NVIDIA)

The Drive PX Pegasus could catapult Nvidia as the go-to processor supplier of the $38 billion/year ride-hailing industry’s adoption of robotaxis. Goldman Sachs also expects the ride-hailing industry to grow to $285 billion by 2030.

The Drive PX 2 Pegasus level 5 autonomous car processor can make Nvidia the enabler of some unicorn companies. The massive valuation of ride-sharing firms/taxi-hailing like Uber ($68 billion) and Didi Chuxing ($50 billion) can probably come true when they start augmenting their human-driven cars with Nvidia-powered robotaxis.

Nvidia can usher in the datacenter-in-a-car concept. Now that AMD is encroaching the high-end gaming/workstation GPU market with its Vega GPUs, Nvidia needs to work harder growing its Auto and datacenter GPU businesses.

Why I Am Optimistic About Robotaxis

Since Intel and Advanced Micro Devices (AMD) won’t have an equalizer to the Drive PX Pegasus, Nvidia can sell it at a high markup. After government regulators approve level 5 autonomous cars on the road, taxi fleet operators and ride-sharing companies will start using them. It is probably going to take 3-5 years before government regulators approve level 5 robotaxis. However, Nvidia’s surging valuation is already boosted by its potential role as the top supplier of semiconductor products for self-driving cars.

The three-year return of NVDA is more than 1,000%. Nvidia launched its Drive PX hardware/platform in March 2015. Nvidia priced its first Drive PX car processor at $10,000 and it still found acceptance. There are now more than 25 car manufacturers who work with Drive PX hardware. We have to conclude that the Auto segment is a contributor to the massive 3-year return of NVDA. No other company has matched Nvidia Drive PX’s level of acceptance among car manufacturers.

(Source: Morningstar)

Going forward, self-driving taxis can be better taxi-hailing service providers than human driven cars. More often than not, human drivers tend to burden me with stories about politics, their health, their family, their favorite TV shows and sports teams. I am ride-sharing to get from Point A to Point B. I do not need the additional aggravation of listening to another person’s ills/problems/thoughts.

If the travel time will take more than 30 minutes, I would prefer to nap rather than petty talk with a human driver. Robotaxis will assure me I won’t be driven around by chance by a sickly/DUI driver. Unlike a human driver, the Nvidia Drive PX Pegasus-based robotaxi won’t consider kidnapping or robbing its passenger.

Final Thoughts

The Gaming segment is currently the biggest revenue/profit generator of Nvidia. However, AMD will eventually take more market share in discrete gaming/mining GPUs. Nvidia developing more GPU products for Auto and datacenter is commendable. As far as I know, AMD still has no car-centric GPU in the pipeline.

As per released specs, the 500-Watt Drive PX Pegasus will come with 2 integrated Volta GPUs and 2 discrete post-Volta GPUs. The first Volta GPU, the Tesla V100 comes with 21 billion transistors, 5,120 CUDA cores, and 640 Tensor cores.

(Source: Wikipedia/Nvidia)

The Drive PX Pegasus is a stellar example of how far Nvidia has achieved in making its formerly gaming-only GPUs become in-demand for other enterprise-level, scalable applications. The rapid appreciation of Nvidia’s market cap over the last twelve months is because many investors believe in the future roles of GPUs in datacenter/deep learning and self-driving cars.

NVDA is a buy. According to the Artificial Intelligence-powered Relative Valuation Model of FundamentalSpeculation, NVDA is fairly valued.

(Source: FundamentalSpeculation)

FundamentalSpeculation derived a fair value of $182.74 for NVDA. It came about after FundamentalSpeculation’s AI computed the average valuation ratios of other companies with similar business fundamentals of Nvidia to get the Cohort Fair Value. After which, the Cohort Fair Value was modified with values derived from calculating the average valuation ratios of Nvidia’s peers in the Technology sector and Computer Hardware Industry.

Disclosure: I am/we are long NVDA, AMD, INTC, GOOG.

I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.

The Tesla Self-Driving Myth Dies Another Death – Tesla Motors (NASDAQ:TSLA)

The 2018 Cadillac CT-6, which includes the Super Cruise option.

I’ve already shown how Tesla (NASDAQ:TSLA) massively lags other companies when it comes to self-driving technology:

All of the above were and are very evident. Yet, believers kept on believing in something else: that Tesla was ahead. How could such obviously false belief even hold? The reason is simple:

  • The overwhelming majority of people do not follow and are not familiar with the actual state of the art when it comes to self-driving.

Instead, most people, and certainly the Tesla faithful, were familiar with something else. They were familiar with Tesla’s Autopilot capabilities. Those driver assistance capabilities, most notably the lane keeping abilities, were indeed the best which were commercially available.

As a result of this, most people have wrongly thought that Tesla led in self-driving technology. They thought that driver assistance leadership was the same as leading in self-driving technology development.

It was not, though. The best of today’s commercial driver assistance efforts is orders of magnitude less capable than any of the leading self-driving efforts being developed by Google (NASDAQ:GOOG) (NASDAQ:GOOGL), General Motors (NYSE:GM) or Nissan (OTCPK:NSANY). Those cars navigate city streets autonomously, and they do so safely under trying conditions. Google’s cars can go for thousands of miles between needing human intervention, and GM’s can go for hundreds of miles even on difficult San Francisco streets. They were able to do so on average during 2016. They’re likely a lot more advanced now.

So what does Tesla’s new self-driving death consist of? It consists on the realization that Tesla is about to lose even its driver assistance lead.

Whereas before Teslarians (Tesla fans) could point to Autopilot and say there was nothing quite like it, that’s really about to change – immediately. For instance, just recently the trusted Car and Driver magazine reviewed several driver assistance systems. Among them was Tesla’s Autopilot 2.0 and Cadillac’s Super Cruise.

It’s evident from the review that Cadillac’s system is the superior one for the purpose. A few of phrases make that evident:

Park it in the right lane, though, and Super Cruise could seemingly track forever. In 40 miles of driving, the system didn’t ask us to take the wheel, except when we intentionally aggravated it by looking away from the road for an extended period. After less than 10 minutes, Super Cruise’s rock-steady competence became boring.

This comparison will likely be made over and over. Some won’t quite understand immediately why GM’s system cannot but be superior, for instance, Jalopnik thought the two systems were more or less equivalent. Jalopnik even wrote about how Tesla has automatic lane changes where the car checks for safety while GM does not.

Alas, as was Charles Mackay once said:

Men, it has been well said, think in herds; it will be seen that they go mad in herds, while they only recover their senses slowly, and one by one.

Hence, above, Car and Driver has recovered its senses, and Jalopnik is in the process – just by deeming the systems more or less equivalent. I’m saying this because:

  • Tesla’s system remains highly unsafe since the car is prone to have a mind of its own and simply track “the wrong lanes.” Meaning, the car can suddenly swerve out of its lane or into a divider. GM’s car won’t do this (provided it’s not used in a construction zone, where the Tesla can also fail). Why not? Because GM’s solution doesn’t just rely on seeing the lanes. The car uses an HD Map of its surroundings and has precise information about the road and lanes it’s traveling on, as well as its own position even without being able to see the lanes. As a result, the car does not need to guess. Also as a consequence, it won’t swerve suddenly because it thinks the road/lane follows this or that curvature … since it knows what the curvature is and where the lanes are, at all times.
  • Tesla’s automatic lane change previously couldn’t check for safety (the range of the ultrasound sensors was not enough), and now likely doesn’t check for safety other than using the improved ultrasound sensors (whose range is still insufficient). It likely doesn’t because Tesla hasn’t enabled the use of the required cameras yet – though this might happen in the future. As a result, using automatic lane changes without the driver checking for safety is still unsafe in the Tesla. The same care, obviously, remains needed in the Cadillac.

Anyway, the relevant point here is that Cadillac’s system will be shown to be superior for the purpose – providing reliable driving assistance in highway driving. This will slowly filter out through multiple reviews. Tesla’s system, though, remains unreliable and prone to suddenly change course.

Both systems require the driver to keep his attention on the road, of course. But Tesla requires the driver to be on the alert not just for what the road might bring, but also how the car might irrationally behave when nothing threatens it. GM’s system will just require attention to what the road might bring.

This difference is massive, and those reviewers knowledgeable enough will identify it: What the road might bring is predictable if you are looking at the road. You know when you can be relaxed since there is no immediate incoming danger. Tesla’s approach, on the other hand, means action might be required at any moment, out of the blue, even with no observable danger within sight, just because of the car’s reactions to things the driver does not perceive as problems. This is intrinsic to the technological approaches followed by GM and Tesla.

Conclusion

The myth that Tesla was ahead in terms of self-driving technology was long dead for any knowledgeable person following developments in the industry.

However, for the general public, the myth was and is as alive as ever. This happens because the general public doesn’t follow the state of the art when it comes to self-driving. Instead, the general public conflates the state of driving assistance with self-driving. And Tesla did indeed lead in commercially available driving assistance (the lane keeping part, at least). As a result, the general public thought Tesla was the leader in self-driving technology.

It is that second myth that starts dying now, with Cadillac’s fielding of a superior driver assistance feature due to technological features not used by Tesla. The myth also will likely take further blows as other advanced systems make their way to the market.

Of course, the myth certainly won’t survive past the day where Google and GM start providing services based on self-driving technology even while Tesla has nothing to deliver.

In the meantime, rumor has it that at least 35,000 Tesla owners bought the FSD (Full Self-Driving) option for their cars. Tesla won’t deliver on this ability (not before others, and not with the current hardware, anyway), and these 35,000 cars (and growing) will become a giant liability.

In my opinion, Tesla will try to fend off the liability by returning the $3,000 option value per car to the owners, but this won’t be sufficient. The reason is simple: those owners can easily argue that they just bought the whole car because in time it would self-drive itself. As a result, the liability will be for the whole car and not just the FSD option.

Disclosure: I/we have no positions in any stocks mentioned, but may initiate a short position in TSLA over the next 72 hours.

I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.

Nvidia’s AI Developments Don’t Change Self-Driving Car Timeline (NASDAQ:NVDA)

Robotics Analyst: Nvidia's AI Developments Don't Change The Self-Driving Car Timeline

NVIDIA Corporation (NASDAQ: NVDA) stirred the markets Tuesday with news of its groundbreaking supercomputer capable of supporting fully autonomous vehicles. The announcement drove Nvidia shares to the $190 level.

But for all the excitement, the world’s first artificial intelligence computer for “Level 5” autonomy may not widely impact the AV space.

“This is definitely a net positive for the entire self-driving car space but doesn’t change our thesis on electric or self-driving vehicles,” Austin Bohlig, robotics industry adviser at Loup Ventures, told Benzinga. “At the least, it may have modestly shortened the time frame when we begin to see self-driving vehicles on the road, but our 2020 date for the influx to begin still holds.”

Nvidia is already considered a leader among AV suppliers. Loup managing partner Gene Munster previously listed Nvidia as the field’s top components manufacturer and the best way to play the AV theme outside of a Tesla Inc (NASDAQ: TSLA) stake.

The Tesla Impact

Tesla currently uses Nvidia chips, but was recently reported to have partnered with Advanced Micro Devices, Inc. (NASDAQ: AMD) in an alleged step toward self-sufficiency. Analysts posited that the move could be indicative of Tesla’s eventual shift from Nvidia products but insisted that Nvidia investors had little to worry about.

Tesla supporters were similarly unconcerned by Nvidia’s latest demonstration of strength.

“As it relates to Tesla, I don’t think they are second guessing themselves [with regard to the AMD partnership],” Bohlig said. “While Nvidia may have a slight [leg] up in the GPU space, AMD has made great progress over the last couple years and is much more competitive today.”

Loup still considers Tesla and Alphabet Inc (NASDAQ: GOOGL)’s Waymo leaders in self-driving auto manufacturing.

Related Links:

The Autonomous Future: Munster’s 2020 Vision Of The Road

Gene Munster: Traditional Car Manufacturers Face ‘Innovator’s Dilemma’

Posted-In: Analyst Color News Travel Top Stories Exclusives Tech Trading Ideas General Best of Benzinga

Uber self-driving fleet has safety drivers to take control

uber-mapping-car-7153.jpg

Here’s one of Uber’s self-driving Volvos.


James Martin/CNET

Before Rick McKahan could even get behind the wheel of one of Uber’s self-driving cars as a “safety driver,” he had to spend days driving around a small simulated city in Pennsylvania, handling corners and dodging obstacles.

Now he’s tasked with training other safety drivers at Uber’s test track in Pittsburgh.

At the ride-hailing company’s autonomous vehicle training facility on the banks of the city’s lush, green Monongahela River, aka “The Mon,” Uber’s self-driving cars learn to navigate the roads. And prospective safety drivers spend three weeks learning to operate the robo-cars — Ford Fusions and Volvo XC90s — so they can take over when needed.

“When we simulate real world experiences, we make that the worse-case scenario,” said McKahan, a 28-year-old who was an oil and gas professional before he joined Uber last year to become a vehicle operator trainer. “When they get off the test track and get onto the public road, that actually seems easier than the first week of training.”


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As self-driving cars circle around grassy knolls and past parked cars at the closed-course facility, passenger doors suddenly fly open to test whether vehicles will safely steer out of the way. Jaywalking zombielike mannequins scoot out in front of moving automobiles to teach the cars to stop for pedestrians before it’s too late.

Self-driving cars use a series of sensors, lasers and cameras to “see” their surroundings and detect traffic, pedestrians, bicyclists and other obstacles. Uber is one of dozens of companies working on bringing the futuristic tech to the roads. Automakers from Toyota to Ford to Volvo all have projects underway, as do Silicon Valley giants including Google, Apple and Tesla.

Uber started working on self-driving cars in 2015 and is the first company to roll out autonomous vehicles to the public. It brought a small fleet of self-driving cars to passengers in Pittsburgh in September 2016. The company has since driven more than 1 million autonomous miles and is testing about 200 cars in Pennsylvania, Arizona and California.

uber-mapping-car-7145.jpg

Uber’s self-driving cars lined up behind a warehouse in downtown San Francisco last December.


James Martin/CNET

Uber’s autonomous vehicle project isn’t free from controversy, though. The company is currently in a legal battle with Waymo, the self-driving car unit of Google parent company Alphabet. Waymo accused Uber of stealing its autonomous vehicle technology; Uber calls Waymo’s claims “baseless.” The trial is slated to begin in December.

The ride-hailing company is also reeling from a botched launch of its self-driving cars to passengers in San Francisco last December. After Uber failed to secure permission from the state’s Department of Motor Vehicles to have its cars on the road, it was forced to halt the passenger program. Uber also had to deal with a handful of safety issues during that time, including one vehicle caught running a red light.

Since then, its self-driving cars have been involved in minor accidents in Pittsburgh and Tempe, Arizona. Even though injuries weren’t reported and police determined Uber wasn’t at fault for the crashes, it’s up to the company to show the public its self-driving cars are safe.

Backseat driver

Companies working on self-driving car tech say these vehicles will one day be safer than driver-controlled autos and that they can also help ease traffic congestion and eliminate drunk driving. But until that future arrives, a safety driver needs to be in each car.

Anyone with a clean driving record can sign up to be one of Uber’s hundreds of vehicle operators, but applicants must first go through a series of driving tests in a regular car. If they pass those and get through the interview process, they’ll next enter Uber’s three-week training period.

Week one of training includes classroom instruction, exams and supervised driving at the test track. In weeks two and three, trainees go out onto public roads but only on preselected routes with an experienced operator, aka a backseat driver. Uber declined to say how much its safety drivers earn.

“We have a very strict process for bringing drivers onto our program,” McKahan said. “Part of that process is with a professional driving company where they learn how to operate a vehicle in situations that might be less than ideal.”

Uber’s test track in Pittsburgh where it trains “safety drivers” how to control self-driving cars.


Uber

This training program constantly shifts to deal with the technology as it evolves and conditions change. For example, snow on the ground could change how a vehicle acts and reacts, so drivers need to be ready for that. There are also the normal ins and outs of driving in a city.

“We’re in a really jam-packed city here in Pittsburgh,” McKahan said. “Every day we’re dealing with pedestrians, bicyclists, different traffic lights, construction zones — just anything that is a challenge for a vehicle or a driver.”

When safety drivers are at the wheel of a self-driving car (without actually holding onto the steering wheel), they’re not sitting back and relaxing, McKahan said. They have to be actively monitoring what the vehicle is doing and documenting the behavior of the car. It’s still a learning process for both the car and the drivers.

I ask McKahan if he’d be OK being a passenger in a self-driving car without a safety driver.

“Maybe on the track, at this point,” he said. “In a controlled environment.”


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Uber puts self-driving cars on the road: In 2016, the ride-hailing app launched its own fleet of self-driving cars for passengers in Pittsburgh and San Francisco. 

Roadshow: Everything about the intersection of cars and technology. 

Nvidia to step up self-driving focus – Business

Nvidia to step up self-driving focus

The logo of Nvidia Corporation is seen during the annual Computex computer exhibition in Taipei, May 30, 2017. [Photo/Agencies]

Nvidia Corp, the world’s biggest maker of graphics chips used by computer gamers, will step up efforts to tap into opportunities created by self-driving and the big data industry in China.

Jensen Huang, founder and CEO of Nvidia, said on Wednesday that China is the one of the fastest-growing markets for the United States semiconductor maker.

“No industry has achieved what China’s computer and IT industry has achieved in 10 years. No consumer market has achieved what China has achieved in 10 years,” Huang said.

Nvidia is the leading supplier of graphics processors, or chips that help enable high-definition images on computer games. As artificial intelligence demands bigger computing powers, the US company is leveraging the advantages of graphics chips to help accelerate nascent technologies from voice recognition to self-driving cars.

In China, it partnered with the search engine giant Baidu Inc to accelerate the development of autonomous vehicles. Baidu used both Nvidia’s chips and solutions in its Apollo project, which is designed to open its autonomous driving platform to partners.

Nvidia has also invested in TuSimple, a Chinese startup that offers autonomous driving solutions for trucks.

Huang said many elements, including data and in-car operating systems, are important to the development of self-driving, but a holistic system is the key.

“We need to create a wholly integrated system, from architecture, AI algorithm, technologies for safety, cloud technology, to sensors, so that a car can operate safely,” he added.

Nvidia developed a self-driving platform called Drive PX, and carmakers increasingly look to it to run the data-intensive deep learning calculations, though many of these projects are still in the pilot stage.

It is also muscling in to the big data industry, which is demanding more chips used in big data centers. That is the most profitable business for its rival Intel Corp.

Chinese tech heavyweights such as Baidu, Alibaba Group Holding Ltd, and Tencent Holdings Ltd are using Nvidia’s technologies for their cloud business.

Nvidia posted second-quarter earnings that beat Wall Street estimates: $2.23 billion in revenue on earnings of 92 cents per share, versus estimates of $1.96 billion in revenue on earnings of 70 cents per share.

The road beyond self-driving cars

Many autonomous vehicle discussions don’t go far enough in describing the impact the technology will have on behavior over time. We are concerned mostly with whether we’re talking about “hands on” or “hands off” the steering wheel, but at some point, confidence in the technology will grow and we won’t have to pay attention to the road or other cars at all as we ride. Then we can begin to consider how other areas of life, work, and travel can be supported by these evolving vehicles.

For example, when all riders are focused inward and the driving is handled by a sensor network, indicators like road signs, brake lights, and lane separators become unnecessary. If there are no human drivers, we won’t have a need for these visual guides.

By dividing the rollout of autonomous vehicles into stages, breaking down the component parts, and connecting it to other trends, we can reveal the most likely areas of impact.

Trucks, rides, and safety

Examples of self-driving trucks are already appearing, and a primary suggested benefit is that autonomous trucks will make roads safer. Rides for the elderly and others who are unable to drive is another clear early benefit of driverless vehicles.

Possible outcomes:

  • Rides for kids going to after-school activities, with in-vehicle monitoring for parents. An autonomous ride could become preferable to having a stranger in the driver’s seat.
  • Rides for homebound elderly and vision-impaired people, with in-vehicle monitoring and voice services. Amazon Alexa is already joining this part of the trend.
  • In-vehicle “Meals on the Way” services for riders become extensions of food service and delivery. This could also prompt in-vehicle packaging and storage innovation for such services.
  • Seat-surround airbag systems protect passengers independent of orientation.
  • Highways institute dedicated nighttime hours and lanes for self-driving trucks.
  • Continuous shipping, battery-swapping stations, and mobile-charging vehicles keep autonomous trucks on the road at all times.
  • New autonomous and manual vehicles transmit location automatically to provide system awareness to all cars on the road. This could improve the flow of traffic overall but presents some inherent security concerns.
  • Improved solar panel efficiency enables roof-charging for trucks and cars. This could extend travel time and reduce the need for charging stations.

Work and roads

As riding becomes the preferred way to travel, larger “travel pods” will become a natural extension of the growing shared-workspace trend. All visual indicators can be removed from the road and new elements moved inside the vehicle when the act of driving is handled by sensors.

Possible Outcomes:

  • Self-driving working pods for small-team domestic travel. This can reduce travel costs and increase continuity of work.
  • Mobile workspaces connect with shared workspaces.
  • “Sleep cars” become the new, less expensive way to travel short distances, reducing short-range air and train trips.
  • In-car video calling is standard for new self-driving vehicles.
  • Interior brand elements and lighting become more important as visitors and viewers are focused inward.
  • With awareness of approaching vehicles and traffic, intersection traffic lights become less necessary.
  • Night sensor driving reduces the need for streetlights on highways.
  • Road signs and lanes disappear, with roadway intelligence built into vehicles.
  • Highway lanes expand and contract automatically for high-traffic times.
  • Autonomous-only highways allow for much higher rates of speed.
  • Mobile and Wi-Fi networks installed in vehicles allow for dynamic moving networks.

Ownership, homes, and recreation

As people focus more on rides and less on cars, it will start to shift how we design and use areas of our homes and could start a shift toward “manual driving” as a recreational activity.

Possible outcomes:

  • Garages are hired out as self-driving car charging and storage stations.
  • Personal car insurance becomes less common as insurance is handled by driving services.
  • Recreational driving services appear for manual driving, leading to fewer car dealerships.
  • Specialized recreation areas appear for manual driving.
  • Street pickup area indentations at the curb in front of homes become the new driveway. Driveways and garages are no longer standard in home construction.
  • Self-driving tiny homes combine two growing trends.

Merging transportation modes

As these vehicles begin to look less like cars and more like transport pods, they can easily be seen as modular plug-in points for other modes of travel.

Possible outcomes:

  • Modular self-driving pods appear and can drop into Hyperloop tubes for traveling longer distances.
  • Modular vehicles can dock into homes, making travel easier.
  • Aircraft with docking bays for the seating pods from mobile driving units become available, increasing the efficiency of ticketing, boarding, and air travel.

Autonomous cars are not the only area that can be broken down into component parts and sequenced over time and trends. This kind of service and product decomposition can be a good way to look at strategic areas of focus and can reveal unexpected new products and services for companies to explore in any industry.

John Jones, senior vice president of design strategy at Fjord, design and innovation from Accenture Interactive

This story originally appeared on Medium. Copyright 2017.

Waymo wants to postpone the self-driving car technology trial with Uber


In light of the recent federal circuit ruling that Uber must hand over the due diligence report (the Stroz report) it conducted ahead of its acquisition of Otto, Waymo is requesting Judge William Alsup postpone the trial. The trial is currently scheduled to begin October 11.

“It’s now clear why Uber had fought so hard to hide these materials from Waymo and the Court,” a Waymo spokesperson told TechCrunch. “In addition to the Stroz Report, thousands of new documents and hundreds of previously unexamined devices are now being turned over to Waymo, adding to the direct evidence we’ve already found of trade secret misappropriation. They go to the heart of our case and in order to accommodate new depositions, expert reports and briefings, we’ve asked for additional time before trial.”

Last week, a federal circuit appeals judge ruled that Uber must hand over the due diligence report it commissioned ahead of its acquisition of self-driving truck startup Otto. For months, Waymo has argued this report, conducted by legal firm Stroz Friedberg, likely contains information pertinent to its allegations that one of its former engineers, Anthony Levandowski, stole thousands of files and brought them to Uber.

Now that Waymo is getting these documents, the company says it needs way more time to review them. As a result of the ruling, Uber and Stroz are producing thousands of new documents and hundreds of devices that were previously unavailable to Waymo.

Judge Alsup has set a private hearing to discuss the recent developments in the case tomorrow. On October 3, Alsup will host a hearing to discuss motions to continue the trial.

Uber declined to comment for this story.

Featured Image: Bryce Durbin/TechCrunch

Pittsburgh’s self-driving car boom means $200,000 pay packages for robotics grads

Pilot models of the Uber self-driving car are displayed at the Uber Advanced Technologies Center on September 13, 2016, in Pittsburgh.

Angelo Merendino | AFP | Getty Images

Pilot models of the Uber self-driving car are displayed at the Uber Advanced Technologies Center on September 13, 2016, in Pittsburgh.

There’s a war for talent in Pittsburgh’s booming autonomous car market.

It started with Uber and now includes Argo AI, which is majority owned by Ford, and a start-up called Aurora Innovation. With so much hiring, it’s a good time to be at the city’s prized academic institution, Carnegie Mellon University.

Andrew Moore, the dean of Carnegie Mellon’s computer science school, said that computer vision graduates right out of college are commanding pay packages of $200,000, which he described as “unheard of for any role until recently.”

In addition to Uber, Argo and Aurora, Moore said there’s a fourth self-driving car company in Pittsburgh that’s not yet talking publicly.

“One of the effects is this dramatic salary rise for anyone with robotics engineering skills,” said Moore, whose background is in artificial intelligence and robotics. “It does feel very much like a gold rush town at the moment.”

Moore, who previously spent eight years at Google and ran the company’s Pittsburgh office, estimates that there are 1,000 to 2,000 people in the city working on autonomous driving. Pittsburgh has become the de facto capital for self-driving car development, thanks to Carnegie Mellon’s top-ranked robotics program and the city’s openness to partnering with tech companies on risky endeavors.

Despite all of Uber’s legal, cultural and management troubles, the ride-hailing company is aggressively hiring in Pittsburgh. Uber currently has 60 job openings there in its advanced technologies group, which houses the self-driving engineering team.

Samsung makes a $300 million push into self-driving cars

Samsung is making a big push into the nascent self-driving car market with a new $300 million fund and a dedicated business unit for developing autonomous technology, the company announced today. That new unit will be built inside Harman, the audio technology giant that Samsung acquired for $8 billion late last year. Its goal will be to build a top to bottom technological platform that automakers can incorporate into their cars to run everything from infotainment to self-driving capabilities.

The announcement comes two weeks after the California Department of Motor Vehicles revealed Samsung was granted a self-driving vehicle permit in that state, adding to the ones that the company already had in its home country.

Of course, dozens of companies big and small have self-driving permits in California. So today’s news offers much more clarity about Samsung’s big ambitions in the autonomous vehicle space, including a goal of building a more open-source suite of self-driving software solutions.

“Our industry is literally screaming, saying, ‘We love Mobileye but we need an open platform,’” Dinesh Paliwal, Harman’s chief executive officer, told Bloomberg. “Competition is the best thing ever. The auto industry wants us to do it and we think we have the capacity and the fuel power.”

Mobileye, which is perhaps best known for being a key partner in the original version of Tesla’s Autopilot software, is now owned by Intel, which has automotive partnerships with BMW, Fiat Chrysler, and Delphi Automotive.

To accomplish this, Samsung’s first investment from that new automotive fund is in TTTech, a company that powers the semi-autonomous and automatic safety capabilities of the 2017 Audi A8. (Audi also already uses Samsung processors, semiconductors, and other components for its infotainment systems and driver assistance features.) Samsung plans to work with TTTech on everything from infotainment to “scalable architectures to support fully autonomous vehicles across various industries,” according to a statement.

“We’re excited about Samsung’s commitment to TTTech and the joint creation of new architecture for open autonomous and ADAS technologies, involving multiple key automotive players and suppliers,” Alejandro Vukotich, the vice president of autonomous driving of Audi said in a statement.

In that sense, it sounds like Samsung wants to be more than just an open-source competitor to Mobileye. It also sounds a lot like the partnership model that Waymo, the former Google self-driving car project, has been pursuing since late last year. Of course, Waymo has a head start of a few million self-driven miles.

Samsung Given Approval To Test Self-Driving Cars In California

September 9th, 2017 by James Ayre 

Samsung has been given approval from the California Department of Motor Vehicles to begin testing self-driving vehicle tech on public roads in the state, according to recent reports.

The decision means that the already crowded autonomous vehicle development sector in California is now even more crowded. It’s already home to Waymo/Google, GM, Apple, Tesla, Bosch, Baidu, BMW, Lyft, Daimler, Nissan, NVIDIA, Delphi, and numerous others.

This news follows our earlier article revealing that Samsung had been granted approval to begin testing its self-driving vehicle tech on public roads in its home market of South Korea.

The Verge provides more on that: “In May, Samsung received approval to test cars in its home country of South Korea, using software adapted to Hyundai vehicles. That sounds odd because Samsung owns nearly 20 percent of Renault Samsung Motors, which is part of the Renault-Nissan alliance that’s the world’s fourth-largest auto producer. Nissan also has approval to test self-driving cars in California. But in 2015, Samsung announced a new division that would focus on self-driving vehicle software, rather than the creation of the entire vehicle.”

It’s an interesting situation. Pretty much every company out there with any connection at all to the auto industry seems to be taking aim at self-driving vehicle tech. You have to wonder at this point, though, how many of these companies will end up possessing market share (of any kind) a decade or two from now. One of them? A couple of them? All of them?

Also worth remembering: Samsung (Samsung SDI) is one of the world’s leading battery producers, providing batteries for BMW’s electric cars, some of Daimler’s, Porsche’s, and the Fiat 500e.


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Tags: California, samsung


About the Author

James Ayre ‘s background is predominantly in geopolitics and history, but he has an obsessive interest in pretty much everything. After an early life spent in the Imperial Free City of Dortmund, James followed the river Ruhr to Cofbuokheim, where he attended the University of Astnide. And where he also briefly considered entering the coal mining business. He currently writes for a living, on a broad variety of subjects, ranging from science, to politics, to military history, to renewable energy. You can follow his work on Google+.