A Google Engineer Accused of Stealing Trade Secrets Sold to Uber

A former Google engineer has been accused of stealing trade secrets from the company, which he then sold to Uber, the Associated Press reported.

A former Google engineer has been accused of stealing trade secrets from the company, which he then sold to Uber, the Associated Press reported.

The charge filed by the San Jose, California prosecutor’s office is related to a case filed in 2017 by Google’s Unmanned Aerial Vehicle division, Waymo. Uber agreed to pay $ 245 million to settle the case, but the federal judge in charge of the case made an unusual recommendation to launch a criminal investigation.

It’s important for Uber to have self-driving car technology

Anthony Levandowski, a pioneer in the development of robotic cars, has been accused of stealing trade secrets. He could be sentenced to 10 years in prison and a fine of $ 250,000 for each of the 33 counts or a total of 8.25 million, the BTA reported.

According to the prosecution, months before his abrupt departure in 2016, Levandowski downloaded numerous files from Google’s self-driving car program and used the information to create his Otomotto company, which was later acquired by Uber for $ 680 million.

Prosecutors have indicated that the investigation is ongoing, but will not specify whether Uber and company founder Travis Kalanik are the subject of the investigation.

“He didn’t steal anything, from anyone,” the statement reads. “This case rehashes claims already discredited in a civil case that settled more than a year and a half ago. The downloads at issue occurred while Anthony was still working at Google—when he and his team were authorized to use the information. None of these supposedly secret files ever went to Uber or to any other company.”, Tech Crunch reported.

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Self-Driving Cars Still Face A Long Road

Uber CEO Travis Kalanick promised in 2016 that his company would get rid of human drivers by 2020. Various automakers heralded a huge shift in …

Self-driving cars are the future. Probably. Just maybe not as near a future as the automotive industry – and optimistic investors – had imagined.

A few years ago, the rise of driverless cars seemed not only inevitable but imminent. Google CEO Sergey Brin once predicted that “ordinary people” would be using fully autonomous vehicles by 2017. Uber CEO Travis Kalanick promised in 2016 that his company would get rid of human drivers by 2020. Various automakers heralded a huge shift in the type of vehicles they’d be making within a handful of years. Just a few years ago, the federal Transportation Department worried that technology would outpace regulation.

But more recently, a future without drivers seems much further off. In 2018, a self-driving car owned by Uber killed a pedestrian in Tempe, Arizona. That accident put a chill on not only Uber’s testing, but the development of other autonomous vehicles, as popular sentiment shifted toward greater wariness. Companies abruptly seemed to realize the high stakes that testing in populated areas might involve.

Popular distrust may also hinder the widespread adoption of driverless cars, even as the technology improves. Beyond worries about accidental collisions, security is a major concern. No passenger wants to be in a car when the driver is subject to hacking. AAA found that 71% of Americans surveyed were afraid to ride in a fully autonomous car. Industry experts suggest that this fear will decrease as people have more experience with these vehicles. But when and how most people will get that experience isn’t clear.

Beyond safety concerns, there are other practical considerations. As Timothy B. Lee observed for Ars Technica earlier this year, there are two phases of development for self-driving cars. The first involves making sure the vehicle obeys traffic laws, preserves the safety of passengers and pedestrians, and generally avoids obstacles. Some companies, including Waymo, have made it this far.

Yet the second stage may be equally important for widespread adoption. In stage two, vehicles will have to learn to make quick, accurate decisions in order to move as a skilled human driver might. Otherwise, the vehicle could have trouble navigating roundabouts, merging onto highways, or completing other potentially dangerous but essential elements of driving. A car that doesn’t master stage two may get you safely from point A to point B, but it would be a long and frustrating ride. As Sam Anthony, the chief technology officer of Perceptive Automata, told The Huffington Post, “The difference between a good self-driving car and a perfect self-driving car is massive.”

Creating a smooth and straightforward ride has proven more challenging than automakers initially anticipated. While driving on well-paved roads in good conditions is one thing, autonomous vehicles have had trouble with heavy rain and other challenging weather. Construction and badly maintained roads also cause problems. Self-driving cars will need to anticipate the actions of human drivers, cyclists and pedestrians, which may be hard to predict through programming. All of this means development has taken more time than companies originally projected.

Regulation is also a concern. No federal rules currently govern the testing or development of autonomous vehicles, though some states make it harder or easier for developers to operate. Carmakers also may try to influence lawmakers. Some have suggested that updating road infrastructure could mitigate some of the vehicles’ technological shortcomings. An unnamed automotive official suggested to The New York Times that Manhattan could install anti-jaywalking gates at each intersection. (I’ll let you imagine how New Yorkers are likely to take this idea.) How the interplay between government and business evolves may hinder or help the rollout of self-driving cars and trucks.

While boosters remain – Tesla CEO Elon Musk remains confident that driverless vehicles will shuttle people to their destinations by late 2020 – most industry insiders agree we’re decades away from fleets of cars that don’t need monitoring from a human driver. General Motors’ subsidiary Cruise said in 2017 that it would launch automated taxis in San Francisco by the end of 2019. This July, Cruise announced that it would not meet the deadline and did not set a new one. Waymo launched a taxi-like service in Arizona last year as promised, except that human drivers remain behind the wheel.

In light of all these setbacks, it seems reasonable to wonder: Just how far away are self-driving cars from commonplace use? What if they never arrive at all, at least to the extent developers have promised?

Self-driving cars are not an all-or-nothing proposition. Many vehicles on the road now boast “advanced driver assistance systems” that automate at least some tasks, such as adaptive cruise control or the ability to park with minimal driver input. Much like automatic transmissions and power steering, such features are likely to become an everyday part of how we drive as older cars leave the roads. Even if we don’t reach a point where there is no driver, the driver may be able to rely with increasing confidence on the car’s computing power.

SAE International, an organization that develops engineering standards in the automotive industry and others, describes vehicle automation on a scale of zero to five. At level zero, the human driver is responsible for all driving tasks. Most cars with “advanced driver assistance systems” would be around a level two, though level three automated vehicles may become common in the future. Think of the way autopilot systems today assist in most, if not all, aspects of flying a commercial jet. Level five would be a fully autonomous experience, in which the only thing a human needs to do is enter the destination. That has yet to arrive.

In some cases, full level-five autonomy may never be the goal. For example, most startups focused on long-haul trucking intend to leave urban driving to humans for the foreseeable future, even when trucks are ready to handle highways unaided. Yet for those invested in a completely autonomous future for cars, levels one through four likely aren’t good enough.

Based on indications today, I would not be surprised to see autonomous vehicles in more limited use in the near future. On a closed course under controlled conditions, they can work. For instance, Optimus Ride recently launched a shuttle service in the Brooklyn Navy Yard, which will carry passengers between the office park’s entrance and its new ferry dock. (For now, this service still involves human operators as a safety precaution.) Autonomous vehicles may be useful in areas that are otherwise closed to motor vehicles – say, moving people around amusement parks, airports or college campuses.

Investors, however, have plenty of reasons to be wary of any champagne-tinted predictions that traditional, human-driven cars will vanish. Self-driving cars are unlikely to revolutionize the auto industry in the next few years. They are not going to resolve the economics of ride-hailing services like Uber in the next year or two, either. In fact, like ride-hailing – as well as food delivery services and a variety of other sectors – self-driving cars are attracting a lot of hype but haven’t yet proven a profitable and reliable part of our future.

When it comes to truly driverless cars, the future may or may not be coming, but it certainly isn’t now.

Vice President and Chief Investment Officer Paul Jacobs, of our Atlanta office, contributed several chapters to our firm’s book, Looking Ahead: Life, Family, Wealth and Business After 55, including Chapter 12, “Retirement Plans;” Chapter 15, “Investment Approaches and Philosophy;” and Chapter 19, “A Second Act: Starting a New Venture.”

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2019 Worldwide Automotive Lidar Market Development, Technologies and Major Key Players …

ReportsnReports added a new report on The Automotive Lidar Market report delivers the clean elaborated structure of the Report comprising each …

ReportsnReports added a new report on The Automotive Lidar Market report delivers the clean elaborated structure of the Report comprising each and every business related information of the market at a global level. The complete range of information related to the Automotive Lidar Market is obtained through various sources and this obtained bulk of the information is arranged, processed, and represented by a group of specialists through the application of different methodological techniques and analytical tools such as SWOT analysis to generate a whole set of trade based study regarding the Automotive Lidar Market.

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LiDAR (Light Detection and Ranging) has been widely used in satellite systems, geographic information systems, and industrial applications. In recent years, LiDAR systems for autonomous navigation have attracted huge investments from carmakers, automotive electronics vendors, hedge fund companies, and IT firms due to significant improvements in the weight and size of the systems. Some LiDAR startups have been able to raise more money between US$ 1 million and US$100 million, indicating the importance of LiDAR in autonomous vehicles and its impact on the industry. This report analyzes the development of automotive LiDAR technologies and leading players, mainly Velodyne, Ibeo, Quanergy, and Luminar.

List of Topics in this report:

-Development of automotive LiDAR technologies and includes an overview of patent applications filed by leading brands such as Google, Hyundai, Microsoft, Waymo, GM, Patheon Company, HERE Global B.V., Apple, LeddarTech, and Ford

-Development of automotive LiDAR systems and major players, including Velodyne, Quanergy, Ibeo, Luminar, and Chinese companies

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List of Companies in this report:

1517 Fund

360 Capital Partners




Analog Devices





AutoTech Venture



Bessemer Venture Partners

Black Forest Engineering





Canvas Ventures

Caterpillar Venture Capital

Composite Capital Management







Fastree 3D






Genius Pros

Glory Ventures



GP Capital

Grains Valley VC

Greylock Partners

GVA Capital


HERE Global B.V

Hesai Technologies



iGaming Platform




Jiangmen VC



LeiShen Intelligent System

Light House Capital


Magma Venture


Magneti Marelli

Maniv Mobility


Millennium Technology

Motus Ventures

Nautilus Venture Partners


Navya Arma


Next Frontier Capital




Oryx Vision


Pagoda Investment



Raytheon Company



Samsung Electronics







TASS international

TEEC Angel Fund


Third Point Ventures


Toyo Corp.



Trucks VC




Ulrich Lages


Value Partners




Wardenclyffe Partners LLC





Zhiping Capita

Table of Contents in this report:

1.Automotive LiDAR Technologies

1.2 Analysis of Automotive LiDAR Patents

2.Development of Automotive LiDAR Systems

3.Development of Automotive LiDAR Companies

3.1 Fundraising Status of Startups

3.1 Leading Vendors of Autonomous LiDAR Systems

3.1.1 Velodyne

3.1.2 Quanergy

3.1.3 Ibeo

3.1.4 Luminar

3.1.5 Chinese LiDAR Vendors

4.MIC Perspective


Glossary of Terms

List of Companies

and more

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ReportsnReports.com is your single source for all market research needs. Our database includes 500,000+ market research reports from over 95 leading global publishers & in-depth market research studies of over 5000 micro markets.

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The world’s leader in self-driving lidar technology is suing two Chinese companies over IP

(Elon Musk, though, has argued that lidar isn’t necessary, and is instead banking on cameras.) The units are incredibly expensive. A Velodyne …

China’s vision of profiting from the driverless economy is being clouded by intellectual property debates, as two Chinese suppliers of a key self-driving technology are accused of stealing IP from a Silicon Valley company.

Velodyne, the world’s largest producer of lidar—a radar-like sensor technology that uses lasers instead of radio waves—has brought patent infringement complaints against Suteng (also known as Robosense) and Hesai. According to two complaints filed to the United States District Court for the Northern District of California on Tuesday (Aug. 13), San Jose-based Velodyne said the two Chinese companies have “threatened Velodyne and its business” by copying its flagship technology: lidar configurations that allow vehicles to “see” their surroundings by bouncing lasers off objects.

The majority of self-driving companies rely on lidar: Silicon Valley’s Waymo, Chinese search giant Baidu, China’s state-owned electric vehicle maker BJEV, and autonomous driving startups Pony.ai and WeRide have all been using the technology. Usually mounted on top of the car, a lidar channel continuously sends out laser beams; as it senses the light bouncing back, it produces a 360-degree real-time map that the car uses to make decisions. Most serious players in the autonomous race see laser vision as indispensable. (Elon Musk, though, has argued that lidar isn’t necessary, and is instead banking on cameras.)

The units are incredibly expensive. A Velodyne 64-channel unit costs $85,000, more than a Tesla Model X in the US. And the more channels a lidar unit has, the more expensive it gets. A car that requires no human intervention at all—a level 5 car, at the top of the Society of Automotive Engineers’ self-driving classification scale—could require a 128-channel lidar system.

In its complaints, Velodyne said both Robosense and Hesai have been selling products that infringe on multiple aspects of US Patent No. 7,969,558 (“High Definition Lidar System“), which was awarded to Velodyne’s founder David Hall in 2011. Hall didn’t invent lidar itself, but he created the so-called “3-D point cloud” system, building upon lidar that used a single, fixed line of sight. Hall’s invention has made Velodyne’s lidar the standard for self-driving technology.

Velodyne said information it gathered has shown alleged infringement. It pointed out the similarities between its product structures and data capture technologies and those marketed by Robosense and Hesai. The complaints are identical. Here’s the one filed against Robosense:

On information and belief, Robosense copied Velodyne’s products, including the VLP-16, and learned of the ’558 patent no later than the time at which it first inspected and performed a tear-down of Velodyne’s products. And Robosense actively promotes the sale, use, and importation of its infringing rotating 3-D LiDAR devices in marketing materials, technical specifications, data sheets, web pages on its website, press releases, and user manuals, as well as at trade shows and through its sales and distribution channels that encourage infringing offers to sell, sales, and/or importation of the Accused Products. These actions collectively demonstrate that Robosense has had the specific intent to induce, or was willfully blind to inducing, infringement of the ’558 patent.

Lidar products offered by Robosense.

Velodyne has asked the court to stop both Hesai and Robosense from selling the alleged copied products, which include all the major lidar products listed on the Chinese companies’ sites. Neither Hesai nor Robosense responded to multiple calls and messages requesting comment. WeRide, a Guangzhou-based self-driving company which also has a Silicon Valley office, has been using both Velodyne and Hesai’s lidar, said it’s refraining from commenting on the complaints, but said its road tests aren’t affected.

It’s not the first time a Chinese company has been accused of stealing driverless IP from a US company. A former Tesla self-driving engineer admitted last month he had uploaded files containing Tesla’s Autopilot source code to iCloud while he was an employee there—right around the time he took an offer from five-year-old Chinese electric car maker Xiaopeng Motors. Xiaopeng said it wasn’t aware of the engineer’s alleged misconduct. Earlier, another engineer who took a post with Xiaopeng in its Mountain View office was charged by the FBI with stealing IP from Apple’s self-driving team.

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Tesla CEO Elon Musk Thinks LiDAR Technology is ‘Doomed,’ Experts Disagree

Startups working with the technology have raised $1.2 billion USD in the past five years, according to CB Insights. While lidar has other uses, such as …

Lidar is a radar-like technology that uses laser pulses to create a 3D-map of its environment, reads a new report from Ars Technica. Many see it as integral to the development of self-driving cars — but not Tesla CEO Elon Musk.

“Lidar is a fool’s errand,” Musk said in April at a Tesla event. “Anyone relying on lidar is doomed. Doomed.”

“Lidar is really a shortcut,” added Tesla AI guru Andrej Karpathy. “It sidesteps the fundamental problems of visual recognition that is necessary for autonomy. It gives a false sense of progress, and is ultimately a crutch.”

Lidar, which stands for light detection and ranging, sends out pulses that bounce off objects and return to the sensor, telling it how far away things are. Startups working with the technology have raised $1.2 billion USD in the past five years, according to CB Insights. While lidar has other uses, such as making topographical maps, most of the investing energy has surrounded autonomous driving.

Uber, Waymo, Cruise and several others use the technology in their self-driving technology stack. As proponents of the technology, they point to lidar’s ability to see through challenging weather and light conditions better than existing cameras.

Musk, on the other hand, believes that artificial intelligence-powered cameras will eventually become so good that they will render lidar technology obsolete.

Some industry insiders agree with Musk, but only to a point. Starsky Robotics, a self-driving truck startup, spurns lidar, believing it isn’t needed. Starsky co-founder Kartik Tiwari recently wrote that lidar today lacks reliability, and has insufficient range for the distance a loaded tractor-trailer needs to see to stop on highways.

Eric Meyhofer, head of Uber’s advanced technology division, doesn’t expect lidar will be needed in five years, given how good cameras and radar will get. But for now Uber continues to use lidar for its self-driving cars.

“We’re going to develop lidar until we don’t need to,” Meyhofer said. “The problem is easier to solve with lidar. It lets us do things sooner.”

Read the entire report on lidar technology over at Ars Technica.

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