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AIDriving COO Justin Xu Hopes to Enhance Fleet Safety With Artificial Intelligence

According to the industry's general view, the way to autonomous driving is divided into four stages. Now, the automotive industry is experiencing the transition from ADAS to semi-automated driving. There are many automakers, ADAS & autonomous driving startups, and investors are trying to seize their own opportunities.

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The chief operation officer of AIDriving, Mr. Justin Xu, is very glad to introduce their ADAS device to us. “AIDriving C7 for fleet safety is an industry-wide innovation with dual-camera design,” he said, ”one for forward collision avoidance and another for driver fatigue monitor.” The forward facing ADAS camera can recognize vehicle, pedestrian, cyclist, motorcycle driver and lane ahead. Meanwhile, the driver facing camera can recognize driver eye closed, yawn, inattention, phone using, smoking and driver absence.

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Data shows that about 1.25 million people die each year as a result of road traffic accidents, which means 1 person is killed every 25 seconds. Traffic accidents not only kill and hurt people, but also affect the work-related fleets and insurance companies. Even though fleets have been equipped with a variety of Telematics hardware, there still are some problems faced by fleet owners.

Justin said, “Humans are unintentionally make mistakes, hence we’re training our artificial intelligence model to help drivers reduce the risk of collisions and save lives.” Based on artificial vision & deep learning technologies, AIDriving’s ADAS based fleet safety solution can enhance the fleet safety and provide totally a more secure driving environment.

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As a leading global ADAS solutions provider, AIDriving is also the first ADAS startup in China(founded in 2011). The founder Mr. Xiaoguang Zhang has in the past worked for Shenzhen Institutes of Advanced Technology, Chinese Academy of Science, with abundant experience in vehicle active safety system. In 2013, AIDriving released smartphone based ADAS APP and won 2 very important competitions: excellent player prize in Android World Global Developers Conference and runner-up in Hardeggs iFuture Hardware Competition. In 2014, AIDriving received around 5 million USD business financing then went to CES 2015 and released their first professional level ADAS device there. By the end of 2015, AIDriving acquired a hardware company.

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AIDriving is now focusing on auto OEM market and fleet market. Before autonomous driving vehicle becomes a reality, OEM market has more urgent and bigger demands for ADAS. With the combination of vehicle CANBUS data and braking system, a vehicle will be more intelligent, for example, Automatic Emergency Braking and Adaptive Cruise Control.

In the aftermarket, fleet and insurance industries are the target markets for Mobileye and other ADAS companies. Countries like USA, Chile and China have released mandatory laws which require specific vehicles under operation to install aftermarket ADAS system. In Israel and Mexico, vehicles with Mobileye collision avoidance system will benefit in the form of insurance reduction.

Recently, the Ministry of Transport of the People’s Republic of China has issued an announcement which makes it mandatory for all vehicles under operation to install forward collision warning and lane departure warning systems.

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As a result, all Chinese ADAS companies are ushering in a big market opportunity. Among them, AIDriving ranks the highest ADAS accuracy in China. The market feedback of AIDriving is also positive. Latest news is that AIDriving will receive their A+ round business financing in May. Good luck!

 

 

 

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Microsoft has now created an A.I. that can write its own code

microsoft deep code


You know how teachers used to say, “You can’t cheat your way through life, so don’t cheat on this test.” They may have been right about the test...but not life.

Researchers at Microsoft and University of Cambridge have developed an Artificial Intelligence that’ll write code all by itself.

They call it DeepCoder

And it could change the job description for software developers.

The vision for DeepCoder is for a person to be able to merely give it an idea and the AI will automatically write all of the necessary code, without errors, in just seconds. More than anything, it will allow anybody with an idea to potentially build an internet business worth millions.

You’d think that DeepCoder would put a lot of programmers out of a job, but Armando Solar-Lezama, a professor at MIT, doesn’t think so. He believes this will enable programmers to attack more sophisticated problems, while the AI takes care of the tedious dirty-work.

Ironically, DeepCoder is a cheater itself

It works through a method called program synthesis, which essentially means stealing lines of code from other finished software. In the developer community, this is commonplace among the mid-to-lower level coders (script kiddies) because of the efficiency.

Currently, DeepCoder is capable of solving basic challenges one would see at programming competitions, nothing more than five lines of code at a time. But, it’s just starting out.

Its advantages are what sets it apart

Being that it is an AI, it has very little limitations to its work capabilities, allowing it to more swiftly and thoroughly scour source code databases, and put together programs in a way humans may not have thought of.

Most importantly, it has a great memory – reminding itself which code worked last time and which didn’t.

Honestly, why should I care

Aside from empowering the non-coder to build software, this signals the exciting times that are ahead for Artificial Intelligence and automation of white-collar jobs.

It can be fearful to think about a machine taking your job, but as long as you are aware of the possibility before it happens, you can prepare. Not to mention, AI will start by taking care of our dirty work.

For an accountant, AI will first learn to keep track of entries in the general ledger. For a marketer, AI will compile massive amounts of buying history and present you with a report on best marketing strategies. And for programmers, AI will write a lot of the time-consuming, simple code.

As long as you are open to adapting and aware of the changes, you won’t go extinct.

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Magna teams with Innoviz LiDAR for Autonomous driving system

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Global automaker supply leader Magna International is teaming up with LiDAR-maker Innoviz to help fill out its sensor fusion picture for self-driving vehicles, the companies announced Tuesday. The addition of Innoviz’s LiDAR sensor means Magna’s suite now includes carmakers, ultrasonic and RADAR fusion sensing capabilities, which essentially covers the range of available sensing techniques used in creating a three-dimensional, highly accurate and live image of the world surrounding an autonomous vehicle.

Innoviz will be showing off prototypes of upcoming LiDAR hardware it plans to bring to production at CES this year. The key to wider use of LiDAR in autonomous systems is reducing size and cost, and that’s definitely going to be true for Magna, which is going to be attempting to sell its driverless / self-driving systems as a Tier 1 supplier to automakers, providing them with everything they need to field production self-driving cars once the tech gets to where it needs to be, and regulation also catches up to the pace of industry progress.

Innoviz is pursuing solid-sate LiDAR tech, which will help reduce cost and size of components by minimizing the number of moving parts required in a LiDAR sensor’s construction. The InnovizOne it’s demoing at CES has a 200m detection range, and yet costs just $100 per unit, with a 5x5x5cm footprint and improved durability vs. previously LiDAR designs.

 

Magna, which is a key supplier to essentially ever major automaker in operation right now, has been investing heavily in autonomous driving technology, but has previously cautioned that it still believes true self-driving tech won’t be widely available to consumers for a long while yet, with cost accounting for part of that.

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Could Quanergys new $250 Solid State Lidar bring Self Driving to the Masses?

quanergy lidar
By Paul Godsmark

One of the big hurdles when equipping vehicles with sensors for autonomous driving is the cost. For example, the Light Detection and Ranging (LiDAR) sensors that power many versions of self-driving car technology are pricey, currently ranging from around several thousand dollars up to $85,000 per sensor—and vehicles often need multiple sensors to see enough of what is going on around them to drive safely.

But the latest entry into the LiDAR market could change all that.  Quanergy introduce the world's first affordable solid-state LiDAR, coming in at about $250.

LiDAR Sensors Are Key

Quanergy builds LiDAR systems, a remote sensing method that uses light in the form of a pulsed laser to measure ranges (variable distances) from the car to everything around it, enabling the car to track its environment better than other types of sensor.

Radar-based systems are really good at operating in all weather, as they see through anything obscuring vision. However, they lack resolution, and while they can tell you that something is there, they can't tell if that something is a car, a barrier, or even a wall.


Vision-based systems are great at determining shapes and colors, are essential for reading traffic signals, and allow rapid categorization of objects in the the field of view such as pedestrians and cyclists. But vision systems don't work so well at night in the dark, and they are not good at precisely tracking distances and velocities of moving objects.

This is the strength of the new higher resolution LiDAR, as they can literally provide a 3D view of the world as the infrared pulses bounce off of surrounding objects and return to the sensor with precise distance information. Because the pulses are so close, the speed at which objects are moving relative to the vehicle is easily calculated.


The $250 price point is a really big deal for the automated vehicle ecosystem, as the cost of the LiDAR technology specifically has been a major stumbling block to the deployment of driverless cars.


For comparison, the  that has been used by Google, Uber, Toyota, Bosch, etc. was often quoted at costing between $75,000-$85,000 only 6 years ago. More recently, LiDAR costs have tumbled to around $8,000 with the less capable model, of which is on their initial prototype vehicles.


Prices may have been inflated with Velodyne facing little in the way of competition for high-end automotive-grade LiDAR systems, and the imminent arrival of a $250 unit from Quanergy should drastically shake up the market. Suddenly, affordable ride-sharing driverless fleets for public use looks to be a far more realistic possibility.


'Production Lines Have Been Built'

"The new S3 solid state LiDAR meets all the  and we are already ramping up to mass produce them," Louay Eldada, founder of Quanergy, told Driverless in an exclusive interview. "Production lines have been built."


With volume production in the region of 100,000 units, the cost is expected to be $250 or less per sensor. For that, the customer will receive a solid-state LiDAR contained in a 9 x 6 x 6 centimeter housing that can detect objects as far as 150 meters away (at 80% reflectivity), and as close as 10 centimeters. The 120-degree field of view in both horizontal and vertical planes implies that multiple S3 units would be needed around the vehicle to provide the necessary sensor coverage for the safest possible driving. And if you're worried about the sensors being vulnerable to hacking—Eldada says they've engineered the sensor with seven layers of protection to make sure the system can't be tricked.


Importantly, because the S3 is a solid-state LiDAR, it isn't vulnerable to facing into the sun as other types of LiDAR are. Eldada explained that they have done testing, and unlike LiDAR that have physical mirrors that are always gathering light and therefore prone to being blinded, the solid-state optical phase array is like an electronic lens that is only turned on and gathering light waves precisely when it is needed.


Justifying 'Unicorn Status'

Back in August of last year, Quanergy joined the exclusive "unicorn-club" with a 90-Million Funding, valuing the startup in excess of a billion dollars. Eldada explained that this funding deal was done in June 2016, and that he doesn't currently foresee the need for any further funding rounds, although an IPO (an initial public offering, when a company raises funds by offering publicly traded shares) shouldn't be ruled out.


It is possible that the B funding round didn't make waves because the company promised much last year at CES 2016, but did not subsequently publicly demonstrate their solid-state technology in a way that satisfied the skeptics. This latest news appears to build a more solid foundation for the future, justifying the faith of the early Quanergy investors in what appears to be a game changing technology for affordable self-driving vehicles.


This is a very big deal for far more than just the automotive sector. LiDAR is used in many different sectors; Eldada is expecting initial sales will likely go to mining, agriculture, and security companies. But mobility (in the form of driverless vehicles) is where the biggest bucks will eventually be made.


Growth from all areas where automated systems can provide safety and efficiency benefits will likely lead to a multibillion dollar market. According to Quanergy, they expect the LiDAR market to exceed $1 billion by 2020 and $3 billion by 2022.


Driverless Breakthroughs Expected in 2018

Even though the new vehicle cycle time for major automakers (the time to design, prototype, and manufacture new models) has been steadily falling over the last decade from around 5 years to closer to 3 years, Eldada will not be surprised if it only takes another year before a major increase in demand for LiDAR from the automotive sector.

Eldada thinks we are likely to see some form of fully automated vehicles operating on public roads, even if constrained by speed or geofencing, in 2018 (not 2017).

Some automated vehicle developers are trying to achieve 2017, but 2018 is more likely to be the breakthrough year as even with the shorter cycle times it takes time to manufacture the vehicles and get them through the development, production and sales cycle. The newer, faster moving, more aggressive automakers are putting pressure on the major automakers, but the volumes that they have are not the same. Quanergy will be active in this space and ready to support any automakers; and there will be announcements later this year.

If we do see some fully automated vehicles commercially deployed on public roads in the next couple of years, then there's a reasonable chance that Quanergy's affordable LiDAR technology will have played an important role in making it possible.

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Google’s self-driving car project is now a new company called Waymo
waymo logo

By Katie Burke

This new Google company led by CEO John Krafcik, exists under the Google parent company Alphabet and will operate like a “venture-backed startup,” Krafcik said at a press event today. Waymo will be based in Mountain View, Calif., and will be responsible for developing self-driving technology and will explore opportunities in trucking, logistics and automaker partnerships.

“We are a self-driving technology company with a mission to make it safe and easy for people to move around,” Krafcik said, emphasizing that Waymo is not a car company.
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Until now, the program has been part of secretive research unit Google X.
Waymo stands for "A new way forward in mobility," according to Krafcik.

The company’s first driverless car ride on public roads -- without a steering wheel or brake pedal -- happened in Austin, Texas, in October 2015, and 10,000 similar tests have since taken place.

"Waymo’s next step will be to let people use our vehicles to do everyday things like run errands, commute to work, or get safely home after a night on the town," the company said in a statement.

Krafcik added that the company is in “build phase” in its partnership with Fiat Chrysler Automobiles to develop 100 self-driving Chrysler Pacifica minivans, outfitting the vehicles with updated sensor systems.

Waymo’s autonomous system uses radar, camera and lidar sensors, and the company is developing primarily Level 4 and Level 5 technology. Nathaniel Fairfield, Waymo’s principal software engineer, said the sensors have been able to handle rough weather conditions.

The spinoff shows Alphabet believes there is a market for these cars, but it’s still uncertain whether consumers want self-driving technology.

“The question remains whether consumers are ready for this, since most prefer at least an option to take over the driving,” said Rebecca Lindland, senior analyst for Kelley Blue Book.

More engineers

Google has expanded its program over the past year, hiring more engineers while doubling its testing centers from two U.S. cities to four.

Although there have been some significant departures over the past year -- Chief Technical Officer Chris Urmson left in August after leading the project from its inception -- some new hires have pointed to the program's readiness to move past its experimental stage.

In July, the project appointed its first general counsel and a month later it hired former Airbnb executive Shaun Stewart as director of the project, with a mandate to commercialize the company's self-driving technology.

Krafcik, 55, the former Hyundai Motor America CEO and longtime Ford executive, joined Google in September 2015.

Reuters also contributed to this report.

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