NIVIDA is the leader in Deep Learning and GPU. For the past few years, it is gaining in
market momentum and stock value (Investments from Sunsoft is another proof). Its
Deep Learning technology not only drives cars; it helps self-driving cars to improve
skills overtime. RTC Magazine’s Editor-in-Chief, John Koon caught up with Danny
Shapiro, Senior Director of Automotive to gain his latest insight is future cars.
by John Koon, Editor-in-Chief
1. What is your vision of future self-driving cars? How
important is machine vision in making self-driving cars
Self-driving cars will have an incredibly positive effect
on society access to transportation will be transformed.
Autonomous cars will not only redefine the way people
commute, giving them hours back each day, but will
change how goods are transported.
We believe machine vision plays a role to make cars
commercially feasible, but it is not the sole answer to an
autonomous future. Just like we use our five senses to navigate
the world around us, we believe for cars to better pilot
themselves, they should also include other sensors such as
radar, lidar, ultra-sonic, and HD maps to further augment
the execution of an aware SDC.
Fundamentally AI is essential to be able to take data
coming from these sensors, and be able to interpret it.
There is no way that computer vision algorithms can be
programmed to account for the near infinite number of
scenarios that happen on our roads. But with deep learning,
autonomous vehicles can be trained to drive better
2. What hurdles need to be overcome before fully autonomous
vehicles can be achieved? Do you think the 2020
goals are achievable?
Currently the biggest hurdle autonomous technology
companies are facing is the legislative red tape. State and
federal regulators are having a hard time keeping up with
the cadence of these new technologies. However, just in the
last year alone, there have been leaps and bounds in improvements
in coming up with a streamlined plan for the rollout
of self-driving cars. NVIDIA recently testified in front of the
U.S. Senate Committee on Commerce, Science, and Transportation
for the need to implement AI in self-driving cars, and
provided guidance on rule making to ensure safe deployment
of this vital technology on our roads.
Is 2020 achievable? Yes absolutely. NVIDIA is developing
systems to bring fully autonomous cars by 2020 that
will be able to operate in specific environments. OEMs
such as Audi announced this year that by 2020 they will
have level 4 capable vehicles powered by NVIDIA ready for
3. In your opinion, what technologies will be used in
self-driving vehicles? Examples include: radar, machine
vision, deep learning/artificial intelligence, smart sensor,
IoT and big data analytics. How does vehicle-to-vehicle
technology fit in? What is missing?
Everything you mentioned will all play a vital role in
the rollout of autonomous vehicles. But we believe what
plays one of the biggest roles is deep learning. Through
deep learning, the entire suite of sensors will be able to
have a much greater understanding of what is happening
at any given moment. Deep learning also plays a major
role in big data analytics. Information these vehicles are
generating along with smart city information can be used
improve traffic flow.
V2V technology is a nice to have capability in a car,
but it is not essential. A vehicle must be able to navigate
autonomously even before V2V communication is established.
Similarly, connectivity to the cloud cannot be a
requirement for self-driving. All processing for autonomy
must take place on board the vehicle – hence the need for
an energy efficient supercomputer, design for sensor fusion
and deep learning.
4. How important is the infrastructure such as smart
freeway to the success self-driving cars?
Vehicle-to-infrastructure (V2I) will further augment
the driving experience, however given there are no
standard implementations, or widespread adoption; this
is not a useful solution in the short or even medium term.
Self-driving cars need to be self-contained. With a programmable
and updateable platform on board, software
updates can leverage V2I and V2V data when it is available.
5. What contribution does your company make to the
field of self-driving cars?
While sensors play a vital role in the operation of autonomous
cars a powerful computing platform needs to be able to
make sense of the information these sensors are generating.
The NVIDIA DRIVE PX car computing platform is designed
to handle the entire driving pipeline including sensing, localization
and path planning. The platform is designed for deep
learning inferencing and is capable of performing 30 trillion
operations per second while only consuming 30 watts. In
addition, NVIDIA also developed a complete, open software
development stack for companies to use when developing
their autonomous cars, shuttles, large trucks, and more.
Currently over 225 OEMs, Tier 1s, start-ups, HD mapping
companies, and research institutions are currently using our
solutions for an autonomous future.
Santa Clara, CA
Danny Shapiro is Senior Director of Automotive at NVIDIA, focusing
on artificial intelligence (AI) solutions self-driving cars, trucks and
shuttles. The NVIDIA automotive team is engaged with over 225 car
and truck makers, tier 1 suppliers, HD mapping companies, sensor
companies and startup companies that are all using the company’s
DRIVE PX hardware and software platform for autonomous vehicle
development and deployment. Danny serves on the advisory boards of
the Los Angeles Auto Show, the Connected Car Council and the NVIDIA
Foundation, which focuses on computational solutions for cancer
research. He holds a Bachelor of Science in electrical engineering and
computer science from Princeton University and an MBA from the Haas
School of Business at UC Berkeley.