Nvidia doesn’t always announce new consumer graphics cards
at its annual technology conference, but it was widely expected to this
year. Instead, GTC 2016 is all about AI, VR, and especially self-driving
cars. Following up on its announcement of the Drive PX 2 car computer,
Nvidia updated its plans to ship a complete set of developer tools —
fueled by its own autonomous vehicle research — for car makers, and to
sponsor and help equip a robot car racing league.
DriveWorks is the power behind the Drive PX 2
A supercomputer in your trunk,
like Nvidia’s Drive PX 2, isn’t much good without the software to run
it. That’s where Nvidia’s DriveWorks platform comes in. First announced
at CES, it is getting closer to reality with a “Spring 2016” ship date.
Nvidia CEO Jen-Hsun Huang also used his keynote to go into more detail
about what it will include. The developer platform starts with sensor
fusion and computer vision software that can work with up to 12 cameras
and other sensors to provide a comprehensive model of the vehicle’s
environment. From there, advanced machine learning capability will
assist with navigation, vehicle control, and path planning.
High-quality
maps, like those from HERE, are also going to be supported. One
interesting feature is support for map creation using the DriveWorks
in-car platform coupled with cloud-based processing for the actual map
creation. It was a little unclear from Huang’s description exactly how
all this would work — except that he hopes and expects that that cloud
will be populated with Nvidia’s new $130K DGX-1 supercomputer
— but what is clear is that he sees this technology greatly reducing
the cost of mapping areas, and of training autonomous vehicles. In
particular, it should make it possible to do a better job of keeping
maps up to date. Instead of needing routes to be re-driven with
expensive, specialized, vehicles to pick up changes in the road layout
or obstacles, data from “regular” Drive PX 2-equipped cars could be
used.Self-driving with DAVENET, or “I can do that, Dave”
Rounding out Nvidia’s DriveWorks offering will be a deep
neural network (DNN) that has been trained to know how to drive.
Traditionally, autonomous vehicles, such as the ones used in the DARPA
challenge, have relied on manually-coded algorithms to follow a desired
route, and provide vehicle control. Nvidia (along with many other
current vehicle research teams) has been experimenting with using deep
learning neural networks instead. According to Huang (and illustrated
with a demo video), after only 3,000 miles of supervised driving, its
car — powered by its DAVENET (formerly named DRIVENET) neural network —
was able to navigate on freeways, country roads, gravel driveways, and
in the rain.
Of
course, what he showed was only a demo video. But all in all, it was
quite a remarkable achievement when contrasted with the hundreds of man
years of coding that went into the much-less-sophisticated driving of
the DARPA challenge cars only 10 years ago. Obviously, Nvidia isn’t
suddenly planning to become a car company, but it will be providing its
technology as part of the set of tools for the auto industry to use to
take advantage of its Drive PX 2. Huang showed, for example, how the PX
2’s ability to process 12 cameras at once not only assists driving
safely through traffic and obstacles, but builds a sufficient model of
the world around it to allow for adjusting to road conditions and
routing.Roborace: Full-size robotic car racing
For decades, car and auto accessory manufacturers have used
racing as both an advertising tool and a way to advance their own
research and development. Whether it is F1, IndyCar, or NASCAR, factory
teams are ever present and always using what they learn to help them
with their next generation of street vehicles. Now that autonomous
operation is an increasingly realistic future path for road cars,
bringing computing front and center in auto development, it makes sense
racing should become a platform for AI-based vehicle R&D.
That’s exactly what Nvidia and others are planning for the
newly announced Roborace league. Piggybacking off the fast-growing
Formula E (all Electric) schedule and car design, the league will
feature 20 identical Roborace cars allocated to 10 teams. They will race
on the same courses as Formula E, except without drivers. The cars
won’t be remote-controlled, either. They’ll be fully autonomous, using
an Nvidia Drive PX 2 portable supercomputer to run their software. So
the teams’ innovation and differentiation will be in the software they
develop for the race. The Roborace is scheduled to start alongside the
2016-2017 Formula E season, later this year. Roborace founder Dennis
Sverdlov told GTC attendees he expected it to make heroes out of
software developers: “It’s not possible to get competitive advantage
based on how much money you put in hardware. Our heroes are not the
drivers. Our heroes are engineers.”
Jealous? You too can build a (small) self-driving car!
Along
with each new autonomous vehicle announcement, there is always a
statement of the massive investment needed to make it happen. But for
those of us who want to do more than be passive spectators, there is an
exciting new opportunity to learn how to build your own — scaled-down —
robotic race car. Startup JetsonHacks has taken MIT’s RACECAR autonomous
car learning platform and made it accessible to the DIY community with detailed assembly instructions,
and cost-saving hardware options to make it more affordable than the
University’s original version. The RACECAR is a massive kit bash of an
offf-the-shelf RC vehicle — a Traxxas Rally — so that all the DIY fun is
concentrated on the control and programming. The brain is (naturally) a
Jetson TK1, running Robot OS (ROS).
In an exclusive interview, JetsonHacks Founder Bill Jenson
excitedly explained that this year will feature an upgraded model based
on this Spring’s MIT Controls Course — which will be available online —
and a new design featuring a more-powerful Jetson TX1. If you’d rather
flex your maker muscle with a drone, he also offers a lot of great DIY
drone advice based on the DJI Matrice 100 development platform.
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