Intel Editorial: For Self-Driving Cars, There’s Big Meaning Behind One Big Number: 4 Terabytes
April 14 2017 - 12:16PM
Business Wire
If 3,000 People Talked All at Once, Could You Understand What
Each One Was Saying?
As an engineer, I love solving problems and using the “language
of math” – or numbers – to understand the world we live in. With
meaning beyond their stated numerical value, numbers add context to
stories and challenges in a way that words alone cannot. Big
numbers are interesting as their meaning is often much more complex
than their sheer size might suggest. With one number in particular
– 4 terabytes (TB) – this is especially true, and I’m excited about
the meaning behind that number for the autonomous driving
industry.
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Kathy Winter is vice president and
general manager of the Automated Driving Division at Intel
Corporation. (Photo: Intel Corporation)
First things first: Why that number? Four terabytes is the
estimated amount of data that an autonomous car will generate in
about an hour and a half of driving – or the amount of time a
typical person spends in their car each day. By 2020, that’s also
the amount of data that 3,000 individual internet users are
expected to put out each and every day. It might not sound like
much until you think of it in a different way: How many of us have
3,000 friends on Facebook? Now imagine trying to follow and absorb
everything they all post each and every single day.
If the interesting thing about the data created by a
self-driving car was simply the amount of it, 4TB wouldn’t be very
exciting. What makes “data the new oil” for autonomous driving –
and what makes it a real challenge – is our need to make sense of
that data, to turn it into actionable insight that lets cars think,
learn and act without human intervention. Data that lets cars do
the driving so that the 90 percent of the accidents caused by human
error1 may one day be a thing of the past.
Press Kit: Autonomous Driving at Intel
Intel is a data company. We know how to create, move, store,
process, analyze and manage data – at massive scale – and we’re
applying this vast expertise to the autonomous driving industry.
From experience, we also know the fastest way to solve the
autonomous driving data challenge is through industry
collaboration. While there’s a lot of work to do to deliver fully
autonomous vehicles by 2021, I am confident that by working with
the industry and our partners, together we can get it done.
Autonomous driving data comes in three basic types: technical
data, crowdsourced data and personal data.
Technical data is perhaps most obvious. This data comes from a
suite of sensors and is the car’s “view” of the world immediately
around it. This data helps the car recognize a person or fire
hydrant, “see” a new pothole, or maybe calculate how quickly a
nearby car is approaching. This kind of technical data is also
great for capturing new driving scenarios and pushing it to the
cloud for learning and improving the software that controls driving
behavior. When this kind of data goes to the cloud, it becomes
incredibly valuable to other vehicles connected with that same
cloud.
Crowdsourced data is something that a community of local cars
takes in from their surroundings, such as traffic or changes to the
road conditions. You can imagine all kinds of cool applications
that could use this kind of information, such as finding a nearby
parking spot or avoiding traffic jams.
Finally, there is personal data, including the radio stations
you like to listen to, coffee shops you frequent, routes you prefer
and so on. This type of data could be useful in creating the most
amazing personalized experience in your autonomous vehicle.
As the industry moves toward fully autonomous cars, data
presents a number of challenges for the entire global industry. The
first challenge goes back to that original number: 4TB. The
exponentially growing size of the data sets necessitates an
enormous amount of compute capacity to organize, process, analyze,
understand, share and store. Think data center server compute
power, not PC power.
The need to train autonomous vehicles as quickly as possible
presents another challenge. When new driving responses or
situations are identified, machine learning, simulation and
algorithm improvements must happen almost instantly – not weeks or
months later – and updated driving models must be pushed to the
cars immediately once available. When, where and how that happens
has implications not just for today, but for the day when
self-driving cars are the norm.
There’s also the matter of data protection and what that means
for consumers to eventually trust the autonomous experience. How we
will achieve truly secure storage and sharing of data is a question
I am asked about frequently and one we take very seriously. Which
data gets stored? Which gets tossed? Which data sets get shared?
And how will we protect it all? These are valid questions that will
require industry collaboration and our best experts to address in a
meaningful way.
Finally, the data challenge grows over time as small fleets of
vehicles eventually become hundreds of millions of vehicles. The
ability to make this happen comes only through the ability to
process increasingly larger data sets. True system scalability will
be critical both inside our cars – back to that 4TB number – and
outside our cars in massive data centers, as the self-driving
supercomputer and the cloud that supports it continue to
evolve.
No one company can tackle these data challenges on its own. At
Intel, we believe the best way to solve the autonomous driving data
challenge is to do it collectively, to work together across the
industry to develop secure state-of-the-art platforms and to share
safety-related information. As we work toward a shared vision of a
world without accidents and with mobility for all, industry
collaboration will accelerate our ability to deliver. I am thrilled
to be working with our Intel team and key partners on the 4TB
challenge, as I know that solving this problem will lead to safer
roads and a better journey for all.
Kathy Winter is vice president and general manager of the
Automated Driving Solutions Division at Intel Corporation. She
joined Intel in 2016 from Delphi, where she engineered the first
cross-country drive of a fully autonomous vehicle.
This is the third in an occasional series of Intel newsroom
editorials related to autonomous driving. To comment or reach Kathy
directly, email autonomousdriving@intel.com.
1 National Motor Vehicle Crash Causation Survey, U.S. Department
of Transportation, at 25 (2008),
https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/811059;
http://cyberlaw.stanford.edu/blog/2013/12/human-error-cause-vehicle-crashes
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Intel CorporationKathy Winterautonomousdriving@intel.com
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