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 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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 <img xmlns="http://www.w3.org/1999/xhtml" alt="Kathy Winter is vice president and general manager of the Automated Driving Division at Intel Corpor ... " src="http://mms.businesswire.com/media/20170414005225/en/580889/4/Kathy-Winter.jpg"></img>
 Kathy Winter is vice president and general manager of the Automated Driving Division at Intel Corporation. (Photo: Intel Corporation) 
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 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 <a xmlns="http://www.w3.org/1999/xhtml" href="http://cts.businesswire.com/ct/CT?id=smartlink&amp;url=https%3A%2F%2Fnewsroom.intel.com%2Feditorials%2Fkrzanich-the-future-of-automated-driving%2F&amp;esheet=51541311&amp;newsitemid=20170414005225&amp;lan=en-US&amp;anchor=%E2%80%9Cdata+the+new+oil%E2%80%9D&amp;index=1&amp;md5=5e595e1565b169ba09d4c79555ebca43" rel="nofollow" rel="nofollow">"data 
 the new oil"</a> 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 <a xmlns="http://www.w3.org/1999/xhtml" href="http://cts.businesswire.com/ct/CT?id=smartlink&amp;url=https%3A%2F%2Fcrashstats.nhtsa.dot.gov%2FApi%2FPublic%2FViewPublication%2F811059&amp;esheet=51541311&amp;newsitemid=20170414005225&amp;lan=en-US&amp;anchor=90+percent+of+the+accidents+caused+by+human+error&amp;index=2&amp;md5=93df6ea63ec053a4e602323fbb882902" rel="nofollow" rel="nofollow">90 
 percent of the accidents caused by human error</a>1 may one 
 day be a thing of the past.
 
 
 Press Kit: <a xmlns="http://www.w3.org/1999/xhtml" href="http://cts.businesswire.com/ct/CT?id=smartlink&amp;url=https%3A%2F%2Fnewsroom.intel.com%2Fpress-kits%2Fautonomous-driving-intel%2F&amp;esheet=51541311&amp;newsitemid=20170414005225&amp;lan=en-US&amp;anchor=Autonomous+Driving+at+Intel&amp;index=3&amp;md5=256bd80dc73c30fd29f55aaa9f9b5ed6" rel="nofollow" rel="nofollow">Autonomous 
 Driving at Intel</a>
 
 
 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 <a xmlns="http://www.w3.org/1999/xhtml" href="http://cts.businesswire.com/ct/CT?id=smartlink&amp;url=https%3A%2F%2Fnewsroom.intel.com%2Ftag%2Fkathy-winter-editorials%2F&amp;esheet=51541311&amp;newsitemid=20170414005225&amp;lan=en-US&amp;anchor=an+occasional+series+of+Intel+newsroom+editorials+related+to+autonomous+driving&amp;index=4&amp;md5=01a2c9a949ea7face893597411d49319" rel="nofollow" rel="nofollow">an 
 occasional series of Intel newsroom editorials related to autonomous 
 driving</a>. To comment or reach Kathy directly, email <a xmlns="http://www.w3.org/1999/xhtml" href="mailto:autonomousdriving@intel.com" rel="nofollow" rel="nofollow">autonomousdriving@intel.com</a>.
 
 
 1 National Motor Vehicle Crash Causation Survey, U.S. 
 Department of Transportation, at 25 (2008), <a xmlns="http://www.w3.org/1999/xhtml" href="http://cts.businesswire.com/ct/CT?id=smartlink&amp;url=https%3A%2F%2Fcrashstats.nhtsa.dot.gov%2FApi%2FPublic%2FViewPublication%2F811059&amp;esheet=51541311&amp;newsitemid=20170414005225&amp;lan=en-US&amp;anchor=https%3A%2F%2Fcrashstats.nhtsa.dot.gov%2FApi%2FPublic%2FViewPublication%2F811059&amp;index=5&amp;md5=14cafb262b9430a7858d0afe590aab7f" rel="nofollow" rel="nofollow">https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/811059</a>; 
 <a xmlns="http://www.w3.org/1999/xhtml" href="http://cts.businesswire.com/ct/CT?id=smartlink&amp;url=http%3A%2F%2Fcyberlaw.stanford.edu%2Fblog%2F2013%2F12%2Fhuman-error-cause-vehicle-crashes&amp;esheet=51541311&amp;newsitemid=20170414005225&amp;lan=en-US&amp;anchor=http%3A%2F%2Fcyberlaw.stanford.edu%2Fblog%2F2013%2F12%2Fhuman-error-cause-vehicle-crashes&amp;index=6&amp;md5=915b975f63cc47d1efaab1176ef2377f" rel="nofollow" rel="nofollow">http://cyberlaw.stanford.edu/blog/2013/12/human-error-cause-vehicle-crashes</a>
 
 
 
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