INSUBCONTINENT EXCLUSIVE:
Kevin Guo
Contributor
Kevin Guo is the CEO and co-founder of Hive.
For most Americans, the thought of
carsautonomously navigating our streets still feels like a science fiction story
Despite thebillions of dollarsinvested into the industry in recent years, no self-driving car company has proven that its technology is
capable of producing mass-market autonomous vehicles in even the near-distant future.
In fact, a recent IIHS investigationidentified
significant flaws in assisted driving technologyand concluded that in all likelihood &autonomous vehicle[s] that can go anywhere, anytime&
will not be market-ready for &quite some time.& The complexity of the problem has even led Uber topotentially spin off their autonomous car
unitas a means of soliciting minority investments — in short, the cost of solving this problem is time and billions (if not trillions) of
dollars.
Current shortcomings aside, there is a legitimate need for self-driving technology: every year, nearly 1.3 million people die and 2
million people are injured in car crashes
alone, 40,000 peopledied last yeardue to car accidents, putting car accident-based deaths in thetop 15 leading causesof death in America
GM has determined that the major cause for94 percentof those car crashes is human error.Independent studieshave verified that technological
advances such as ridesharing have reduced automotive accidents by removing from our streets drivers who should not be operating
vehicles.
The challenge of developing self-driving technology is rooted in replicating the incredibly nuanced cognitive decisions we
make every time we get behind the wheel.
We should have every reason to believe that autonomous driving systems — determinant
and finely tuned computers always operating at peak performance — will all but eliminate on-road fatalities
The challenge of developing self-driving technology is rooted in replicating the incredibly nuanced cognitive decisions we make every time
we get behind the wheel.
Anyone with experience in the artificial intelligence space will tell you that quality and quantity of training
data is one of the most important inputs in building real-world-functional AI
This is why today&slarge technology companies continue to collect and keep detailed consumer data, despite recent public backlash
From search engines, to social media, to self driving cars, data — in some cases even more than the underlying technology itself — is
what drives value in today technology companies
It should be no surprise then thatautonomous vehicle companies do not publicly share data, even in instances of deadly crashes
When it comes to autonomous vehicles, the public interest (making safe self-driving cars available as soon as possible) is clearly at odds
with corporate interests (making as much money as possible on the technology).
We need to create industry and regulatory environments in
which autonomous vehicle companies compete based upon the quality of their technology — not just upon their ability to spend hundreds of
millions of dollars to collect and silo as much data as possible (yes, this is how much gathering this data costs)
In today environment the inverse is true: autonomous car manufacturers are focusing on are gathering as many miles of data as possible, with
the intention of feeding more information into their models than their competitors, all the while avoiding working together.
The
competition generated from a level data playing field could create tens of thousands of new high-tech jobs.
The siloed petabytes
(and soon exabytes) of road data that these companies hoard should be, without giving away trade secrets or information about their models,
pooled into a nonprofit consortium, perhaps even a government entity, where every mile driven is shared and audited for quality
By all means, take this data to your private company and consume it, make your models smarter and then provide more road data to the pool to
make everyone smarter — and more importantly, increase the pace at which we have truly autonomous vehicles on the road, and their safety
once they&re there.
The complexity of this data is diverse, yet public — I am not suggesting that people hand over private, privileged
data, but actively pool and combine what the cars are seeing
There a reason that many of the autonomous car companiesare driving millions of virtual miles— they&re attempting to get as much active
Beyond the fact that they drove those miles, what truly makes that data something that they have to hoard By sharing these miles, by seeing
as much of the world in as much detail as possible, these companies can focus on making smarter, better autonomous vehicles and bring them
to market faster.
If you&re reading this and thinking it deeply unfair, I encourage you to once again consider 40,000 people are preventably
dying every year in America alone
If you are not compelled by the massive life-saving potential of the technology, consider that publicly licenseable self-driving data sets
would accelerate innovation by removing a substantial portion of the capital barrier-to-entry in the space and increasing
competition.
Though big technology and automotive companies may scoff at the idea of sharing their data, the competition generated from a
level data playing field could create tens of thousands of new high-tech jobs
Any government dollar spent on aggregating road data would be considered capitalized as opposed to lost — public data sets can be reused
by researchers for AI and cross-disciplinary projects for many years to come.
The most ethical (and most economically sensible) choice is
that all data generated by autonomous vehicle companies should be part of a contiguous system built to make for a smarter, safer humanity
We can&t afford to wait any longer.
