i don't think anything says we're living in the future quite like the
proliferation of self-driving cars now granted they're not exactly all over the
roads yet but given the huge investments that automakers and tech companies are
making in them it seems like it's a matter of when rather than if
but how exactly do they work i mean most
of our trains aren't even self-driving and those things are quite literally on
rails well as you'd expect self-driving cars
are jam-packed with tons of equipment to help them both see and understand their
environment some of it is rather familiar such as a data connection for
traffic info and a gps transponder to allow the car to know where it is but
while gps is fine for providing driving directions in a human-controlled car it
has a margin of error of up to several meters and besides to control a car with
such a slow and error prone system would result in more than a few fender benders
so to start with self-driving cars also use freaking laser beams to build a map
of what's around them lidar which you can learn more about up
here can be built into units that can spin
360 degrees shooting invisible lasers in
all directions then measuring how long it takes for each beam to hit an object
and bounce back this allows the car to judge not only the distances but even
the shapes of the objects immediately around it giving the car even more
information is traditional radar to help it gauge speed gyroscopes and
accelerometers to provide more movement data than would be possible with a
traditional speedometer and high resolution cameras now you might think
well we've already got frickin laser beams
what are the cameras for well although the car's other systems give it a pretty
good idea of what's in the general vicinity the cameras really help provide
a complete picture as they can see in color this helps your car distinguish
let's say a caution sign from a construction sign so you put all of this
together and the car has a coherent 3d
map that provides the data that it needs to make decisions for example lidar and
video can determine whether that thing up ahead is telling the car to stop or
yield whether the vehicle in front is a small sports car or a large truck so that it
can decide whether to pass or whether that two-wheeled contraption is a
motorcycle or a bicycle so the car can get around the cyclist and give her a
bit more space also many self-driving cars have
ultrasonic sensors in the wheels so the car will know how close it is to the
curb and other vehicles while parking and even microphones so that they can
hear a police or ems siren to get out of the way in a timely manner i mean that's
going to be one improvement on the roads around here once self-driving cars are ubiquitous people can be so rude they're
going to an emergency anyway that's how all the data gets collected
but what about the processing an experienced driver can take in
information from the environment filter out everything that's not important and
make all the kinds of decisions we talked about before in a fraction of a
second so of course then you need a lot of
computing power for a self-driving car to pull off the same thing this is made
possible by running many processors in
parallel to crunch these numbers it's actually quite similar to how desktop
gpus work these types of processors also happen to
be more trainable with machine learning so for example you can teach a car what
a pedestrian looks like by showing it a large data set with lots of photos of
people crossing the road but what if a self-driving car didn't only have to
rely on its own sensors and processors well another goal is to have these cars
communicate with each other while they're on the road so they can
actually tell other cars what they're doing and why enhancing safety and
taking some of the guesswork away from the individual vehicles and if you think
about it having many cars working together like this in unison could help
with lots of other issues too like easing congestion by coordinating
movement so that traffic can keep flowing smoothly
also in the future especially once faster 5g connections become more common
we could even see smart infrastructure that communicates with the cars i mean
think about a parking garage that could tell a car that it's too full to
accommodate it or transponders in construction zones that could tell them
to slow down and prepare for narrower lanes of course we do still have a long
way to go before this kind of tech is commonplace but given how many
semi-autonomous cars are available for purchase today and how much money is
being poured into this whole endeavor along with the ever-rising processing
and data speeds we continue to enjoy the hope
is that in the near future our car trips will be much easier and safer since well
over 90 percent of car accidents are attributable to human error and speaking
of self-driving cars today's episode was brought to you by ibm spectrum storage
did you know that a self-driving car can generate up to 15 terabytes of data
every hour and lots of this is actually not even used while they're driving so
it's really common in the ai industry to have to ingest just tons of this raw
data and transform it after the fact while the car's not even driving into
intelligence and ibm is using spectrum storage to help automakers manage all
this data so it's optimized for ai and machine
learning with industry-leading GPU accelerated servers and ibm spectrum
scale software-defined storage combined with groundbreaking performance and
simplified deployment so get the fastlane to insights into
whatever field you're in by learning more about data storage for ai and ibm
storage solutions for autonomous driving at the link below so thanks for watching
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