everybody's doing from tested and I am in this futuristic office park that also
turns out to be the headquarters for Amazon and in one of these office
buildings here is the headquarters for AWS deep racer that's the platform for
their autonomous race car system that they have developed to teach developers
about machine learning and reinforcement learning and they just announced a new
deep racer Evo system as well as upgrades to the software platform I'm
gonna chance to check both loads out right now let's go take a look
all right so I'm really excited to be checking out the appraiser here with
sahih casino replied scientists on the project this is the racer tell me about
how this was designed oh it was designed as part of a process that we would like
to teach machine learning to people out there who are interested I would like to
really put that hands-on experience for them what we were really trying to
figure out there was how we can create an educational content around it and
that we can apply it hands-on using a vehicle so Michael was the essential
core for us to make the connection between what you're coding versus what
you're observing it's a totally a hands-on product that really physical
eise's all that modeling that can be done with machine learning so not only
is it of course it's for a car you've designed the idea to be a race right
this needs to strive autonomously on a track tell me about the kind of the
parameters of that how is this what are what are your users configuring here
what input you get from the sensors and what are they tweaking to have it run
autonomously so in the in the console there are many things that you can
adjust and one of the things that we were hearing from academia and from our
developers along was be careful what you wish for
so it were not important and most critical parts is the reward function so
users today can change the reward function and they can get parameters
from the simulation such as the speed the steering and is a turn coming up and
also things like am I going I'm not staying within the track within
the white lines am i steering off to the left of the yellow line and things like
that so all those are exposed and then you can design your own reward function
with let's not win a mine that's good right right the idea is that there's not
only you have one camera here there's computer vision so it's seeing you know
analyzing the scene on a frame by frame basis but was also it's lemon tree are
you talking its own speed its own where the wheels are turned
and that's all simulated in virtual environment
exactly and the other key component of its value were moving to the device was
out to have the inference fast enough so this is where the racing comes into the
picture and this is what we see in the industry people working in the field are
really interested in making the machine learning work in the real world really
requires you to move fast right so one thing that we did was we use an
optimizer so around the neural network is making inferences frame by frame it's
actually putting in an optimizer on the neural network to make those decisions
fast so that it can actually act as quickly as possible we use Intel open we
know to do that can we take a look under the hood here sure yeah see how this
whole thing the whole thing is designed and obviously it's very inspired by just
a scale model car you have wheels but it's really a computer system yeah
exactly so the actually what you see here is that the top of the car is our
computer environment but the bottom here is very low cost
we actually experimented with different scales and what we really found out is
that we want to go smaller for the racing experience but we also would like
to give the opportunity for people to try different tracks that's why we have
rubber so you can use carpet you can use floor so if you're racing your opponents
in the office that it's as ready to go and as versatile as you can get it
there's so many variables in the physics of the real world yeah the friction of
the track the reflectivity of the material all that needs to be kind of
accounted for yeah it's actually funny that you
mentioned that even the springs that you will see okay
yeah the suspension so in our earlier models one of our cars came with a
different suspension and we were testing and the car starts bobbing and I'm like
this car is so excited to be on the track no it was it just hit the wrong
springs and ami we're thinking why it's not transferring properly so you really
need to get a high fidelity simulation environment so we've currently modelled
the steering so you would see that in our console they exposed her to degrees
as max because what the vehicle can do I can
also take this off and I can show you yeah let's look at computer system
because see heat sink there it's running computer obviously processing the
visuals from the single camera system you get a live view as well on your
control panel but you're only adjusting speed there this is like truly as
autonomous as can be yes so it's actually a learning both steering and
speed so we wanted to give the opportunity for the racers to fine tune
their cars so they can calibrate it the way they wanted based on their model and
they can calibrate it such that they can decide to go a little slower on the
curves and that's a big difference between that personal environment in the
real world hey grab every car through many laps will change there'll be wear
on the actual physical hardware and that is that's shows how robust I guess yeah
yeah so we create a lot of very we actually inject noise in the simulation
environment so what we do is we inject noise when the steering and speed
signals are stand that accommodates for some of the variation in the mechanical
components so neural network in the simulation learns to deal with some of
those to create robustness when it's removed from the simulation to
real-world there is a funny story about the heatsink that we doesn't have a
really big heatsink initially and then our next generation cars family were
developing heats think came and it turned out that it was really happy and
none of our models were working so we had to go back and change the mass so
masts friction Newtonian physics to really I mean that's a lot of mass as
well it's something that you know electric vehicles and autonomous cars
will have to contend with because at the scale it is a big portion but you do
have the sensor it is this camera you chose this one for you know obviously
for for cost but also again to test the robustness of the system didn't need to
be ultra wide ultra high fidelity after some of those limitations helped the
model show that there okay well they are yes
it is actually be reduced resolution on the camera bamberg learning so we were
thinking like we were going to do 64 48 but what happened is we could get by by
just doing 160 by 120 pixels pixel and the actual do gray scales it turns out
that there are more robust to the changes in the texture and the years
convolutional neural network so it really picks up what matters on the
image even with the lower resolution that's very cool and obviously this
platform was to be was used for this first season of the deep brazier
challenge but what you're announcing at reinvent is an upgrade to this can we
take a look at the upgrade yes so he could tell me about this we have a new
experience coming up here we preview it reinvent and what we are planning is
that you're going to now have the option to add more sensors to your car and we
think this is really critical because one of the important things that we
learn from our developers is they usually have to work with various types
of sensors and these types of sensors change and inputs as well this was
literally we show people out there trying to raise multiple depressors on
the track and we're like this looks really fun maybe maybe we shall try it
is it turned out that Theriault of it is really really difficult and the
literature on this topic is really catching up as more and more systems are
coming out where you need to coordinate many different components and you have
this multitude of sensors with different modalities so we thought this is really
a big challenge that was really fun to work with and make it happen it does
really sound like a challenge because even though you're adding second camera
lidar it's orders of magnitudes complex because all that sensor fusion is then
something that needs to be accounted for on the model on again like a
frame-by-frame basis but it's what the people wanted it's they were already
putting these deep racers next to each other and so this allows you to go
head-to-head yes basically it does buy it's what cameras me alone
the card have a depth perception and that means that when you're doing a
head-to-head race with another car it enables you to see how far you are from
the other car and you can navigate through so it's just that raw data the
models will train they'll understand that you now have near-field awareness
so that it's not just looking at the contrast that the lines kind of staying
within the lines as the most basic tasks but other moving objects now moving
obstacles are one thing and I know sterile lets you kind of navigate around
off schools but moving obstacles other deep racers that seems to be very
complex yes it is very complicated because you have to account for
maybe people at the Navy will not know this better when you have two
battleships and they have to really predict where the other a battleship is
going to be in order to make their maneuver so it is very similar now and
other car is moving the car has to learn that it's going to be at a certain
trajectory and that it will try to avoid that trajectory one of the things that
we figured out very quickly that we can do these maneuvers and predict what the
car is and the opponent car is moving using a stereo camera but then we
started seeing lots of crashes and down the analyze why these crashes were
happening because you see the car in front of you you're going really fast
and you steer it but it turns out that we have a multi objective your board
function that even the car is passing the moving car is coming out of the
corner so it really needs to slow down blind spot exactly so what we realized
is that the car actually needs to look back in order to decide that oh there's
a car coming behind me and it's another racer it's the competence of another
collaborator multi-agent system so it really needs to make a decision on
there's an opponent coming from behind and there's a curve in front of me
should I move fast or should I slow down so we actually put the lidar a bit
tilted to provide maximum coverage and so people can they're gonna get access
to this and work CLEC City it's gonna be a whole nother
season of people with obstacles and the tracks what is this like going forward
it means more sensors like things change and surprise you in setting this program
up in what do you see less going yes yes actually in the platform you will see
that in the consoles you'll see that you're going to be learning how to race
against another car by having kind of a very nasty racer so you're really
training to be really well in the real world however we are going to provide
opportunity for people to pick their levels so you can adjust the boat car
that you're racing towards which we call both car and you will see different
versions of it coming some of them will be changing lanes and some of them may
be try to block you so over the course of the year you will see that I you will
see this varying levels of opponents that you will be racing against time to
get ready for the next challenge it's awesome thank you so much thank you
for sharing with us this new version that the bracer evo thank you thank you
for having me