Kind: captions
Language: en
the following is a conversation with
Chris Urmson he was a CTO of the Google
self-driving car team a key engineer and
leader behind the Carnegie Mellon
University autonomous vehicle entries in
the DARPA Grand Challenges and the
winner of the DARPA urban challenge
today he's the CEO of Aurora innovation
and the autonomous vehicle software
company he started with sterling
Anderson who was the former director of
Tesla autopilot and drew back now Uber's
former autonomy and perception lead
chris is one of the top roboticists and
autonomous vehicle experts in the world
and a longtime voice of reason in a
space that is shrouded in both mystery
and hype he both acknowledges the
incredible challenges involved in
solving the problem of autonomous
driving and is working hard to solve it
this is the artificial intelligence
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with me on Twitter at Lex Friedman
spelled Fri D ma a.m. and now here's my
conversation with Chris Armisen
you were part of both the DARPA Grand
Challenge and the DARPA urban challenge
teams at CMU with red Whittaker what
technical or philosophical things have
you learned from these races I think the
the high order bit was that it could be
done I think that was the thing that was
incredible about the first the the Grand
Challenges that I remember you know I
was a grad student at Carnegie Mellon
and there we was kind of this dichotomy
of it seemed really hard so that'd be
cool and interesting but you know at the
time we were the only robotics Institute
around and so if we went into it and
fell on our faces that would that would
be embarrassing so I think you know just
having the will to go do it to try to do
this thing that at the time was marked
as you know darn near impossible and and
then after a couple of tries be able to
actually make it happen I think that was
you know that was really exciting but at
which point did you believe it was
possible
did you from the very beginning did you
personally because you're one of the
lead engineer you actually had to do a
lot of the work yeah I was the technical
director there and did al huddle the
work along with a bunch of other really
good people
did I believe it could be done yeah of
course right like why would you go do
something you thought was impossible
completely impossible we thought it was
gonna be hard we didn't know how we're
gonna be able to do it we didn't know if
we'd be able to do it the first time
turns out we couldn't
that yeah I guess you have to I think
there's a certain benefit to naivete
right that if you don't know how hard
something really is you you try
different things and you know gives you
an opportunity that others who are you
know wiser maybe don't don't have what
were the biggest pain points mechanical
sensors hardware software algorithms for
mapping localization just general
perception control what the hardware
soft first of all I think that's the joy
of this field is that it's all hard and
that's you have to be good at at each
part of it so for the first for the
urban challenges if I look back at it
from today
it should be easy today that you know it
was a static world there weren't other
actors moving through it that is what
that means it was out in the desert so
you get really good GPS you know so that
that went in you know we could map it
roughly and so in retrospect now it's
you know it's it's within the realm of
things we could do back then just
actually getting the vehicle and the you
know there's a bunch of engineering work
to get the vehicle so that we could
control and drive it that's you know
that's still a pain today but it was
even more so back then and then the
uncertainty of exactly what they wanted
us to do was was part of the challenge
as well right you didn't actually know
the track heading in you know
approximately but you know it didn't
actually know the route the route that's
gonna be taken that's right we didn't
know the route we didn't even really the
way the rules had been described you had
to kind of guess so if you think back to
that challenge the idea was to that the
the government would give us the DARPA
would give us a set of waypoints and
kind of the width that you had to stay
within between the line that went
between you know each of those waypoints
and so the the most devious thing they
could have done is set you know a
kilometer wide corridor across you know
a field of scrub brush and rocks and
said you know go figure it out
fortunately it really it turned into
basically driving along a set of trails
which you know is much more relevant to
to the application they were looking for
but no it was it was a hell of a thing
back in the day so the legend read was
kind of leading that effort in terms
just broadly speaking so you're a leader
now what have you learned from reading
about leadership I think there's a
couple things one is you know go and try
those really hard things that that's
where there is an incredible opportunity
I think the other big one though is to
see people for who they can be not who
they are it's one of the things that I
actually one of the deepest lessons I
learned from read was that he would look
at you know undergraduates or graduate
students and empower them to be leaders
to to you know have responsibility to do
great things that I think another person
might look at them and think oh that's
just you know another graduate student
what could they know and so I think that
that you know kind of trust but verify I
have confidence and what people can
become I think is a really powerful
thing so through that it's just like
fast-forward through the history can you
maybe talk through the technical
evolution of autonomous vehicle systems
from the first to Grand Challenges to
the urban challenge to today are there
major shifts in your mind or is it the
same kind of technology just made more
robust I think there's been some big big
steps
so the for the grand challenge the real
technology that unlocked that was HD
mapping prior to that a lot of the
off-road robotics work had been done
without any real prior model of what the
vehicle was going to encounter and so
that innovation that the fact that we
could get you know decimeter resolution
models was really a big deal and that
allowed us to to kind of bound the
complexity of the driving problem the
vehicle had and allowed it to operate at
speed because we could assume things
about the environment that it was going
to encounter so that was a that was one
of the that was the big step there
for the urban challenge you know one of
the big technological innovations there
was the multi beam lidar and being able
to generate a high resolution you know
mid to long range 3d models the world
and use that for you know for
understanding the world around the
vehicle and that was really a you know
kind of a game-changing technology in
parallel with that we saw a bunch of
other technologies that have been kind
of converging half their their day in
the Sun so Bayesian estimation had been
you know slam had been a big field in
robotics you know you would go to a
conference you know a couple years
before that and every paper would
effectively have slams somewhere in it
and so seeing that you know that looks
Bayesian estimation techniques you know
play out on a very visible stage you
know I thought that was that was pretty
exciting to see
and mostly slam was done based on lidar
that time well yeah and in fact we
weren't really doing slam per se you
know it you know in real time because we
had a model ahead of time we had a road
map but we were doing localization and
we were using you know the lidar or the
cameras depending on you know who
exactly was doing it to localize to a
model of the world and I thought that
was that was a big step from kind of
naively trusting GPS I and s before that
and and again like lots of work had been
going on in this field certainly this
was not doing anything particularly
innovative in slam over in localization
but it was seeing that technology
necessary in a real application on a big
stage I thought was very cool so for the
urban challenge that was already maps
constructed offline yes in general okay
and did people do that individually
individual teams do it individually so
they had their own difference of
different approaches there or they never
really kind of
share that information at least
intuitively so so the DARPA gave all the
teams a a model of the world they you
know a map and then you know one of the
things that we had to figure out back
then was and it's still one of these
things that trips people up today is
actually the coordinate system so you
get a latitude longitude and you know -
so many decimal places you don't really
care about kind of the ellipsoid of the
earth that's being used but when you
want to get to ten centimeter or
centimeter resolution you care whether
the the core system is you know Nats 83
or wgs84 or you know these are different
ways to describe both the the kind of
non spherical nosov the earth but also
kind of the actually in I think I can't
remember which one the tectonic shifts
that are happening and how to transform
you know the the global datum as a
function of that so you're getting a map
and then actually matching it to reality
two centimeter resolution that was kind
of interesting and fun back then so how
much work was the perception doing there
so how how much were you relying on
localization based on maps without using
perception to register to the maps and
how I guess the question is how advanced
was perception at that point it's
certainly behind where we are today
right we're we're more than a decade
since the graph or the urban challenge
but the the core of it was there that we
were tracking vehicles we had to do that
at a hundred plus meter range because we
had to merge with other traffic we were
using you know Bayesian again Bayesian
estimates for for state of these
vehicles we had to deal with a bunch of
the problems that you you think of today
of predicting what that where that
vehicle is going to be a few seconds
into the future we had to deal with the
fact that there were multiple hypotheses
for that because a vehicle at an
intersection might be going right or it
might be going straight or I'd be making
a left turn
and we had to deal with the challenge of
the fact that our behavior was going to
impact the behavior of that other upper
operator and you know we did a lot of
that in relative Najee relatively naive
ways but it caused third still had to
have some kind of Thanos yeah and so
where does that ten years later where
does that take us today from that
artificial city construction to real
cities to the urban environment yeah I
think the the biggest thing is that the
you know the the actors are truly
unpredictable that most of the time you
know the drivers on the road the other
road users are out there
behaving well but everyone's father or
not the variety of other vehicles is you
know you have all of the intended
behavior in terms of perception or both
that we have you know back then we
didn't have to deal with cyclists we
didn't have to deal with pedestrians
didn't have to deal with traffic lights
you know the scale over which that you
have to operate us now you know is much
larger than you know the airbase that we
were thinking about back then
so what easy question what do you think
is the hardest part about driving easy
question yeah no I'm joking I I'm sure
no nothing really jumps out at you as
one thing but in in the jump from the
urban challenge to the real world is
there something that's a particularly
for seus very serious difficult
challenge I think the most fundamental
difference is that were doing it for
real and that in that environment it was
both a limited complexity environment
because certain actors weren't there
because you know the roads were
maintained there were barriers keeping
people separate from from robots at the
time and it only had to work for 60
miles which looking at it from you know
2006 it had to work for 60 miles yeah
right looking at it from now you know we
we want things that will go and drive
for you know half a half a million miles
and you know it's just a it's a
different game so how important he said
leiter came into the game early on and
it's really the primary driver of
autonomous vehicles today as a sensor so
how important is the role of lidar in
the sense of suite in the near term so I
think it's I think it's essential you
know I believe it but I also believe is
the cameras are essential and I believe
the radars is essential I think that you
you really need to use the composition
of data from from these different
sensors if you want the thing to to
really be robust the question I want to
ask let's see if we kind of tangle is
what are your thoughts on the Elon Musk
provocative statement that lidar is a
crutch that is the kind of I guess
growing pains and that's much of the
perception tasks can be done with
cameras so I think it is undeniable that
people walk around without you know
lasers in their forehead
and they can get into vehicles and drive
them and and so there's an existence
proof that you can drive using you know
passive fission no doubt can't argue
with that in terms of sensors yeah so
yes maybe sensors right so like there's
there's an example that we all go do it
have many of us everyday in terms of
latter being a crutch sure but but you
know in the same way that you know the
combustion engine was a crutch on the
path to an electric vehicle on the same
way that you know any technology
ultimately gets
replaced by some superior technology in
the future and really what with the way
that I look at this is that the way we
get around on the ground the way that we
use transportation is broken and that we
have you know this this you know what
was I think the number I saw this
morning 37,000 Americans killed last
year on our roads and that's just not
acceptable and so tech any technology
that we can bring to bear that
accelerates the this techno you know
self-driving technology coming to market
and saving lives is technology we should
be using and it feels just arbitrary to
say well you know I'm I'm not okay with
using lasers because that's whatever but
I am okay with using an 8 megapixel
camera or a 16 megapixel camera you know
like it's just these are just bits of
technology and we should be taking the
best technology from the tool bin that
allows us to go and you know and solve a
problem the question I often talk to
well obviously you do as well to sort of
automotive companies and you know if
there's one word that comes up more
often than anything is costs and and
trying to drive cost down so while it's
it's true that it's a tragic number the
37,000 the the question is what and I'm
not the one asking these questions I
hate this question but yeah we want to
find the cheapest sensor suite that the
creates a safe vehicle so in that
uncomfortable trade-off do you foresee
lidar coming down in cost in the future
or do you see a day where level for
autonomy is possible without lighter I
see both of those but it's really a
matter of time and I think really maybe
the I would talk to the question you
asked about you know the cheapest set
certainly I don't think that's actually
what you want what you want is a sensor
suite that is economically viable and
then after that everything is about
margin and driving cost out of the
system what you also want is a sense
suite that were
and so it's great to tell a story about
how you know how it'd be better to have
a self-driving system with a $50 sensor
instead of a you know a $500 dancer but
if the $500 sensor makes it work and the
$50 sensor doesn't work you know who
cares the longest you can actually you
have an economic offer you know there's
an economic opportunity there and the
economic opportunity is important
because that's how you actually have a
sustainable business and that's how you
can actually see this come to scale and
and and be out in the world and so when
I look at lidar I see a technology that
has no underlying fundamentally you know
expense to it fundamental expense to it
it's it's going to be more expensive
than an imager because you know CMOS
processes or you know fab processes are
dramatically more scalable than
mechanical processes but we still should
be able to drive cost out substantially
on that side and then I also do think
that with the right business model you
can absorb more you know certainly more
cost on the Bill of Materials yeah if
the sense of sweetie works extra values
provided thereby you don't need to drive
costs down to zero it's the basic
economics you've talked about your
intuition that level to autonomy is
problematic because of the human factor
of vigilance that command complacency
over trust and so on just us being human
yeah we trust the system we start doing
even more so partaking in the secondary
activities like smart phone and so on
have your views evolved on this point in
either direction can you can you speak
to it so and I want to be really careful
because sometimes this gets
twist in a way that's that that I
certainly didn't intend so active safety
systems are a really important
technology that we should be pursuing
and integrating into vehicles and
there's an opportunity in the near term
to reduce accidents reduce fatalities
and that's and we should be we should be
pushing on that level two systems are
systems where the vehicle is controlling
two axes so in breaking and braking and
throttle / steering and I think there
are variants of level two systems that
are supporting the driver that
absolutely like we should we should
encourage to be out there where I think
there's a real challenge is in the the
human factors part around this and the
misconception from the public around the
capability set that that enables and the
trust they should have in it and that is
where I you know I kind of I am actually
incremental II more you know concerned
around level three systems and you know
how exactly a level two system is
marketed and delivered and you know how
people how much effort people have put
into those human factors so I still
believe several things around this one
is people will over trust the technology
we've seen over the last few weeks you
know a spate of people sleeping in their
Tesla you know I watched an episode last
night of Trevor Noah
talking about this and you know him you
know this is a smart guy who's has a lot
of resources at his disposal describing
a Tesla's a self-driving car and that
why shouldn't people be sleeping in
their Tesla there's like well because
it's not a self-driving car and it is
not intended to be and you know these
people will almost certainly you know
die at some point or hurt other people
and so we need to really be thoughtful
about how that technology is described
and brought to market I also think that
because of the economic issue
you know Iike my economic challenges we
were just talking about
that that technology path will ugly
these level two driver assistance
systems that technology path will
diverge from the technology path that we
need to be on to actually deliver truly
self-driving vehicles ones where you can
get it and sleep and have the equivalent
or better safety then you know a human
driver behind the wheel because the
again the economics are very different
in those two worlds and so that leads to
you know divergent technology so you
just don't see the economics of
gradually increasing from level two and
doing so quickly enough to where it
doesn't cost safety critical safety
concerns you believe that the it needs
to diverge at this point in two
different basically different routes and
really that comes back to what are those
l2 and l1 systems doing and and they are
driver systems functions where the the
the people that are marketing that
responsibly are being very clear and
putting human factors in place such that
the driver is actually responsible for
the vehicle and that the technology is
there to support the driver and the
safety cases that are built around those
or dependence on that driver attention
and attentiveness and at that point you
you can kind of give up
to some degree for economic reasons you
can give up on safe false negatives and
so and the way to think about this is
for a for collision mitigation braking
system if it half the times the driver
missed a vehicle in front of it it hit
the brakes and brought the vehicle to a
stop that would be an incredible
incredible advance and in safety on our
roads right that would be equivalent to
seatbelts but it would mean that if that
vehicle wasn't being monitored it would
hit one out of two cars and so
economically that's a perfectly good
solution for a driver assistance system
what you should do at that point if you
can get it to work 50 percent of the
time is drive the cost out of that so
you can get it on as many vehicles as
possible but driving the cost out of it
doesn't drive up performance on the
false negative case and so you'll
continue to not have a technology that
could you know really be available for
for a self driven vehicle so clearly the
communication and this probably applies
though for vehicles as well the
marketing and a communication of what
the technology is actually capable of
how hard it is how easy it is all that
kind of stuff is highly problematic so
but it's say everybody in the world was
perfectly communicated and were made to
be completely aware of every single
technology out there what they what it's
able to do what's your intuition and now
maybe getting into philosophical ground
is it possible to have a level 2 vehicle
where we don't over trust it I don't
think so if people truly understood the
risks and internalized it then then sure
you could do that safely but that that's
a world that doesn't exist that people
are going to they're gonna you know if
the facts are put in front of them
they're gonna then combine that with
their experience and you know let's say
they're they're using an l2 system and
they go up and down the 101 every day
and they do that for a month and it just
worked every day for a month like that's
pretty compelling at that point you know
just even if you know the statistics
like well
I don't know maybe there's something
funny about those maybe they're you know
driving in difficult places like I've
seen it with my own eyes it works and
the problem is that that sample size
that they have so it's 30 miles up but
now so 60 miles times 30 days so 60 180
a thousand eight hundred miles that's
that's a drop in the bucket compared to
the one you know what eighty-five
million miles between fatalities and so
they don't really have a true estimate
based on their personal experience of
the real risks but they're gonna trust
it anyway because it's hard not to work
for a month
West what's gonna change so even if you
start at perfect understanding of the
system your own experience will make it
drift and that's a big concern you know
over a year over two years even it
doesn't have to be months and I think
that as this technology moves from what
I say it's kind of the more technology
savvy ownership group to you know the
mass market you may be able to have some
of those folks who are really familiar
with technology they may be able to
internalize it better and you know
you're kind of immunization against this
kind of false risk assessment might last
longer but as folks who are who aren't
as savvy about that you know read the
material and they compare that to their
personal experience I think there that
you know it's it's going to it's gonna
move more quickly so your work the
program that you've created a Google and
now at Aurora is focused more on the
second path of creating full autonomy so
it's such a fascinating I think it's one
of the most interesting AI problems of
the century right it's a I just talked
to a lot of people just regular people I
don't know my mom about autonomous
vehicles and you begin to grapple with
ideas of giving your life control over
to a machine is philosophically
interesting it's practically interesting
so let's talk about safety how do you
think we demonstrate you spoken about
metrics in the past how do you think we
demonstrate to the world that an
autonomous vehicle an Aurora system is
safe
this is one where it's difficult because
there isn't a soundbite answer that we
have to show a combination of work that
was done diligently and thoughtfully and
this is where something like a
functional safety process as part of
that is like here's here's the way we
did the work that means that we were
very thorough so you know if you believe
that we what we said about this is the
way we did it then you can have some
confidence that we were thorough in in
in the engineering work we put into the
system and then on top of that the you
know to kind of demonstrate that we
weren't just thorough we were actually
good at what we did there'll be a kind
of a collection of evidence in terms of
demonstrating that the capabilities work
the way we thought they did you know
statistically and and to whatever degree
we can we can demonstrate that both in
some combination of simulations some
combination of unit testing and
decomposition testing and then some part
of it will be on Road data and and I
think the the way we will ultimately
convey this to the public is they'll be
clearly some conversation with the
public about it but we'll you know kind
of invoke the the kind of the trusted
nodes and that will spend more time
being able to go into more depth with
folks like like nitsa and other federal
and state regulatory bodies and kind of
given that they are operating in the
public interest and they're trusted
that if we can you know show enough work
to them that they're convinced then you
know I think we're in a in a pretty good
place
that means you work with people that are
essentially experts at safety to try to
discuss and show do you think the answer
is probably no but just in case do you
think there exists a metric so currently
people have been using number of
disengagement yeah and it quickly turns
into a marketing scheme to just sort of
you alter the experiments you run to
adjust I think you've spoken that you
don't like no Mohammed no in fact I I
was on the record telling DMV that I
thought this was not a great metric do
you think it's possible to create a
metric a number that that could
demonstrate safety outside of fatalities
so so I I do and I think that it won't
be just one number so as we are
internally grappling with us and at some
point we'll be we'll be able to talk
more publicly about it is how do we
think about human performance in
different tasks say detecting traffic
lights or safely making a left turn
across traffic and what do we think the
failure rates are for those different
capabilities for people and then
demonstrating to ourselves and then
ultimately folks the regulatory role and
and then ultimately the public that we
have confidence that our system will
work better than that and so these these
individual metrics will can tell a
compelling story ultimately I do think
at the end of the day what we care about
in terms of safety is life saved and
injuries reduced and then and then
ultimately you know kind of casualty
dollars
that people aren't having to pay to get
their car fixed and I do think that you
can you know we in aviation they look at
a kind of an event pyramid where you
know a crash is at the top of that and
that's the worst event obviously and
then there's injuries and you know
near-miss events and whatnot and and you
know violation of operating procedures
and and you kind of build a statistical
model of the relevance of the low
severity things to the high spirit of
things I think that's something where
we'll be able to look at as well because
you know an event per 85 million miles
that you know statistically a difficult
thing even at the scale of the u.s. to
to to kind of compare directly and that
event the fatality that's connected to
an autonomous vehicle is significantly
at least currently magnified in the
amount of attention and yet so that
speaks to public perception I think the
most popular topic about autonomous
vehicles in the public is the trolley
problem formulation right which has
let's not get into that too much but is
misguided but in many ways but it speaks
to the fact that people are grappling
with this idea of giving control over to
a machine so how do you win the hearts
and minds of the people that autonomy is
something that could be a part of their
lives thank you let them experience it
alright I think it's I think I think
it's right I think people should be
skeptical I think people should ask
questions I think they should doubt
because this is something new and
different
they haven't touched it yet and I think
it's perfectly reasonable and but at the
same time it's clear there's an
opportunity to make the roads safer it's
clear that we can improve access to
mobility it's clear that we can reduce
the cost of mobility and that once
people try that and are you know
understand that it's safe and are able
to use in their daily lives I think it's
one of these things that will will just
be obvious and I've seen this
practically
in you know demonstrations that I've you
know given where I've had people come in
and you know they're very skeptical they
again in a vehicle you know my favorite
one is taking somebody out on the
freeway and we're on the 101 driving at
65 miles an hour
and after ten minutes they they kind of
turn and ask is that all it does and
you're like yeah it's self-driving car
not sure exactly which I thought it
would be right but they you know they it
becomes mundane which is which is
exactly what you want a technology like
this to be right we don't really when I
turn the light switch on in here I don't
think about the complexity of you know
the those electrons you know being
pushed down a wire from wherever it was
and being generated it's not like it's
just it's like I just get annoyed if it
doesn't work right and and what I value
is the fact that I can do other things
in this space I can you know see my
colleagues I can read stuff on a paper I
can you know not be afraid of the dark
and I think that's what we want this
technology to be like is it's it's in
the background and people get to have
those those life experiences and and do
so safely
so putting this technology in the hands
of people speaks to scale the deployment
all right so what do you think the the
dreaded question about the future
because nobody can predict the future
yeah but just maybe speak poetically
about when do you think we'll see a
large-scale deployment of autonomous
vehicles ten thousand those kinds of
numbers you will see that within ten
years I'm pretty confident we what's an
impressive scale what moment so you've
done DARPA Challenger there's one
vehicle at which moment does it become
wow this is serious scale so so I think
the moment it gets serious is when we
really do have driverless vehicle
operating on public roads and that we
can do that kind of continuously without
a safety dry without a safety driver in
the vehicle I think at that moment we've
we've kind of crossed the zero to one
throw
shoulde and then it is about how do we
continue to scale that how do we build
the right business models how do we
build the right customer experience
around it so that it is actually you
know a useful product out in the world
and I think that is really at that point
it moves from a you know what is this
kind of mixed science engineering
project into engineering and
commercialization and really starting to
deliver on the value that we all see
here and you know actually making that
real in the world what do you think that
deployment looks like where do we first
see the inkling of no safety driver one
or two cars here and there is it on the
highway is it in specific roads in the
urban environment I think it's going to
be urban suburban type environments you
know with a roar when we we thought
about how to tackle this I is kind of
enfoque to think about trucking as
opposed to urban driving and and you
know the again the human intuition
around this is that freeways are easier
to drive on because everybody's kind of
going in the same direction and you know
lanes are wider etc and I think that
that intuition is pretty good except we
don't really care about most of the time
we we care about all of the time and
when you're driving on a freeway with a
truck say 70 70 miles an hour and you've
got 70,000 pound load with you that's
just an incredible amount of kinetic
energy and so when that goes wrong it
goes really wrong and that those those
challenges that you see occur more
rarely so you don't get to learn as all
as quickly and there you know
incrementally more difficult than urban
driving but they're not easier than
urban driving and so I think this
happens in moderate speed urban
environments because they're you know if
if two vehicles crash at 25 miles per
hour it's not good but probably
everybody walks away
those those events where there's the
possibility for that occurring happened
frequently so we get to learn more
rapidly we get to do that with lower
risk for everyone and then we can
deliver value to people that they need
to get from one place to another and
then once we've got that solved then the
kind of the freeway driving part of this
just falls out but we were able to learn
it's more safely more quickly in the
urban environment so ten years and then
scale twenty thirty year I mean who
knows if if it's sufficiently compelling
experience is created it can be faster
and slower do you think there could be
breakthroughs and what kind of break
throughs might there be that completely
changed that timeline again not only am
I asked to predict the future oh yeah
I'm asking you to predict breakthroughs
that haven't happened yet so what's the
I think another way to ask that was
would be if I could wave a magic wand
what part of the system would I make
work today to accelerate it as quick as
possible as quickly as possible
don't say infrastructure please don't
say infrastruc no it's definitely not
infrastructure it's really that car that
perception forecasting capability so if
if tomorrow you could give me a perfect
model of what's happened what is
happening and what will happen for the
next five seconds around a vehicle on
the roadway that would accelerate things
pretty dramatically how you in terms of
staying up at night are you mostly
bothered by cars pedestrians or cyclists
so I I worry most about the vulnerable
road users about the combination of
cyclists and cars right just because I
khlyst and pedestrians because you know
they're not in armor you know with the
cars they're bigger
they've got protection for the people
and so the ultimate risk is is lower
there
whereas a pedestrian or cyclist they're
out in the road you know they they don't
have any protection and so you know we
need to pay extra attention to that do
you think about a very difficult
technical challenge of the fact that
pedestrians if you try to protect
pedestrians by being careful and slow
they'll take advantage of that so the
game theoretic dance yeah does that
worry you of how from a technical
perspective how we solve that because as
humans the way we solve that it's kind
of nudge our way through the pedestrians
which doesn't feel from a technical
perspective as a appropriate algorithm
but do you think about how we solve that
problem yeah I think I think there's
there's I think that was actually
there's two different concepts there so
one is am I worried that because these
vehicles are self-driving people kind of
step in the road and take advantage of
them and I've heard this and I don't
really believe it because if I'm driving
down the road and somebody steps in
front of me I'm going to stop right like
a even if I'm annoyed I'm not going to
just drive through a person stood on the
road right and so I think today people
can take advantage of this and you and
you do see some people do it I guess
there's an incremental risk because
maybe they have lower confidence that
I'm going to see them than they might
have for an automated vehicle and so
maybe that shifts it a little bit I
think people don't want to get hit by
cars and so I think that I'm not that
worried about people walking out of the
101 and you know creating chaos more
than they would today regarding kind of
the nudging through a big stream of
pedestrians leaving a concert or
something I think that is further down
the technology pipeline I think that
you're right that's tricky I don't think
it's necessarily I think the algorithm
people use for this is pretty simple
Yeah right it's kind of just move
forward slowly and if somebody's really
close and stop and and I think that that
probably can be replicated pretty pretty
easily and particularly given that it's
you don't do this at 30 miles an hour
you do it at one that even in those
situations the risk is relatively
minimal but I you know it's not
something we're thinking about in any
serious way and probably the that's less
an algorithm problem or creating a human
experience so they see AI people that
create a visual display that you're
pleasantly as a pedestrian nudged out of
the way yes that's a that's a yeah
that's an experienced problem not an
algorithm problem who's the main
competitor to Arora today and how do you
out-compete them in the long run so we
really focus a lot of what we're doing
here I think that you know I've said
this a few times that this is a huge
difficult problem and it's great that a
bunch of companies are tackling it
because I think it's so important for
society that somebody gets there so we
you know we're we don't spend a whole
lot of time like thinking tactically
about who's out there and and how do we
beat that that that person individually
what are we trying to do to go faster
ultimately well part of it is the
leadership team we have has got pretty
tremendous experience and so we kind of
understand the landscape and understand
where the coldest acts are to some
degree and you know we try and avoid
those I think there's a part of it just
this great team we've built people this
is a technology and a company that
people believe in the mission of and so
it allows us to attract just awesome
people to go work we've got a culture I
think that people appreciate that allows
them to focus allows them to really
spend time solving problems and I think
that keeps them energized and then we've
invested hard invested heavily in the
infrastructure and architectures that we
think will ultimately accelerate us so
because of the folks were able to bring
in early on because the the the great
investors we have you know we don't
spend all of our time doing demos and
kind of leaping from one demo to the
next we've been given the freedom to
invest in
infrastructure to do machine learning
infrastructure to pull data from our
on-road testing infrastructure to use
that to accelerate engineering and I
think that that early investment and
continuing investment and those kind of
tools will ultimately allow us to
accelerate and do something pretty
incredible Chris beautifully put
it's a good place to end thank you so
much for talking today oh thank you very
much really enjoyed it
you