The following is a conversation with Chris Sampson.
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, an 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 podcast.
If you enjoy it, subscribe on YouTube,
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at Lex Friedman, spelled F R I D M A N.
And now, here's my conversation with Chris Sampson.
You were part of both the DARPA Grand Challenge
and the DARPA Urban Challenge teams
at CMU with Red Whitaker.
What technical or philosophical things
have you learned from these races?
I think the high order bit was that it could be done.
I think that was the thing that was
incredible about the first of the Grand Challenges,
that I remember I was a grad student at Carnegie Mellon,
and there was kind of this dichotomy of it
seemed really hard, so that would
be cool and interesting.
But at the time, we were the only robotics institute around,
and so if we went into it and fell on our faces,
that would be embarrassing.
So I think just having the will to go do it,
to try to do this thing that at the time
was marked as darn near impossible,
and then after a couple of tries,
be able to actually make it happen,
I think 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 engineers.
You actually had to do a lot of the work.
Yeah, I was the technical director there,
and did a lot of the work, along with a bunch
of other really good people.
Did I believe it could be done?
Yeah, of course.
Why would you go do something you thought
was completely impossible?
We thought it was going to be hard.
We didn't know how we were going to 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 try different things, and it gives you an opportunity
that others who are wiser maybe don't have.
What were the biggest pain points?
Mechanical, sensors, hardware, software,
algorithms for mapping, localization,
just general perception, control?
Like hardware, software, first of all?
I think that's the joy of this field, is that it's all hard
and that you have to be good at each part of it.
So for the urban challenges, if I look back at it from today,
it should be easy today, that it was a static world.
There weren't other actors moving through it,
is what that means.
It was out in the desert, so you get really good GPS.
So that went, and we could map it roughly.
And so in retrospect now, it's within the realm of things
we could do back then.
Just actually getting the vehicle and the,
there's a bunch of engineering work
to get the vehicle so that we could control it and drive it.
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 part of the challenge as well.
Right, you didn't actually know the track heading in here.
You knew approximately, but you didn't actually
know the route that was going to 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 that 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 each of those waypoints.
And so the most devious thing they could have done
is set a kilometer wide corridor across a field
of scrub brush and rocks and said, go figure it out.
Fortunately, it really, it turned into basically driving
along a set of trails, which is much more relevant
to the application they were looking for.
But no, it was a hell of a thing back in the day.
So the legend, Red, was kind of leading that effort
in terms of just broadly speaking.
So you're a leader now.
What have you learned from Red about leadership?
I think there's a couple things.
One is go and try those really hard things.
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 Red
was that he would look at undergraduates
or graduate students and empower them to be leaders,
to have responsibility, to do great things
that I think another person might look at them
and think, oh, well, that's just an undergraduate student.
What could they know?
And so I think that kind of trust but verify,
have confidence in what people can become,
I think is a really powerful thing.
So through that, let's just fast forward through the history.
Can you maybe talk through the technical evolution
of autonomous vehicle systems
from the first two 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 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
decimeter resolution models was really a big deal.
And that allowed us 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 the big step there.
For the Urban Challenge,
one of the big technological innovations there
was the multi beam LIDAR
and being able to generate high resolution,
mid to long range 3D models of the world
and use that for understanding the world around the vehicle.
And that was really kind of a game changing technology.
In parallel with that,
we saw a bunch of other technologies
that had been kind of converging
half their day in the sun.
So Bayesian estimation had been,
SLAM had been a big field in robotics.
You would go to a conference a couple of years before that
and every paper would effectively have SLAM somewhere in it.
And so seeing that the Bayesian estimation techniques
play out on a very visible stage,
I thought that was pretty exciting to see.
And mostly SLAM was done based on LIDAR at that time.
Yeah, and in fact, we weren't really doing SLAM per se
in real time because we had a model ahead of time,
we had a roadmap, but we were doing localization.
And we were using the LIDAR or the cameras
depending on who exactly was doing it
to localize to a model of the world.
And I thought that was a big step
from kind of naively trusting GPS, INS before that.
And again, lots of work had been going on in this field.
Certainly this was not doing anything
particularly innovative in SLAM or 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,
those are already maps constructed offline in general.
And did people do that individually,
did individual teams do it individually
so they had their own different approaches there
or did everybody kind of share that information
at least intuitively?
So DARPA gave all the teams a model of the world, a map.
And then 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 to 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 10 centimeter
or centimeter resolution,
you care whether the coordinate system is NADS 83
or WGS 84 or these are different ways to describe
both the kind of non sphericalness of the earth,
but also kind of the, I think,
I can't remember which one,
the tectonic shifts that are happening
and how to transform the global datum as a function of that.
So getting a map and then actually matching it to reality
to centimeter resolution, that was kind of interesting
and fun back then.
So how much work was the perception doing there?
So how much were you relying on localization based on maps
without using perception to register to the maps?
And I guess the question is how advanced
was perception at that point?
It's certainly behind where we are today, right?
We're more than a decade since the urban challenge.
But the core of it was there.
That we were tracking vehicles.
We had to do that at 100 plus meter range
because we had to merge with other traffic.
We were using, again, Bayesian estimates
for state of these vehicles.
We had to deal with a bunch of the problems
that you think of today,
of predicting where that vehicle's 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 it might 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 operator.
And we did a lot of that in relatively naive ways,
but it kind of worked.
Still had to have some kind of solution.
And so where does that, 10 years later,
where does that take us today
from that artificial city construction
to real cities to the urban environment?
Yeah, I think the biggest thing
is that the actors are truly unpredictable.
That most of the time, the drivers on the road,
the other road users are out there behaving well,
but every once in a while they're not.
The variety of other vehicles is, you have all of them.
In terms of behavior, in terms of perception, or both?
Both.
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.
The scale over which that you have to operate is now
is much larger than the air base
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'm sure nothing really jumps out at you as one thing,
but in the jump from the urban challenge to the real world,
is there something that's a particular,
you foresee as very serious, difficult challenge?
I think the most fundamental difference
is that we're doing it for real.
That in that environment,
it was both a limited complexity environment
because certain actors weren't there,
because the roads were maintained,
there were barriers keeping people separate
from robots at the time,
and it only had to work for 60 miles.
Which, looking at it from 2006,
it had to work for 60 miles, right?
Looking at it from now,
we want things that will go and drive
for half a million miles,
and it's just a different game.
So how important,
you said LiDAR 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 sensor suite in the near term?
So I think it's essential.
I believe, but I also believe that cameras are essential,
and I believe the radar is essential.
I think that you really need to use
the composition of data from these different sensors
if you want the thing to really be robust.
The question I wanna ask,
let's see if we can untangle it,
is what are your thoughts on the Elon Musk
provocative statement that LiDAR is a crutch,
that it's a kind of, I guess, growing pains,
and that much of the perception task
can be done with cameras?
So I think it is undeniable
that people walk around without lasers in their foreheads,
and they can get into vehicles and drive them,
and so there's an existence proof
that you can drive using passive vision.
No doubt, can't argue with that.
In terms of sensors, yeah, so there's proof.
Yeah, in terms of sensors, right?
So there's an example that we all go do it,
many of us every day.
In terms of LiDAR being a crutch, sure.
But in the same way that the combustion engine
was a crutch on the path to an electric vehicle,
in the same way that any technology ultimately gets
replaced by some superior technology in the future,
and really 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 this, 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 any technology that we can bring to bear
that accelerates this self driving technology
coming to market and saving lives
is technology we should be using.
And it feels just arbitrary to say,
well, I'm not okay with using lasers
because that's whatever,
but I am okay with using an eight megapixel camera
or a 16 megapixel camera.
These are just bits of technology,
and we should be taking the best technology
from the tool bin that allows us to go and solve a problem.
The question I often talk to, well, obviously you do as well,
to sort of automotive companies,
and if there's one word that comes up more often
than anything, it's cost, and trying to drive costs down.
So while it's true that it's a tragic number, the 37,000,
the question is, and I'm not the one asking this question
because I hate this question,
but we want to find the cheapest sensor suite
that 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 four autonomy
is possible without LiDAR?
I see both of those, but it's really a matter of time.
And I think really, maybe I would talk to the question
you asked about the cheapest sensor.
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 costs out of the system.
What you also want is a sensor suite that works.
And so it's great to tell a story about
how it would be better to have a self driving system
with a $50 sensor instead of a $500 sensor.
But if the $500 sensor makes it work
and the $50 sensor doesn't work, who cares?
So long as you can actually have an economic opportunity,
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 be out in the world.
And so when I look at LiDAR,
I see a technology that has no underlying
fundamentally expense to it, fundamental expense to it.
It's going to be more expensive than an imager
because CMOS processes or FAP processes
are dramatically more scalable than mechanical processes.
But we still should be able to drive costs down
substantially on that side.
And then I also do think that with the right business model
you can absorb more,
certainly more cost on the bill of materials.
Yeah, if the sensor suite works, extra value is 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 two autonomy is problematic
because of the human factor of vigilance,
decrement, complacency, over trust and so on,
just us being human.
We over trust the system,
we start doing even more so partaking
in the secondary activities like smartphones and so on.
Have your views evolved on this point in either direction?
Can you speak to it?
So, and I want to be really careful
because sometimes this gets twisted in a way
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 we should be pushing on that.
Level two systems are systems
where the vehicle is controlling two axes.
So braking and throttle slash steering.
And I think there are variants of level two systems
that are supporting the driver.
That absolutely we should encourage to be out there.
Where I think there's a real challenge
is in the human factors part around this
and the misconception from the public
around the capability set that that enables
and the trust that they should have in it.
And that is where I kind of,
I'm actually incrementally more concerned
around level three systems
and how exactly a level two system is marketed and delivered
and how much effort people have put into those human factors.
So I still believe several things around this.
One is people will overtrust the technology.
We've seen over the last few weeks
a spate of people sleeping in their Tesla.
I watched an episode last night of Trevor Noah
talking about this and him,
this is a smart guy who has a lot of resources
at his disposal describing a Tesla as a self driving car
and that why shouldn't people be sleeping in their Tesla?
And it's like, well, because it's not a self driving car
and it is not intended to be
and these people will almost certainly 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 challenges
we were just talking about,
that 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 in it and drive it.
Can get in it and sleep and have the equivalent
or better safety than a human driver behind the wheel.
Because again, the economics are very different
in those two worlds and so that leads
to 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 cause safety, critical safety concerns.
You believe that it needs to diverge at this point
into basically different routes.
And really that comes back to what are those L2
and L1 systems doing?
And they are driver assistance functions
where 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
are dependent on that driver attention and attentiveness.
And at that point, you can kind of give up
to some degree for economic reasons,
you can give up on say false negatives.
And the way to think about this
is for a four 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
in safety on our roads, right?
That would be equivalent to seat belts.
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% 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 really be available for a self driven vehicle.
So clearly the communication,
and this probably applies to all four vehicles as well,
the marketing and 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 say everybody in the world was perfectly communicated
and were made to be completely aware
of every single technology out there,
what it's able to do.
What's your intuition?
And now we're maybe getting into philosophical ground.
Is it possible to have a level two vehicle
where we don't over trust it?
I don't think so.
If people truly understood the risks and internalized it,
then sure, you could do that safely.
But that's a world that doesn't exist.
The people are going to,
if the facts are put in front of them,
they're gonna then combine that with their experience.
And let's say 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,
just even if you know the statistics,
you're like, well, I don't know,
maybe there's something funny about those.
Maybe they're 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 and down,
so 60 miles times 30 days,
so 60, 180, 1,800 miles.
Like that's a drop in the bucket
compared to the, what, 85 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.
It worked for a month, what's gonna change?
So even if you start a perfect understanding of the system,
your own experience will make it drift.
I mean, that's a big concern.
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 would say is kind of the more technology savvy
ownership group to 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 your kind of immunization
against this kind of false risk assessment
might last longer,
but as folks who aren't as savvy about that
read the material and they compare that
to their personal experience,
I think there it's going to move more quickly.
So your work, the program that you've created at 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, 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.
It's philosophically interesting,
it's practically interesting.
So let's talk about safety.
How do you think we demonstrate,
you've 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
is part of that.
It's like here's the way we did the work,
that means that we were very thorough.
So if you believe that what we said
about this is the way we did it,
then you can have some confidence
that we were thorough in the engineering work
we put into the system.
And then on top of that,
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
worked the way we thought they did,
statistically and to whatever degree
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 I think the way we'll ultimately
convey this to the public
is there'll be clearly some conversation
with the public about it,
but we'll kind of invoke the kind of the trusted nodes
and that we'll spend more time
being able to go into more depth with folks like NHTSA
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 show enough work to them
that they're convinced,
then I think we're 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's probably no,
but just in case,
do you think there exists a metric?
So currently people have been using
number of disengagements.
And it quickly turns into a marketing scheme
to sort of you alter the experiments you run to adjust.
I think you've spoken that you don't like.
Don't love it.
No, in fact, 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 could demonstrate safety
outside of fatalities?
So I do.
And I think that it won't be just one number.
So as we are internally grappling with this,
and at some point 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 in the regulatory role
and then ultimately the public
that we have confidence that our system
will work better than that.
And so these individual metrics
will kind of 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 ultimately kind of casualty dollars
that people aren't having to pay to get their car fixed.
And I do think that in aviation
they look at a kind of an event pyramid
where a crash is at the top of that
and that's the worst event obviously
and then there's injuries and near miss events and whatnot
and violation of operating procedures
and you kind of build a statistical model
of the relevance of the low severity things
or the high severity things.
And I think that's something
where we'll be able to look at as well
because an event per 85 million miles
is statistically a difficult thing
even at the scale of the U.S.
to kind of compare directly.
And that event fatality that's connected
to an autonomous vehicle is significantly
at least currently magnified
in the amount of attention it gets.
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 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?
I think you let them experience it, right?
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 that's perfectly reasonable.
And, but at the same time,
it's clear there's an opportunity to make the road 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 understand that it's safe
and are able to use in their daily lives,
I think it's one of these things
that will just be obvious.
And I've seen this practically in demonstrations
that I've given where I've had people come in
and they're very skeptical.
Again, in a vehicle, my favorite one
is taking somebody out on the freeway
and we're on the 101 driving at 65 miles an hour.
And after 10 minutes, they kind of turn and ask,
is that all it does?
And you're like, it's a self driving car.
I'm not sure exactly what you thought it would do, right?
But it becomes mundane,
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 those electrons
being pushed down a wire from wherever it was
and being generated.
It's like, I just get annoyed if it doesn't work, right?
And what I value is the fact
that I can do other things in this space.
I can see my colleagues.
I can read stuff on a paper.
I can not be afraid of the dark.
And I think that's what we want this technology to be like
is it's in the background
and people get to have those life experiences
and do so safely.
So putting this technology in the hands of people
speaks to scale of deployment, right?
So what do you think the dreaded question about the future
because nobody can predict the future,
but just maybe speak poetically
about when do you think we'll see a large scale deployment
of autonomous vehicles, 10,000, those kinds of numbers?
We'll see that within 10 years.
I'm pretty confident.
What's an impressive scale?
What moment, so you've done the DARPA challenge
where there's one vehicle.
At which moment does it become, wow, this is serious scale?
So I think the moment it gets serious
is when we really do have a driverless vehicle
operating on public roads
and that we can do that kind of continuously.
Without a safety driver.
Without a safety driver in the vehicle.
I think at that moment,
we've kind of crossed the zero to one threshold.
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 a useful product out in the world?
And I think that is really,
at that point it moves from
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 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 routes in the urban environment?
I think it's gonna be urban, suburban type environments.
Yeah, with Aurora, when we thought about how to tackle this,
it was kind of in vogue to think about trucking
as opposed to urban driving.
And 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 lanes are a little wider, et cetera.
And I think that that intuition is pretty good,
except we don't really care about most of the time.
We care about all of the time.
And when you're driving on a freeway with a truck,
say 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 those challenges that you see occur more rarely,
so you don't get to learn as quickly.
And they're 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 if two vehicles crash
at 25 miles per hour, it's not good,
but probably everybody walks away.
And those events where there's the possibility
for that occurring happen 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 need to get from one place to another.
And once we've got that solved,
then the freeway driving part of this just falls out.
But we're able to learn more safely,
more quickly in the urban environment.
So 10 years and then scale 20, 30 year,
who knows if a sufficiently compelling experience
is created, it could be faster and slower.
Do you think there could be breakthroughs
and what kind of breakthroughs might there be
that completely change that timeline?
Again, not only am I asking you to predict the future,
I'm asking you to predict breakthroughs
that haven't happened yet.
So what's the, I think another way to ask that
would be if I could wave a magic wand,
what part of the system would I make work today
to accelerate it as quickly as possible?
Don't say infrastructure, please don't say infrastructure.
No, it's definitely not infrastructure.
It's really that perception forecasting capability.
So 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.
Are you, in terms of staying up at night,
are you mostly bothered by cars, pedestrians or cyclists?
So I worry most about the vulnerable road users
about the combination of cyclists and cars, right?
Or cyclists and pedestrians because they're not in armor.
The cars, they're bigger, they've got protection
for the people and so the ultimate risk is lower there.
Whereas a pedestrian or a cyclist,
they're out on the road and they don't have any protection
and so 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, does that worry you
of how, from a technical perspective, how we solve that?
Because as humans, the way we solve that
is 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 there's two different concepts there.
So one is, am I worried that because these vehicles
are self driving, people will 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.
Even if I'm annoyed, I'm not gonna just drive
through a person stood in the road.
And so I think today people can take advantage of this
and you do see some people do it.
I guess there's an incremental risk
because maybe they have lower confidence
that I'm gonna see them than they might have
for an automated vehicle and so maybe that shifts
it a little bit.
But I think people don't wanna get hit by cars.
And so I think that I'm not that worried
about people walking out of the 101
and 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.
It's kind of just move forward slowly
and if somebody's really close then stop.
And I think that that probably can be replicated
pretty easily and particularly given that
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 it's not something we're thinking about
in any serious way.
And probably that's less an algorithm problem
and more creating a human experience.
So the HCI people that create a visual display
that you're pleasantly as a pedestrian
nudged out of the way, that's an experience problem,
not an algorithm problem.
Who's the main competitor to Aurora today?
And how do you outcompete them in the long run?
So we really focus a lot on what we're doing here.
I think that, 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 don't spend a whole lot of time
thinking tactically about who's out there
and how do we beat 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 cul de sacs are to some degree
and 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 we're able to bring in early on,
because of the great investors we have,
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 early investment
and continuing investment in 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.
Thank you very much. Really enjoyed it.