The following is a conversation with Chris Ermsen.
He was the 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 roboticist and autonomous vehicle
experts in the world and a long time 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 Freedman spelled FRID MAN.
And now, here's my conversation with Chris Ermsen.
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 we
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
in 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 impossible,
completely impossible?
We thought it was going to be hard.
We didn't know how we're 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,
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.
That 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 there's a bunch of engineering work
to get the vehicle so that we could control 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 hiding it.
You knew approximately, but you didn't actually
know the route that's going to be taken.
That's right, 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 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 of 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 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 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 one of the big step there.
For the Urban Challenge, one of the big technological
innovations there was the multi beam LiDAR.
And be able to generate high resolution,
mid to long range 3D models the world,
and use that for understanding the world around the vehicle.
And that was really kind of a game changing technology.
And 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 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?
Well, 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 it was very cool.
So for the Urban Challenge, those already maps
constructed offline in general?
OK.
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 nonsphericalness of the Earth,
but also kind of the actually, and I think when 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.
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 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 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 assumption.
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, or 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
as much larger than 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'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 euphorcy
as a 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.
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 Lyder 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 Lyder in the sensor suite
in the near term?
So I think it's essential.
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 want to ask, let's see if we can untangle it,
is what are your thoughts on the Elon Musk provocative statement
that Lyder is a crutch, that is 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.
Yes, in terms of sensors, right?
So there's an example that we all
go do it at many of us every day.
In terms of Lyder 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 technology, 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
OK with using lasers, because that's whatever.
But I am OK with using an 8 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 automotive companies.
And if there's one word that comes up more often than anything,
it's cost and 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 4 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 cost 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.
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 cost
out 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 below materials.
Yeah, if the sensor suite works, extra value is provided.
Thereby, you don't need to drive cost down to zero.
It's a basic economics.
You've talked about your intuition
at level two autonomy is problematic because
of the human factor of vigilance, decrement, complacency,
overtrust, and so on, just us being human.
With the overtrust system, we start doing even more
so partaking in the secondary activities like smartphone
and so on.
Have your views evolved on this point in either direction?
Can you speak to it?
So 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 that's
and we should be pushing on that.
Level two systems are systems where
the vehicle is controlling two axes,
so breaking and thrall 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'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 over trust 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 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?
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 issue,
economic challenges we were just talking about,
that technology path will, these level two driver system
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 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 cost safety, critical safety concerns.
You believe that it needs to diverge at this point
into different, 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 so the way to think about this
is for a four collision mitigation braking system,
if 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 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% 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 the 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 overtrust 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 one on one 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 a little 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.
That's a drop in the bucket compared to the one,
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 you're 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 that it's gonna 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 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.
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 sound bite 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 the way we did the work.
That means that we were very thorough.
So, 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 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
work the way we thought they did, statistically
and to whatever degree we can demonstrate that
both in some combination of simulation,
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 that 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
a number of disengagement.
And it quickly turns into a marketing scheme
to sort of you alter the experiments you run to.
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 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
and 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 US 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 you get.
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 it'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.
And they get in the vehicle.
My favorite one is taking somebody out on the freeway
and we're on the one on one 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 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 to 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.
We...
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 a,
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.
You know, with Aurora,
when we thought about how to tackle this,
it was kind of invoke 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
to do with you, that's just an incredible amount
of kinetic energy.
And so when that goes wrong, it goes really wrong.
And that 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 there,
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 then once we've got that solved,
then the kind of 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 years.
I mean, who knows 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 breakthroughs might there be
that completely change that timeline?
Again, not only am I asking 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?
Right.
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 happening and what will happen
for the next five seconds around a vehicle
on the roadway, that would accelerate things
pretty dramatically.
Are you interested in 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?
Just 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 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 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 on 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 even if I'm annoyed, I'm not gonna just drive
through a person stood on the road.
Right.
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 going to 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 want to get hit by cars.
And so I think that I'm not that worried
about people walking out of the one on one
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.
Right?
It's kind of just move forward slowly
and if somebody's really close and stop.
And I think that that probably can be replicated
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 it's not something we're thinking
about in any serious way.
And probably that's less an algorithm problem
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 out compete 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
like 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 leisure 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 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.
I hope you enjoyed it.