Lex Fridman Podcast - #28 - Chris Urmson: Self-Driving Cars at Aurora, Google, CMU, and DARPA

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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.

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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?


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.