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 The following is a conversation with Chris Sampson.

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 He was a CTO of the Google self driving car team,

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 a key engineer and leader behind the Carnegie Mellon

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 University autonomous vehicle entries in the DARPA Grand

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 Challenges and the winner of the DARPA Urban Challenge.

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 Today, he's the CEO of Aurora Innovation, an autonomous

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 vehicle software company.

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 He started with Sterling Anderson,

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 who was the former director of Tesla Autopilot,

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 and drew back now, Uber's former autonomy and perception lead.

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 Chris is one of the top roboticists and autonomous

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 vehicle experts in the world, and a longtime voice

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 of reason in a space that is shrouded

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 in both mystery and hype.

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 He both acknowledges the incredible challenges

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 involved in solving the problem of autonomous driving

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 and is working hard to solve it.

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 This is the Artificial Intelligence podcast.

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 If you enjoy it, subscribe on YouTube,

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 give it five stars on iTunes, support it on Patreon,

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 or simply connect with me on Twitter

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 at Lex Friedman, spelled F R I D M A N.

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 And now, here's my conversation with Chris Sampson.

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 You were part of both the DARPA Grand Challenge

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 and the DARPA Urban Challenge teams

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 at CMU with Red Whitaker.

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 What technical or philosophical things

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 have you learned from these races?

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 I think the high order bit was that it could be done.

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 I think that was the thing that was

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 incredible about the first of the Grand Challenges,

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 that I remember I was a grad student at Carnegie Mellon,

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 and there was kind of this dichotomy of it

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 seemed really hard, so that would

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 be cool and interesting.

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 But at the time, we were the only robotics institute around,

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 and so if we went into it and fell on our faces,

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 that would be embarrassing.

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 So I think just having the will to go do it,

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 to try to do this thing that at the time

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 was marked as darn near impossible,

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 and then after a couple of tries,

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 be able to actually make it happen,

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 I think that was really exciting.

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 But at which point did you believe it was possible?

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 Did you from the very beginning?

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 Did you personally?

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 Because you're one of the lead engineers.

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 You actually had to do a lot of the work.

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 Yeah, I was the technical director there,

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 and did a lot of the work, along with a bunch

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 of other really good people.

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 Did I believe it could be done?

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 Yeah, of course.

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 Why would you go do something you thought

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 was completely impossible?

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 We thought it was going to be hard.

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 We didn't know how we were going to be able to do it.

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 We didn't know if we'd be able to do it the first time.

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 Turns out we couldn't.

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 That, yeah, I guess you have to.

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 I think there's a certain benefit to naivete, right?

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 That if you don't know how hard something really is,

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 you try different things, and it gives you an opportunity

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 that others who are wiser maybe don't have.

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 What were the biggest pain points?

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 Mechanical, sensors, hardware, software,

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 algorithms for mapping, localization,

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 just general perception, control?

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 Like hardware, software, first of all?

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 I think that's the joy of this field, is that it's all hard

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 and that you have to be good at each part of it.

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 So for the urban challenges, if I look back at it from today,

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 it should be easy today, that it was a static world.

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 There weren't other actors moving through it,

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 is what that means.

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 It was out in the desert, so you get really good GPS.

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 So that went, and we could map it roughly.

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 And so in retrospect now, it's within the realm of things

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 we could do back then.

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 Just actually getting the vehicle and the,

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 there's a bunch of engineering work

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 to get the vehicle so that we could control it and drive it.

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 That's still a pain today, but it was even more so back then.

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 And then the uncertainty of exactly what they wanted us to do

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 was part of the challenge as well.

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 Right, you didn't actually know the track heading in here.

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 You knew approximately, but you didn't actually

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 know the route that was going to be taken.

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 That's right, we didn't know the route.

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 We didn't even really, the way the rules had been described,

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 you had to kind of guess.

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 So if you think back to that challenge,

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 the idea was that the government would give us,

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 the DARPA would give us a set of waypoints

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 and kind of the width that you had to stay within

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 between the line that went between each of those waypoints.

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 And so the most devious thing they could have done

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 is set a kilometer wide corridor across a field

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 of scrub brush and rocks and said, go figure it out.

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 Fortunately, it really, it turned into basically driving

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 along a set of trails, which is much more relevant

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 to the application they were looking for.

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 But no, it was a hell of a thing back in the day.

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 So the legend, Red, was kind of leading that effort

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 in terms of just broadly speaking.

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 So you're a leader now.

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 What have you learned from Red about leadership?

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 I think there's a couple things.

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 One is go and try those really hard things.

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 That's where there is an incredible opportunity.

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 I think the other big one, though,

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 is to see people for who they can be, not who they are.

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 It's one of the things that I actually,

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 one of the deepest lessons I learned from Red

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 was that he would look at undergraduates

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 or graduate students and empower them to be leaders,

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 to have responsibility, to do great things

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 that I think another person might look at them

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 and think, oh, well, that's just an undergraduate student.

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 What could they know?

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 And so I think that kind of trust but verify,

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 have confidence in what people can become,

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 I think is a really powerful thing.

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 So through that, let's just fast forward through the history.

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 Can you maybe talk through the technical evolution

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 of autonomous vehicle systems

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 from the first two Grand Challenges to the Urban Challenge

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 to today, are there major shifts in your mind

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 or is it the same kind of technology just made more robust?

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 I think there's been some big, big steps.

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 So for the Grand Challenge,

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 the real technology that unlocked that was HD mapping.

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 Prior to that, a lot of the off road robotics work

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 had been done without any real prior model

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 of what the vehicle was going to encounter.

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 And so that innovation that the fact that we could get

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 decimeter resolution models was really a big deal.

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 And that allowed us to kind of bound the complexity

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 of the driving problem the vehicle had

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 and allowed it to operate at speed

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 because we could assume things about the environment

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 that it was going to encounter.

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 So that was the big step there.

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 For the Urban Challenge,

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 one of the big technological innovations there

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 was the multi beam LIDAR

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 and being able to generate high resolution,

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 mid to long range 3D models of the world

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 and use that for understanding the world around the vehicle.

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 And that was really kind of a game changing technology.

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 In parallel with that,

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 we saw a bunch of other technologies

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 that had been kind of converging

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 half their day in the sun.

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 So Bayesian estimation had been,

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 SLAM had been a big field in robotics.

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 You would go to a conference a couple of years before that

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 and every paper would effectively have SLAM somewhere in it.

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 And so seeing that the Bayesian estimation techniques

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 play out on a very visible stage,

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 I thought that was pretty exciting to see.

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 And mostly SLAM was done based on LIDAR at that time.

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 Yeah, and in fact, we weren't really doing SLAM per se

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 in real time because we had a model ahead of time,

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 we had a roadmap, but we were doing localization.

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 And we were using the LIDAR or the cameras

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 depending on who exactly was doing it

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 to localize to a model of the world.

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 And I thought that was a big step

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 from kind of naively trusting GPS, INS before that.

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 And again, lots of work had been going on in this field.

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 Certainly this was not doing anything

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 particularly innovative in SLAM or in localization,

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 but it was seeing that technology necessary

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 in a real application on a big stage,

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 I thought was very cool.

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 So for the urban challenge,

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 those are already maps constructed offline in general.

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 And did people do that individually,

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 did individual teams do it individually

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 so they had their own different approaches there

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 or did everybody kind of share that information

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 at least intuitively?

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 So DARPA gave all the teams a model of the world, a map.

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 And then one of the things that we had to figure out

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 back then was, and it's still one of these things

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 that trips people up today

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 is actually the coordinate system.

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 So you get a latitude longitude

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 and to so many decimal places,

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 you don't really care about kind of the ellipsoid

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 of the earth that's being used.

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 But when you want to get to 10 centimeter

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 or centimeter resolution,

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 you care whether the coordinate system is NADS 83

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 or WGS 84 or these are different ways to describe

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 both the kind of non sphericalness of the earth,

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 but also kind of the, I think,

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 I can't remember which one,

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 the tectonic shifts that are happening

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 and how to transform the global datum as a function of that.

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 So getting a map and then actually matching it to reality

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 to centimeter resolution, that was kind of interesting

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 and fun back then.

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 So how much work was the perception doing there?

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 So how much were you relying on localization based on maps

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 without using perception to register to the maps?

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 And I guess the question is how advanced

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 was perception at that point?

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 It's certainly behind where we are today, right?

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 We're more than a decade since the urban challenge.

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 But the core of it was there.

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 That we were tracking vehicles.

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 We had to do that at 100 plus meter range

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 because we had to merge with other traffic.

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 We were using, again, Bayesian estimates

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 for state of these vehicles.

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 We had to deal with a bunch of the problems

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 that you think of today,

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 of predicting where that vehicle's going to be

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 a few seconds into the future.

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 We had to deal with the fact

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 that there were multiple hypotheses for that

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 because a vehicle at an intersection might be going right

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 or it might be going straight

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 or it might be making a left turn.

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 And we had to deal with the challenge of the fact

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 that our behavior was going to impact the behavior

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 of that other operator.

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 And we did a lot of that in relatively naive ways,

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 but it kind of worked.

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 Still had to have some kind of solution.

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 And so where does that, 10 years later,

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 where does that take us today

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 from that artificial city construction

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 to real cities to the urban environment?

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 Yeah, I think the biggest thing

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 is that the actors are truly unpredictable.

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 That most of the time, the drivers on the road,

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 the other road users are out there behaving well,

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 but every once in a while they're not.

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 The variety of other vehicles is, you have all of them.

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 In terms of behavior, in terms of perception, or both?

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 Both.

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 Back then we didn't have to deal with cyclists,

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 we didn't have to deal with pedestrians,

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 didn't have to deal with traffic lights.

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 The scale over which that you have to operate is now

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 is much larger than the air base

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 that we were thinking about back then.

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 So what, easy question,

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 what do you think is the hardest part about driving?

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 Easy question.

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 Yeah, no, I'm joking.

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 I'm sure nothing really jumps out at you as one thing,

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 but in the jump from the urban challenge to the real world,

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 is there something that's a particular,

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 you foresee as very serious, difficult challenge?

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 I think the most fundamental difference

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 is that we're doing it for real.

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 That in that environment,

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 it was both a limited complexity environment

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 because certain actors weren't there,

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 because the roads were maintained,

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 there were barriers keeping people separate

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 from robots at the time,

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 and it only had to work for 60 miles.

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 Which, looking at it from 2006,

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 it had to work for 60 miles, right?

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 Looking at it from now,

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 we want things that will go and drive

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 for half a million miles,

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 and it's just a different game.

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 So how important,

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 you said LiDAR came into the game early on,

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 and it's really the primary driver

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 of autonomous vehicles today as a sensor.

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 So how important is the role of LiDAR

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 in the sensor suite in the near term?

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 So I think it's essential.

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 I believe, but I also believe that cameras are essential,

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 and I believe the radar is essential.

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 I think that you really need to use

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 the composition of data from these different sensors

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 if you want the thing to really be robust.

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 The question I wanna ask,

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 let's see if we can untangle it,

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 is what are your thoughts on the Elon Musk

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 provocative statement that LiDAR is a crutch,

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 that it's a kind of, I guess, growing pains,

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 and that much of the perception task

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 can be done with cameras?

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 So I think it is undeniable

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 that people walk around without lasers in their foreheads,

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 and they can get into vehicles and drive them,

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 and so there's an existence proof

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 that you can drive using passive vision.

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 No doubt, can't argue with that.

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 In terms of sensors, yeah, so there's proof.

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 Yeah, in terms of sensors, right?

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 So there's an example that we all go do it,

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 many of us every day.

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 In terms of LiDAR being a crutch, sure.

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 But in the same way that the combustion engine

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 was a crutch on the path to an electric vehicle,

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 in the same way that any technology ultimately gets

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 replaced by some superior technology in the future,

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 and really the way that I look at this

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 is that the way we get around on the ground,

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 the way that we use transportation is broken,

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 and that we have this, I think the number I saw this morning,

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 37,000 Americans killed last year on our roads,

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 and that's just not acceptable.

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 And so any technology that we can bring to bear

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 that accelerates this self driving technology

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 coming to market and saving lives

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 is technology we should be using.

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 And it feels just arbitrary to say,

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 well, I'm not okay with using lasers

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 because that's whatever,

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 but I am okay with using an eight megapixel camera

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 or a 16 megapixel camera.

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 These are just bits of technology,

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 and we should be taking the best technology

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 from the tool bin that allows us to go and solve a problem.

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 The question I often talk to, well, obviously you do as well,

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 to sort of automotive companies,

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 and if there's one word that comes up more often

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 than anything, it's cost, and trying to drive costs down.

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 So while it's true that it's a tragic number, the 37,000,

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 the question is, and I'm not the one asking this question

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 because I hate this question,

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 but we want to find the cheapest sensor suite

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 that creates a safe vehicle.

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 So in that uncomfortable trade off,

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 do you foresee LiDAR coming down in cost in the future,

17:23.680 --> 17:26.680
 or do you see a day where level four autonomy

17:26.680 --> 17:29.880
 is possible without LiDAR?

17:29.880 --> 17:32.880
 I see both of those, but it's really a matter of time.

17:32.880 --> 17:36.040
 And I think really, maybe I would talk to the question

17:36.040 --> 17:37.840
 you asked about the cheapest sensor.

17:37.840 --> 17:40.360
 I don't think that's actually what you want.

17:40.360 --> 17:45.360
 What you want is a sensor suite that is economically viable.

17:45.680 --> 17:49.440
 And then after that, everything is about margin

17:49.440 --> 17:52.120
 and driving costs out of the system.

17:52.120 --> 17:55.360
 What you also want is a sensor suite that works.

17:55.360 --> 17:58.200
 And so it's great to tell a story about

17:59.600 --> 18:03.260
 how it would be better to have a self driving system

18:03.260 --> 18:08.040
 with a $50 sensor instead of a $500 sensor.

18:08.040 --> 18:10.520
 But if the $500 sensor makes it work

18:10.520 --> 18:14.760
 and the $50 sensor doesn't work, who cares?

18:15.680 --> 18:20.020
 So long as you can actually have an economic opportunity,

18:20.020 --> 18:21.520
 there's an economic opportunity there.

18:21.520 --> 18:23.760
 And the economic opportunity is important

18:23.760 --> 18:27.760
 because that's how you actually have a sustainable business

18:27.760 --> 18:31.120
 and that's how you can actually see this come to scale

18:31.120 --> 18:32.400
 and be out in the world.

18:32.400 --> 18:34.780
 And so when I look at LiDAR,

18:35.960 --> 18:38.880
 I see a technology that has no underlying

18:38.880 --> 18:42.420
 fundamentally expense to it, fundamental expense to it.

18:42.420 --> 18:46.080
 It's going to be more expensive than an imager

18:46.080 --> 18:50.360
 because CMOS processes or FAP processes

18:51.360 --> 18:55.080
 are dramatically more scalable than mechanical processes.

18:56.200 --> 18:58.320
 But we still should be able to drive costs down

18:58.320 --> 19:00.120
 substantially on that side.

19:00.120 --> 19:04.840
 And then I also do think that with the right business model

19:05.880 --> 19:07.560
 you can absorb more,

19:07.560 --> 19:09.480
 certainly more cost on the bill of materials.

19:09.480 --> 19:12.600
 Yeah, if the sensor suite works, extra value is provided,

19:12.600 --> 19:15.480
 thereby you don't need to drive costs down to zero.

19:15.480 --> 19:17.100
 It's the basic economics.

19:17.100 --> 19:18.820
 You've talked about your intuition

19:18.820 --> 19:22.200
 that level two autonomy is problematic

19:22.200 --> 19:25.920
 because of the human factor of vigilance,

19:25.920 --> 19:28.040
 decrement, complacency, over trust and so on,

19:28.040 --> 19:29.600
 just us being human.

19:29.600 --> 19:31.120
 We over trust the system,

19:31.120 --> 19:34.240
 we start doing even more so partaking

19:34.240 --> 19:37.180
 in the secondary activities like smartphones and so on.

19:38.680 --> 19:43.000
 Have your views evolved on this point in either direction?

19:43.000 --> 19:44.800
 Can you speak to it?

19:44.800 --> 19:47.480
 So, and I want to be really careful

19:47.480 --> 19:50.380
 because sometimes this gets twisted in a way

19:50.380 --> 19:53.040
 that I certainly didn't intend.

19:53.040 --> 19:58.040
 So active safety systems are a really important technology

19:58.040 --> 20:00.680
 that we should be pursuing and integrating into vehicles.

20:02.080 --> 20:04.280
 And there's an opportunity in the near term

20:04.280 --> 20:06.520
 to reduce accidents, reduce fatalities,

20:06.520 --> 20:10.320
 and we should be pushing on that.

20:11.960 --> 20:14.680
 Level two systems are systems

20:14.680 --> 20:18.080
 where the vehicle is controlling two axes.

20:18.080 --> 20:21.720
 So braking and throttle slash steering.

20:23.480 --> 20:25.680
 And I think there are variants of level two systems

20:25.680 --> 20:27.280
 that are supporting the driver.

20:27.280 --> 20:31.080
 That absolutely we should encourage to be out there.

20:31.080 --> 20:32.880
 Where I think there's a real challenge

20:32.880 --> 20:37.640
 is in the human factors part around this

20:37.640 --> 20:41.240
 and the misconception from the public

20:41.240 --> 20:43.600
 around the capability set that that enables

20:43.600 --> 20:45.640
 and the trust that they should have in it.

20:46.640 --> 20:50.000
 And that is where I kind of,

20:50.000 --> 20:52.920
 I'm actually incrementally more concerned

20:52.920 --> 20:54.440
 around level three systems

20:54.440 --> 20:58.440
 and how exactly a level two system is marketed and delivered

20:58.440 --> 21:01.840
 and how much effort people have put into those human factors.

21:01.840 --> 21:05.640
 So I still believe several things around this.

21:05.640 --> 21:09.440
 One is people will overtrust the technology.

21:09.440 --> 21:11.440
 We've seen over the last few weeks

21:11.440 --> 21:14.040
 a spate of people sleeping in their Tesla.

21:14.920 --> 21:19.920
 I watched an episode last night of Trevor Noah

21:19.920 --> 21:23.920
 talking about this and him,

21:23.920 --> 21:26.720
 this is a smart guy who has a lot of resources

21:26.720 --> 21:30.720
 at his disposal describing a Tesla as a self driving car

21:30.720 --> 21:33.480
 and that why shouldn't people be sleeping in their Tesla?

21:33.480 --> 21:36.560
 And it's like, well, because it's not a self driving car

21:36.560 --> 21:38.840
 and it is not intended to be

21:38.840 --> 21:43.840
 and these people will almost certainly die at some point

21:46.400 --> 21:48.040
 or hurt other people.

21:48.040 --> 21:50.080
 And so we need to really be thoughtful

21:50.080 --> 21:51.840
 about how that technology is described

21:51.840 --> 21:53.280
 and brought to market.

21:54.240 --> 21:59.240
 I also think that because of the economic challenges

21:59.240 --> 22:01.240
 we were just talking about,

22:01.240 --> 22:05.160
 that these level two driver assistance systems,

22:05.160 --> 22:07.280
 that technology path will diverge

22:07.280 --> 22:10.200
 from the technology path that we need to be on

22:10.200 --> 22:14.080
 to actually deliver truly self driving vehicles,

22:14.080 --> 22:16.920
 ones where you can get in it and drive it.

22:16.920 --> 22:20.800
 Can get in it and sleep and have the equivalent

22:20.800 --> 22:24.680
 or better safety than a human driver behind the wheel.

22:24.680 --> 22:27.520
 Because again, the economics are very different

22:28.480 --> 22:30.880
 in those two worlds and so that leads

22:30.880 --> 22:32.800
 to divergent technology.

22:32.800 --> 22:34.680
 So you just don't see the economics

22:34.680 --> 22:38.560
 of gradually increasing from level two

22:38.560 --> 22:41.600
 and doing so quickly enough

22:41.600 --> 22:44.480
 to where it doesn't cause safety, critical safety concerns.

22:44.480 --> 22:47.680
 You believe that it needs to diverge at this point

22:48.680 --> 22:50.800
 into basically different routes.

22:50.800 --> 22:55.560
 And really that comes back to what are those L2

22:55.560 --> 22:57.080
 and L1 systems doing?

22:57.080 --> 22:59.840
 And they are driver assistance functions

22:59.840 --> 23:04.400
 where the people that are marketing that responsibly

23:04.400 --> 23:08.000
 are being very clear and putting human factors in place

23:08.000 --> 23:12.440
 such that the driver is actually responsible for the vehicle

23:12.440 --> 23:15.160
 and that the technology is there to support the driver.

23:15.160 --> 23:19.880
 And the safety cases that are built around those

23:19.880 --> 23:24.040
 are dependent on that driver attention and attentiveness.

23:24.040 --> 23:28.000
 And at that point, you can kind of give up

23:29.160 --> 23:31.240
 to some degree for economic reasons,

23:31.240 --> 23:33.480
 you can give up on say false negatives.

23:34.800 --> 23:36.200
 And the way to think about this

23:36.200 --> 23:39.320
 is for a four collision mitigation braking system,

23:39.320 --> 23:43.960
 if it half the times the driver missed a vehicle

23:43.960 --> 23:46.080
 in front of it, it hit the brakes

23:46.080 --> 23:47.680
 and brought the vehicle to a stop,

23:47.680 --> 23:51.640
 that would be an incredible, incredible advance

23:51.640 --> 23:53.040
 in safety on our roads, right?

23:53.040 --> 23:55.000
 That would be equivalent to seat belts.

23:55.000 --> 23:56.600
 But it would mean that if that vehicle

23:56.600 --> 23:59.440
 wasn't being monitored, it would hit one out of two cars.

24:00.600 --> 24:05.120
 And so economically, that's a perfectly good solution

24:05.120 --> 24:06.280
 for a driver assistance system.

24:06.280 --> 24:07.240
 What you should do at that point,

24:07.240 --> 24:09.240
 if you can get it to work 50% of the time,

24:09.240 --> 24:10.520
 is drive the cost out of that

24:10.520 --> 24:13.320
 so you can get it on as many vehicles as possible.

24:13.320 --> 24:14.760
 But driving the cost out of it

24:14.760 --> 24:18.800
 doesn't drive up performance on the false negative case.

24:18.800 --> 24:21.440
 And so you'll continue to not have a technology

24:21.440 --> 24:25.680
 that could really be available for a self driven vehicle.

24:25.680 --> 24:28.440
 So clearly the communication,

24:28.440 --> 24:31.600
 and this probably applies to all four vehicles as well,

24:31.600 --> 24:34.440
 the marketing and communication

24:34.440 --> 24:37.040
 of what the technology is actually capable of,

24:37.040 --> 24:38.400
 how hard it is, how easy it is,

24:38.400 --> 24:41.000
 all that kind of stuff is highly problematic.

24:41.000 --> 24:45.640
 So say everybody in the world was perfectly communicated

24:45.640 --> 24:48.400
 and were made to be completely aware

24:48.400 --> 24:50.000
 of every single technology out there,

24:50.000 --> 24:52.840
 what it's able to do.

24:52.840 --> 24:54.120
 What's your intuition?

24:54.120 --> 24:56.880
 And now we're maybe getting into philosophical ground.

24:56.880 --> 25:00.000
 Is it possible to have a level two vehicle

25:00.000 --> 25:03.280
 where we don't over trust it?

25:04.680 --> 25:05.800
 I don't think so.

25:05.800 --> 25:10.800
 If people truly understood the risks and internalized it,

25:11.160 --> 25:14.320
 then sure, you could do that safely.

25:14.320 --> 25:16.160
 But that's a world that doesn't exist.

25:16.160 --> 25:17.520
 The people are going to,

25:18.720 --> 25:20.760
 if the facts are put in front of them,

25:20.760 --> 25:24.440
 they're gonna then combine that with their experience.

25:24.440 --> 25:28.360
 And let's say they're using an L2 system

25:28.360 --> 25:30.800
 and they go up and down the 101 every day

25:30.800 --> 25:32.720
 and they do that for a month.

25:32.720 --> 25:36.200
 And it just worked every day for a month.

25:36.200 --> 25:39.000
 Like that's pretty compelling at that point,

25:39.000 --> 25:41.800
 just even if you know the statistics,

25:41.800 --> 25:43.400
 you're like, well, I don't know,

25:43.400 --> 25:44.760
 maybe there's something funny about those.

25:44.760 --> 25:46.920
 Maybe they're driving in difficult places.

25:46.920 --> 25:49.840
 Like I've seen it with my own eyes, it works.

25:49.840 --> 25:52.400
 And the problem is that that sample size that they have,

25:52.400 --> 25:53.880
 so it's 30 miles up and down,

25:53.880 --> 25:56.360
 so 60 miles times 30 days,

25:56.360 --> 25:58.720
 so 60, 180, 1,800 miles.

25:58.720 --> 26:03.280
 Like that's a drop in the bucket

26:03.280 --> 26:07.640
 compared to the, what, 85 million miles between fatalities.

26:07.640 --> 26:11.400
 And so they don't really have a true estimate

26:11.400 --> 26:14.440
 based on their personal experience of the real risks,

26:14.440 --> 26:15.640
 but they're gonna trust it anyway,

26:15.640 --> 26:16.480
 because it's hard not to.

26:16.480 --> 26:18.640
 It worked for a month, what's gonna change?

26:18.640 --> 26:21.640
 So even if you start a perfect understanding of the system,

26:21.640 --> 26:24.160
 your own experience will make it drift.

26:24.160 --> 26:25.920
 I mean, that's a big concern.

26:25.920 --> 26:28.160
 Over a year, over two years even,

26:28.160 --> 26:29.440
 it doesn't have to be months.

26:29.440 --> 26:32.920
 And I think that as this technology moves

26:32.920 --> 26:37.760
 from what I would say is kind of the more technology savvy

26:37.760 --> 26:40.880
 ownership group to the mass market,

26:42.640 --> 26:44.600
 you may be able to have some of those folks

26:44.600 --> 26:46.280
 who are really familiar with technology,

26:46.280 --> 26:48.840
 they may be able to internalize it better.

26:48.840 --> 26:50.800
 And your kind of immunization

26:50.800 --> 26:53.360
 against this kind of false risk assessment

26:53.360 --> 26:54.280
 might last longer,

26:54.280 --> 26:58.680
 but as folks who aren't as savvy about that

26:58.680 --> 27:00.880
 read the material and they compare that

27:00.880 --> 27:02.160
 to their personal experience,

27:02.160 --> 27:07.160
 I think there it's going to move more quickly.

27:08.160 --> 27:11.280
 So your work, the program that you've created at Google

27:11.280 --> 27:16.280
 and now at Aurora is focused more on the second path

27:16.600 --> 27:18.480
 of creating full autonomy.

27:18.480 --> 27:20.880
 So it's such a fascinating,

27:20.880 --> 27:24.560
 I think it's one of the most interesting AI problems

27:24.560 --> 27:25.600
 of the century, right?

27:25.600 --> 27:28.280
 It's, I just talked to a lot of people,

27:28.280 --> 27:29.440
 just regular people, I don't know,

27:29.440 --> 27:31.720
 my mom, about autonomous vehicles,

27:31.720 --> 27:34.520
 and you begin to grapple with ideas

27:34.520 --> 27:38.080
 of giving your life control over to a machine.

27:38.080 --> 27:40.040
 It's philosophically interesting,

27:40.040 --> 27:41.760
 it's practically interesting.

27:41.760 --> 27:43.720
 So let's talk about safety.

27:43.720 --> 27:46.240
 How do you think we demonstrate,

27:46.240 --> 27:47.880
 you've spoken about metrics in the past,

27:47.880 --> 27:51.880
 how do you think we demonstrate to the world

27:51.880 --> 27:56.160
 that an autonomous vehicle, an Aurora system is safe?

27:56.160 --> 27:57.320
 This is one where it's difficult

27:57.320 --> 27:59.280
 because there isn't a soundbite answer.

27:59.280 --> 28:04.280
 That we have to show a combination of work

28:05.960 --> 28:08.360
 that was done diligently and thoughtfully,

28:08.360 --> 28:10.840
 and this is where something like a functional safety process

28:10.840 --> 28:11.680
 is part of that.

28:11.680 --> 28:14.360
 It's like here's the way we did the work,

28:15.280 --> 28:17.160
 that means that we were very thorough.

28:17.160 --> 28:20.040
 So if you believe that what we said

28:20.040 --> 28:21.440
 about this is the way we did it,

28:21.440 --> 28:22.720
 then you can have some confidence

28:22.720 --> 28:25.200
 that we were thorough in the engineering work

28:25.200 --> 28:26.920
 we put into the system.

28:26.920 --> 28:28.920
 And then on top of that,

28:28.920 --> 28:32.000
 to kind of demonstrate that we weren't just thorough,

28:32.000 --> 28:33.800
 we were actually good at what we did,

28:35.280 --> 28:38.200
 there'll be a kind of a collection of evidence

28:38.200 --> 28:40.440
 in terms of demonstrating that the capabilities

28:40.440 --> 28:42.920
 worked the way we thought they did,

28:42.920 --> 28:45.320
 statistically and to whatever degree

28:45.320 --> 28:47.280
 we can demonstrate that,

28:48.160 --> 28:50.320
 both in some combination of simulations,

28:50.320 --> 28:53.080
 some combination of unit testing

28:53.080 --> 28:54.640
 and decomposition testing,

28:54.640 --> 28:57.000
 and then some part of it will be on road data.

28:58.160 --> 29:02.680
 And I think the way we'll ultimately

29:02.680 --> 29:04.000
 convey this to the public

29:04.000 --> 29:06.760
 is there'll be clearly some conversation

29:06.760 --> 29:08.200
 with the public about it,

29:08.200 --> 29:12.040
 but we'll kind of invoke the kind of the trusted nodes

29:12.040 --> 29:13.880
 and that we'll spend more time

29:13.880 --> 29:17.280
 being able to go into more depth with folks like NHTSA

29:17.280 --> 29:19.720
 and other federal and state regulatory bodies

29:19.720 --> 29:22.080
 and kind of given that they are

29:22.080 --> 29:25.200
 operating in the public interest and they're trusted,

29:26.240 --> 29:28.640
 that if we can show enough work to them

29:28.640 --> 29:30.000
 that they're convinced,

29:30.000 --> 29:33.800
 then I think we're in a pretty good place.

29:33.800 --> 29:35.000
 That means you work with people

29:35.000 --> 29:36.920
 that are essentially experts at safety

29:36.920 --> 29:39.000
 to try to discuss and show.

29:39.000 --> 29:41.720
 Do you think, the answer's probably no,

29:41.720 --> 29:42.920
 but just in case,

29:42.920 --> 29:44.360
 do you think there exists a metric?

29:44.360 --> 29:46.320
 So currently people have been using

29:46.320 --> 29:48.200
 number of disengagements.

29:48.200 --> 29:50.120
 And it quickly turns into a marketing scheme

29:50.120 --> 29:54.280
 to sort of you alter the experiments you run to adjust.

29:54.280 --> 29:56.280
 I think you've spoken that you don't like.

29:56.280 --> 29:57.120
 Don't love it.

29:57.120 --> 29:59.680
 No, in fact, I was on the record telling DMV

29:59.680 --> 30:01.960
 that I thought this was not a great metric.

30:01.960 --> 30:05.280
 Do you think it's possible to create a metric,

30:05.280 --> 30:09.440
 a number that could demonstrate safety

30:09.440 --> 30:12.320
 outside of fatalities?

30:12.320 --> 30:13.440
 So I do.

30:13.440 --> 30:16.560
 And I think that it won't be just one number.

30:17.600 --> 30:21.280
 So as we are internally grappling with this,

30:21.280 --> 30:23.560
 and at some point we'll be able to talk

30:23.560 --> 30:25.040
 more publicly about it,

30:25.040 --> 30:28.520
 is how do we think about human performance

30:28.520 --> 30:29.840
 in different tasks,

30:29.840 --> 30:32.160
 say detecting traffic lights

30:32.160 --> 30:36.200
 or safely making a left turn across traffic?

30:37.680 --> 30:40.080
 And what do we think the failure rates are

30:40.080 --> 30:42.520
 for those different capabilities for people?

30:42.520 --> 30:44.760
 And then demonstrating to ourselves

30:44.760 --> 30:48.480
 and then ultimately folks in the regulatory role

30:48.480 --> 30:50.760
 and then ultimately the public

30:50.760 --> 30:52.400
 that we have confidence that our system

30:52.400 --> 30:54.760
 will work better than that.

30:54.760 --> 30:57.040
 And so these individual metrics

30:57.040 --> 31:00.720
 will kind of tell a compelling story ultimately.

31:01.760 --> 31:03.920
 I do think at the end of the day

31:03.920 --> 31:06.640
 what we care about in terms of safety

31:06.640 --> 31:11.320
 is life saved and injuries reduced.

31:12.160 --> 31:15.280
 And then ultimately kind of casualty dollars

31:16.440 --> 31:19.360
 that people aren't having to pay to get their car fixed.

31:19.360 --> 31:22.680
 And I do think that in aviation

31:22.680 --> 31:25.880
 they look at a kind of an event pyramid

31:25.880 --> 31:28.600
 where a crash is at the top of that

31:28.600 --> 31:30.440
 and that's the worst event obviously

31:30.440 --> 31:34.240
 and then there's injuries and near miss events and whatnot

31:34.240 --> 31:37.320
 and violation of operating procedures

31:37.320 --> 31:40.160
 and you kind of build a statistical model

31:40.160 --> 31:44.440
 of the relevance of the low severity things

31:44.440 --> 31:45.280
 or the high severity things.

31:45.280 --> 31:46.120
 And I think that's something

31:46.120 --> 31:48.200
 where we'll be able to look at as well

31:48.200 --> 31:51.840
 because an event per 85 million miles

31:51.840 --> 31:54.440
 is statistically a difficult thing

31:54.440 --> 31:56.800
 even at the scale of the U.S.

31:56.800 --> 31:59.360
 to kind of compare directly.

31:59.360 --> 32:02.240
 And that event fatality that's connected

32:02.240 --> 32:07.240
 to an autonomous vehicle is significantly

32:07.440 --> 32:09.160
 at least currently magnified

32:09.160 --> 32:12.320
 in the amount of attention it gets.

32:12.320 --> 32:15.080
 So that speaks to public perception.

32:15.080 --> 32:16.720
 I think the most popular topic

32:16.720 --> 32:19.480
 about autonomous vehicles in the public

32:19.480 --> 32:23.080
 is the trolley problem formulation, right?

32:23.080 --> 32:27.000
 Which has, let's not get into that too much

32:27.000 --> 32:29.600
 but is misguided in many ways.

32:29.600 --> 32:32.320
 But it speaks to the fact that people are grappling

32:32.320 --> 32:36.160
 with this idea of giving control over to a machine.

32:36.160 --> 32:41.160
 So how do you win the hearts and minds of the people

32:41.560 --> 32:44.600
 that autonomy is something that could be a part

32:44.600 --> 32:45.520
 of their lives?

32:45.520 --> 32:47.640
 I think you let them experience it, right?

32:47.640 --> 32:50.440
 I think it's right.

32:50.440 --> 32:52.800
 I think people should be skeptical.

32:52.800 --> 32:55.680
 I think people should ask questions.

32:55.680 --> 32:57.000
 I think they should doubt

32:57.000 --> 33:00.120
 because this is something new and different.

33:00.120 --> 33:01.880
 They haven't touched it yet.

33:01.880 --> 33:03.640
 And I think that's perfectly reasonable.

33:03.640 --> 33:07.320
 And, but at the same time,

33:07.320 --> 33:09.320
 it's clear there's an opportunity to make the road safer.

33:09.320 --> 33:12.440
 It's clear that we can improve access to mobility.

33:12.440 --> 33:14.960
 It's clear that we can reduce the cost of mobility.

33:16.640 --> 33:19.480
 And that once people try that

33:19.480 --> 33:22.720
 and understand that it's safe

33:22.720 --> 33:24.440
 and are able to use in their daily lives,

33:24.440 --> 33:25.280
 I think it's one of these things

33:25.280 --> 33:28.040
 that will just be obvious.

33:28.040 --> 33:32.240
 And I've seen this practically in demonstrations

33:32.240 --> 33:35.560
 that I've given where I've had people come in

33:35.560 --> 33:38.840
 and they're very skeptical.

33:38.840 --> 33:40.440
 Again, in a vehicle, my favorite one

33:40.440 --> 33:42.560
 is taking somebody out on the freeway

33:42.560 --> 33:46.000
 and we're on the 101 driving at 65 miles an hour.

33:46.000 --> 33:48.400
 And after 10 minutes, they kind of turn and ask,

33:48.400 --> 33:49.480
 is that all it does?

33:49.480 --> 33:52.080
 And you're like, it's a self driving car.

33:52.080 --> 33:54.840
 I'm not sure exactly what you thought it would do, right?

33:54.840 --> 33:57.920
 But it becomes mundane,

33:58.840 --> 34:01.480
 which is exactly what you want a technology

34:01.480 --> 34:02.720
 like this to be, right?

34:02.720 --> 34:07.280
 We don't really, when I turn the light switch on in here,

34:07.280 --> 34:12.000
 I don't think about the complexity of those electrons

34:12.000 --> 34:14.200
 being pushed down a wire from wherever it was

34:14.200 --> 34:15.240
 and being generated.

34:15.240 --> 34:19.080
 It's like, I just get annoyed if it doesn't work, right?

34:19.080 --> 34:21.400
 And what I value is the fact

34:21.400 --> 34:23.080
 that I can do other things in this space.

34:23.080 --> 34:24.560
 I can see my colleagues.

34:24.560 --> 34:26.160
 I can read stuff on a paper.

34:26.160 --> 34:29.200
 I can not be afraid of the dark.

34:30.360 --> 34:33.320
 And I think that's what we want this technology to be like

34:33.320 --> 34:34.640
 is it's in the background

34:34.640 --> 34:37.120
 and people get to have those life experiences

34:37.120 --> 34:38.440
 and do so safely.

34:38.440 --> 34:42.160
 So putting this technology in the hands of people

34:42.160 --> 34:46.320
 speaks to scale of deployment, right?

34:46.320 --> 34:50.880
 So what do you think the dreaded question about the future

34:50.880 --> 34:53.560
 because nobody can predict the future,

34:53.560 --> 34:57.240
 but just maybe speak poetically

34:57.240 --> 35:00.880
 about when do you think we'll see a large scale deployment

35:00.880 --> 35:05.880
 of autonomous vehicles, 10,000, those kinds of numbers?

35:06.680 --> 35:08.240
 We'll see that within 10 years.

35:09.240 --> 35:10.240
 I'm pretty confident.

35:14.040 --> 35:16.040
 What's an impressive scale?

35:16.040 --> 35:19.200
 What moment, so you've done the DARPA challenge

35:19.200 --> 35:20.440
 where there's one vehicle.

35:20.440 --> 35:23.960
 At which moment does it become, wow, this is serious scale?

35:23.960 --> 35:26.520
 So I think the moment it gets serious

35:26.520 --> 35:31.520
 is when we really do have a driverless vehicle

35:32.240 --> 35:34.120
 operating on public roads

35:35.000 --> 35:37.960
 and that we can do that kind of continuously.

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 Without a safety driver.

35:38.880 --> 35:40.440
 Without a safety driver in the vehicle.

35:40.440 --> 35:41.560
 I think at that moment,

35:41.560 --> 35:44.400
 we've kind of crossed the zero to one threshold.

35:45.920 --> 35:50.200
 And then it is about how do we continue to scale that?

35:50.200 --> 35:53.960
 How do we build the right business models?

35:53.960 --> 35:56.320
 How do we build the right customer experience around it

35:56.320 --> 35:59.960
 so that it is actually a useful product out in the world?

36:00.960 --> 36:03.600
 And I think that is really,

36:03.600 --> 36:05.920
 at that point it moves from

36:05.920 --> 36:09.200
 what is this kind of mixed science engineering project

36:09.200 --> 36:12.360
 into engineering and commercialization

36:12.360 --> 36:15.840
 and really starting to deliver on the value

36:15.840 --> 36:20.680
 that we all see here and actually making that real in the world.

36:20.680 --> 36:22.240
 What do you think that deployment looks like?

36:22.240 --> 36:26.440
 Where do we first see the inkling of no safety driver,

36:26.440 --> 36:28.600
 one or two cars here and there?

36:28.600 --> 36:29.800
 Is it on the highway?

36:29.800 --> 36:33.160
 Is it in specific routes in the urban environment?

36:33.160 --> 36:36.920
 I think it's gonna be urban, suburban type environments.

36:37.880 --> 36:41.560
 Yeah, with Aurora, when we thought about how to tackle this,

36:41.560 --> 36:45.040
 it was kind of in vogue to think about trucking

36:46.040 --> 36:47.800
 as opposed to urban driving.

36:47.800 --> 36:51.280
 And again, the human intuition around this

36:51.280 --> 36:55.400
 is that freeways are easier to drive on

36:57.080 --> 36:59.280
 because everybody's kind of going in the same direction

36:59.280 --> 37:01.560
 and lanes are a little wider, et cetera.

37:01.560 --> 37:03.320
 And I think that that intuition is pretty good,

37:03.320 --> 37:06.040
 except we don't really care about most of the time.

37:06.040 --> 37:08.400
 We care about all of the time.

37:08.400 --> 37:10.880
 And when you're driving on a freeway with a truck,

37:10.880 --> 37:13.440
 say 70 miles an hour,

37:14.600 --> 37:16.240
 and you've got 70,000 pound load with you,

37:16.240 --> 37:18.880
 that's just an incredible amount of kinetic energy.

37:18.880 --> 37:21.440
 And so when that goes wrong, it goes really wrong.

37:22.640 --> 37:27.640
 And those challenges that you see occur more rarely,

37:27.800 --> 37:31.120
 so you don't get to learn as quickly.

37:31.120 --> 37:34.720
 And they're incrementally more difficult than urban driving,

37:34.720 --> 37:37.440
 but they're not easier than urban driving.

37:37.440 --> 37:41.640
 And so I think this happens in moderate speed

37:41.640 --> 37:45.280
 urban environments because if two vehicles crash

37:45.280 --> 37:48.120
 at 25 miles per hour, it's not good,

37:48.120 --> 37:50.120
 but probably everybody walks away.

37:51.080 --> 37:53.720
 And those events where there's the possibility

37:53.720 --> 37:55.800
 for that occurring happen frequently.

37:55.800 --> 37:58.000
 So we get to learn more rapidly.

37:58.000 --> 38:01.360
 We get to do that with lower risk for everyone.

38:02.520 --> 38:04.360
 And then we can deliver value to people

38:04.360 --> 38:05.880
 that need to get from one place to another.

38:05.880 --> 38:08.160
 And once we've got that solved,

38:08.160 --> 38:11.320
 then the freeway driving part of this just falls out.

38:11.320 --> 38:13.080
 But we're able to learn more safely,

38:13.080 --> 38:15.200
 more quickly in the urban environment.

38:15.200 --> 38:18.760
 So 10 years and then scale 20, 30 year,

38:18.760 --> 38:22.040
 who knows if a sufficiently compelling experience

38:22.040 --> 38:24.400
 is created, it could be faster and slower.

38:24.400 --> 38:27.160
 Do you think there could be breakthroughs

38:27.160 --> 38:29.920
 and what kind of breakthroughs might there be

38:29.920 --> 38:32.400
 that completely change that timeline?

38:32.400 --> 38:35.360
 Again, not only am I asking you to predict the future,

38:35.360 --> 38:37.360
 I'm asking you to predict breakthroughs

38:37.360 --> 38:38.360
 that haven't happened yet.

38:38.360 --> 38:41.440
 So what's the, I think another way to ask that

38:41.440 --> 38:44.320
 would be if I could wave a magic wand,

38:44.320 --> 38:46.720
 what part of the system would I make work today

38:46.720 --> 38:49.480
 to accelerate it as quickly as possible?

38:52.120 --> 38:54.200
 Don't say infrastructure, please don't say infrastructure.

38:54.200 --> 38:56.320
 No, it's definitely not infrastructure.

38:56.320 --> 39:00.600
 It's really that perception forecasting capability.

39:00.600 --> 39:04.840
 So if tomorrow you could give me a perfect model

39:04.840 --> 39:06.960
 of what's happened, what is happening

39:06.960 --> 39:09.200
 and what will happen for the next five seconds

39:10.360 --> 39:13.040
 around a vehicle on the roadway,

39:13.040 --> 39:15.360
 that would accelerate things pretty dramatically.

39:15.360 --> 39:17.600
 Are you, in terms of staying up at night,

39:17.600 --> 39:21.760
 are you mostly bothered by cars, pedestrians or cyclists?

39:21.760 --> 39:25.960
 So I worry most about the vulnerable road users

39:25.960 --> 39:28.480
 about the combination of cyclists and cars, right?

39:28.480 --> 39:31.960
 Or cyclists and pedestrians because they're not in armor.

39:31.960 --> 39:36.480
 The cars, they're bigger, they've got protection

39:36.480 --> 39:39.440
 for the people and so the ultimate risk is lower there.

39:41.080 --> 39:43.240
 Whereas a pedestrian or a cyclist,

39:43.240 --> 39:46.480
 they're out on the road and they don't have any protection

39:46.480 --> 39:49.720
 and so we need to pay extra attention to that.

39:49.720 --> 39:54.120
 Do you think about a very difficult technical challenge

39:55.720 --> 39:58.520
 of the fact that pedestrians,

39:58.520 --> 40:00.240
 if you try to protect pedestrians

40:00.240 --> 40:04.560
 by being careful and slow, they'll take advantage of that.

40:04.560 --> 40:09.040
 So the game theoretic dance, does that worry you

40:09.040 --> 40:12.480
 of how, from a technical perspective, how we solve that?

40:12.480 --> 40:14.560
 Because as humans, the way we solve that

40:14.560 --> 40:17.240
 is kind of nudge our way through the pedestrians

40:17.240 --> 40:20.000
 which doesn't feel, from a technical perspective,

40:20.000 --> 40:22.300
 as a appropriate algorithm.

40:23.200 --> 40:25.920
 But do you think about how we solve that problem?

40:25.920 --> 40:30.920
 Yeah, I think there's two different concepts there.

40:31.360 --> 40:35.820
 So one is, am I worried that because these vehicles

40:35.820 --> 40:37.600
 are self driving, people will kind of step in the road

40:37.600 --> 40:38.640
 and take advantage of them?

40:38.640 --> 40:43.640
 And I've heard this and I don't really believe it

40:43.760 --> 40:45.960
 because if I'm driving down the road

40:45.960 --> 40:48.400
 and somebody steps in front of me, I'm going to stop.

40:50.600 --> 40:53.660
 Even if I'm annoyed, I'm not gonna just drive

40:53.660 --> 40:56.400
 through a person stood in the road.

40:56.400 --> 41:00.400
 And so I think today people can take advantage of this

41:00.400 --> 41:02.560
 and you do see some people do it.

41:02.560 --> 41:04.180
 I guess there's an incremental risk

41:04.180 --> 41:05.880
 because maybe they have lower confidence

41:05.880 --> 41:07.720
 that I'm gonna see them than they might have

41:07.720 --> 41:10.400
 for an automated vehicle and so maybe that shifts

41:10.400 --> 41:12.040
 it a little bit.

41:12.040 --> 41:14.360
 But I think people don't wanna get hit by cars.

41:14.360 --> 41:17.080
 And so I think that I'm not that worried

41:17.080 --> 41:18.760
 about people walking out of the 101

41:18.760 --> 41:23.760
 and creating chaos more than they would today.

41:24.400 --> 41:27.040
 Regarding kind of the nudging through a big stream

41:27.040 --> 41:30.040
 of pedestrians leaving a concert or something,

41:30.040 --> 41:33.520
 I think that is further down the technology pipeline.

41:33.520 --> 41:36.960
 I think that you're right, that's tricky.

41:36.960 --> 41:38.620
 I don't think it's necessarily,

41:40.360 --> 41:43.600
 I think the algorithm people use for this is pretty simple.

41:43.600 --> 41:44.800
 It's kind of just move forward slowly

41:44.800 --> 41:46.800
 and if somebody's really close then stop.

41:46.800 --> 41:50.880
 And I think that that probably can be replicated

41:50.880 --> 41:54.040
 pretty easily and particularly given that

41:54.040 --> 41:55.720
 you don't do this at 30 miles an hour,

41:55.720 --> 41:59.080
 you do it at one, that even in those situations

41:59.080 --> 42:01.200
 the risk is relatively minimal.

42:01.200 --> 42:03.640
 But it's not something we're thinking about

42:03.640 --> 42:04.560
 in any serious way.

42:04.560 --> 42:07.920
 And probably that's less an algorithm problem

42:07.920 --> 42:10.160
 and more creating a human experience.

42:10.160 --> 42:14.300
 So the HCI people that create a visual display

42:14.300 --> 42:16.260
 that you're pleasantly as a pedestrian

42:16.260 --> 42:20.760
 nudged out of the way, that's an experience problem,

42:20.760 --> 42:22.000
 not an algorithm problem.

42:22.880 --> 42:25.480
 Who's the main competitor to Aurora today?

42:25.480 --> 42:28.640
 And how do you outcompete them in the long run?

42:28.640 --> 42:31.200
 So we really focus a lot on what we're doing here.

42:31.200 --> 42:34.480
 I think that, I've said this a few times,

42:34.480 --> 42:37.960
 that this is a huge difficult problem

42:37.960 --> 42:40.320
 and it's great that a bunch of companies are tackling it

42:40.320 --> 42:42.320
 because I think it's so important for society

42:42.320 --> 42:43.800
 that somebody gets there.

42:43.800 --> 42:48.800
 So we don't spend a whole lot of time

42:49.120 --> 42:51.600
 thinking tactically about who's out there

42:51.600 --> 42:55.240
 and how do we beat that person individually.

42:55.240 --> 42:58.720
 What are we trying to do to go faster ultimately?

42:59.760 --> 43:02.640
 Well part of it is the leadership team we have

43:02.640 --> 43:04.200
 has got pretty tremendous experience.

43:04.200 --> 43:06.440
 And so we kind of understand the landscape

43:06.440 --> 43:09.160
 and understand where the cul de sacs are to some degree

43:09.160 --> 43:10.980
 and we try and avoid those.

43:10.980 --> 43:14.260
 I think there's a part of it,

43:14.260 --> 43:16.260
 just this great team we've built.

43:16.260 --> 43:19.080
 People, this is a technology and a company

43:19.080 --> 43:22.320
 that people believe in the mission of

43:22.320 --> 43:23.740
 and so it allows us to attract

43:23.740 --> 43:25.740
 just awesome people to go work.

43:26.800 --> 43:29.320
 We've got a culture I think that people appreciate

43:29.320 --> 43:30.460
 that allows them to focus,

43:30.460 --> 43:33.120
 allows them to really spend time solving problems.

43:33.120 --> 43:35.900
 And I think that keeps them energized.

43:35.900 --> 43:38.940
 And then we've invested hard,

43:38.940 --> 43:43.500
 invested heavily in the infrastructure

43:43.500 --> 43:46.540
 and architectures that we think will ultimately accelerate us.

43:46.540 --> 43:50.660
 So because of the folks we're able to bring in early on,

43:50.660 --> 43:53.540
 because of the great investors we have,

43:53.540 --> 43:56.780
 we don't spend all of our time doing demos

43:56.780 --> 43:58.660
 and kind of leaping from one demo to the next.

43:58.660 --> 44:02.820
 We've been given the freedom to invest in

44:03.940 --> 44:05.500
 infrastructure to do machine learning,

44:05.500 --> 44:08.600
 infrastructure to pull data from our on road testing,

44:08.600 --> 44:11.500
 infrastructure to use that to accelerate engineering.

44:11.500 --> 44:14.480
 And I think that early investment

44:14.480 --> 44:17.340
 and continuing investment in those kind of tools

44:17.340 --> 44:19.780
 will ultimately allow us to accelerate

44:19.780 --> 44:21.940
 and do something pretty incredible.

44:21.940 --> 44:23.420
 Chris, beautifully put.

44:23.420 --> 44:24.660
 It's a good place to end.

44:24.660 --> 44:26.500
 Thank you so much for talking today.

44:26.500 --> 44:47.940
 Thank you very much. Really enjoyed it.