back to indexChris Urmson: Self-Driving Cars at Aurora, Google, CMU, and DARPA | Lex Fridman Podcast #28
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The following is a conversation with Chris Ermsen.
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He was the 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
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in the DARPA Grand Challenges and the winner
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of the DARPA Urban Challenge.
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Today, he's the CEO of Aurora Innovation,
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an autonomous 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 roboticist and autonomous vehicle
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experts in the world and a long time voice of reason
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in a space that is shrouded 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 Freedman spelled FRID MAN.
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And now, here's my conversation with Chris Ermsen.
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You were part of both the DARPA Grand Challenge
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and the DARPA Urban Challenge teams 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 incredible about the first
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of the Grand Challenges, that I remember I was a grad
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student at Carnegie Mellon, and there we
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was kind of this dichotomy of it seemed really hard,
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so that would be cool and interesting.
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But at the time, we were the only robotics
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institute around, and so if we went into it and fell
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in our faces, 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 was marked
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as darn near impossible, and then after a couple of tries,
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be able to actually make it happen, I think that was really
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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, because you're
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one of the lead engineers, you actually
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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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Why would you go do something you thought was impossible,
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completely impossible?
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We thought it was going to be hard.
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We didn't know how we're 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,
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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, algorithms
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for mapping, localization, just general perception,
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control, like hardware, software, first of all.
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I think that's the joy of this field,
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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.
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That it was a static world.
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There weren't other actors moving through it.
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That 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,
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and there's a bunch of engineering work
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to get the vehicle so that we could control 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
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to do was part of the challenge as well.
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Right, you didn't actually know the track hiding it.
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You knew approximately, but you didn't actually
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know the route that's going to be taken.
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That's right, we didn't even really,
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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 between the line
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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 of scrub
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brush and rocks and said, go figure it out.
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Fortunately, it turned into basically driving along
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a set of trails, which is much more relevant to the application
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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 of 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 deepest lessons I learned from Red,
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was that he would look at undergraduates or graduate
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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 and think,
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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 trust, but verify, have confidence
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in what people can become, I think,
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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 from the first two
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Grand Challenges to the Urban Challenge to today?
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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, the real technology
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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 of what
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the vehicle was going to encounter.
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And so that innovation, that the fact
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that we could get decimeter resolution models,
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was really a big deal.
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And that allowed us to kind of bound
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the complexity 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 one of the big step there.
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For the Urban Challenge, one of the big technological
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innovations there was the multi beam LiDAR.
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And be able to generate high resolution,
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mid to long range 3D models 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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And parallel with that, we saw a bunch
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of other technologies 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, SLAM had been a big field
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You would go to a conference a couple of years
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before that, and every paper would effectively
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have SLAM somewhere in it.
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And so seeing that 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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And in fact, we weren't really doing SLAM per se in real time,
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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 particularly
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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 it was very cool.
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So for the Urban Challenge, those already maps
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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 back then
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was, and it's still one of these things that trips people up
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today, 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, you
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don't really care about kind of the ellipsoid of the Earth
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that's being used.
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But when you want to get to 10 centimeter or centimeter
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resolution, you care whether the coordinate system is NADS 83
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or WGS 84, or these are different ways
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to describe both the kind of nonsphericalness of the Earth,
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but also kind of the actually, and I think when I can't remember
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which one, 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
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to reality to centimeter resolution,
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that was kind of interesting 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.
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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, 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 of predicting
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where that vehicle is going to be a few seconds into the future.
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We had to deal with the fact that there
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were multiple hypotheses for that because a vehicle
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at an intersection might be going right
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or it might be going straight 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 assumption.
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And so where does that 10 years later, where does that take us
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today 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 is that the actors are truly
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unpredictable, 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, or terms of perception, or 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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as much larger than the airbase that we were thinking about back
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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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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 euphorcy
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as a 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, 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 from robots
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And it only had to work for 60 miles, which looking at it
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from 2006, it had to work for 60 miles.
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Looking at it from now, we want things
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that will go and drive for half a million miles.
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And it's just a different game.
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So how important, you said Lyder came into the game early on,
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and it's really the primary driver of autonomous vehicles
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today as a sensor.
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So how important is the role of Lyder in the sensor suite
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So I think it's essential.
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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 the composition of data
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from these different sensors if you
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want the thing to really be robust.
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The question I want to ask, let's see if we can untangle it,
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is what are your thoughts on the Elon Musk provocative statement
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that Lyder is a crutch, that is a kind of, I guess,
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growing pains, and that much of the perception
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task can be done with cameras?
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So I think it is undeniable that people walk around
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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.
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Yes, in terms of sensors, right?
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So there's an example that we all
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go do it at many of us every day.
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In terms of Lyder 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, the way
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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 technology, 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, well, I'm not
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OK with using lasers, because that's whatever.
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But I am OK with using an 8 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 from the tool
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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 automotive companies.
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And if there's one word that comes up more often than anything,
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it's cost and 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, but we
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want to find the cheapest sensor suite that
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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?
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Or do you see a day where level 4 autonomy is possible
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I see both of those, but it's really a matter of time.
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And I think, really, maybe I would
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talk to the question you asked about the cheapest sensor.
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I don't think that's actually what you want.
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What you want is a sensor suite that is economically viable.
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And then after that, everything is about margin
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and driving cost out of the system.
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What you also want is a sensor suite that works.
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And so it's great to tell a story about how it would be better
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to have a self driving system with a $50 sensor instead
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But if the $500 sensor makes it work and the $50 sensor
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doesn't work, who cares?
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So long as you can actually have an economic opportunity there.
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And the economic opportunity is important,
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because that's how you actually have a sustainable business.
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And that's how you can actually see this come to scale
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and be out in the world.
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And so when I look at lidar, I see
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a technology that has no underlying fundamentally expense
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to it, fundamental expense to it.
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It's going to be more expensive than an imager,
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because CMOS processes or FAP processes
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are dramatically more scalable than mechanical processes.
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But we still should be able to drive cost
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out substantially on that side.
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And then I also do think that with the right business model,
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you can absorb more, certainly more cost
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on the below materials.
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Yeah, if the sensor suite works, extra value is provided.
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Thereby, you don't need to drive cost down to zero.
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It's a basic economics.
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You've talked about your intuition
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at level two autonomy is problematic because
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of the human factor of vigilance, decrement, complacency,
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overtrust, and so on, just us being human.
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With the overtrust system, we start doing even more
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so partaking in the secondary activities like smartphone
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Have your views evolved on this point in either direction?
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Can you speak to it?
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So I want to be really careful, because sometimes this
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gets twisted in a way that I certainly didn't intend.
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So active safety systems are a really important technology
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that we should be pursuing and integrating into vehicles.
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And there's an opportunity in the near term
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to reduce accidents, reduce fatalities, and that's
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and we should be pushing on that.
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Level two systems are systems where
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the vehicle is controlling two axes,
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so breaking and thrall slash steering.
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And I think there are variants of level two systems that
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are supporting the driver that absolutely we
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should encourage to be out there.
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Where I think there's a real challenge is in the human factors
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part around this and the misconception
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from the public around the capability set that that enables
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and the trust that they should have in it.
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And that is where I'm actually incrementally more
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concerned around level three systems
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and how exactly a level two system is marketed and delivered
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and how much effort people have put into those human factors.
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So I still believe several things around this.
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One is people will over trust the technology.
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We've seen over the last few weeks
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a spate of people sleeping in their Tesla.
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I watched an episode last night of Trevor Noah talking
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about this, and this is a smart guy
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who has a lot of resources at his disposal describing
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a Tesla as a self driving car.
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And that why shouldn't people be sleeping in their Tesla?
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It's like, well, because it's not a self driving car
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and it is not intended to be.
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And these people will almost certainly die at some point
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or hurt other people.
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And so we need to really be thoughtful about how
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that technology is described and brought to market.
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I also think that because of the economic issue,
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economic challenges we were just talking about,
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that technology path will, these level two driver system
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systems, that technology path will
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diverge from the technology path that we
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need to be on to actually deliver truly self driving
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vehicles, ones where you can get in it and sleep
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and have the equivalent or better safety
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than a human driver behind the wheel.
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Because, again, the economics are very different
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in those two worlds.
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And so that leads to divergent technology.
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So you just don't see the economics of gradually
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increasing from level two and doing so quickly enough
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to where it doesn't cost safety, critical safety concerns.
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You believe that it needs to diverge at this point
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into different, basically different routes.
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And really that comes back to what
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are those L2 and L1 systems doing?
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And they are driver assistance functions
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where the people that are marketing that responsibly
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are being very clear and putting human factors in place
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such that the driver is actually responsible for the vehicle
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and that the technology is there to support the driver.
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And the safety cases that are built around those
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are dependent on that driver attention and attentiveness.
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And at that point, you can kind of give up, to some degree,
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for economic reasons, you can give up on, say, false negatives.
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And so the way to think about this
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is for a four collision mitigation braking system,
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if half the times the driver missed a vehicle in front of it,
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it hit the brakes and brought the vehicle to a stop,
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that would be an incredible, incredible advance
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in safety on our roads, right?
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That would be equivalent to seatbelts.
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But it would mean that if that vehicle wasn't being monitored,
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it would hit one out of two cars.
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And so economically, that's a perfectly good solution
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for a driver assistance system.
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What you should do at that point,
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if you can get it to work 50% of the time,
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is drive the cost out of that so you can get it
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on as many vehicles as possible.
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But driving the cost out of it doesn't drive up performance
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on the false negative case.
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And so you'll continue to not have a technology
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that could really be available for a self driven vehicle.
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So clearly the communication,
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and this probably applies to all four vehicles as well,
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the marketing and the communication
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of what the technology is actually capable of,
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how hard it is, how easy it is,
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all that kind of stuff is highly problematic.
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So say everybody in the world was perfectly communicated
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and were made to be completely aware
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of every single technology out there,
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what it's able to do.
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What's your intuition?
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And now we're maybe getting into philosophical ground.
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Is it possible to have a level two vehicle
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where we don't overtrust it?
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If people truly understood the risks and internalized it,
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then sure you could do that safely,
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but that's a world that doesn't exist.
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The people are going to,
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if the facts are put in front of them,
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they're gonna then combine that with their experience.
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And let's say they're using an L2 system
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and they go up and down the one on one every day
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and they do that for a month
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and it just worked every day for a month.
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Like that's pretty compelling.
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At that point, just even if you know the statistics,
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you're like, well, I don't know,
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maybe there's something a little funny about those.
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Maybe they're driving in difficult places.
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Like I've seen it with my own eyes, it works.
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And the problem is that that sample size that they have,
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so it's 30 miles up and down,
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so 60 miles times 30 days, so 60, 180, 1,800 miles.
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That's a drop in the bucket compared to the one,
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what 85 million miles between fatalities.
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And so they don't really have a true estimate
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based on their personal experience of the real risks,
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but they're gonna trust it anyway,
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because it's hard not to, it worked for a month.
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What's gonna change?
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So even if you start a perfect understanding of the system,
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your own experience will make it drift.
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I mean, that's a big concern.
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Over a year, over two years even, it doesn't have to be months.
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And I think that as this technology moves from,
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what I would say is kind of the more technology savvy
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ownership group to the mass market,
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you may be able to have some of those folks
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who are really familiar with technology,
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they may be able to internalize it better.
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And you're kind of immunization
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against this kind of false risk assessment
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might last longer, but as folks who aren't as savvy
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about that read the material
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and they compare that to their personal experience,
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I think there that it's gonna move more quickly.
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So your work, the program that you've created at Google
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and now at Aurora is focused more on the second path
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of creating full autonomy.
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So it's such a fascinating,
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I think it's one of the most interesting AI problems
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of the century, right?
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It's a, I just talked to a lot of people,
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just regular people, I don't know, my mom
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about autonomous vehicles and you begin to grapple
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with ideas of giving your life control over to a machine.
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It's philosophically interesting,
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it's practically interesting.
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So let's talk about safety.
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How do you think, we demonstrate,
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you've spoken about metrics in the past,
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how do you think we demonstrate to the world
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that an autonomous vehicle, an Aurora system is safe?
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This is one where it's difficult
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because there isn't a sound bite answer.
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That we have to show a combination of work
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that was done diligently and thoughtfully.
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And this is where something like a functional safety process
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as part of that is like, here's the way we did the work.
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That means that we were very thorough.
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So, if you believe that we, what we said about,
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this is the way we did it,
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then you can have some confidence that we were thorough
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in the engineering work we put into the system.
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And then on top of that, to kind of demonstrate
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that we weren't just thorough,
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we were actually good at what we did.
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There'll be a kind of a collection of evidence
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in terms of demonstrating that the capabilities
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work the way we thought they did, statistically
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and to whatever degree we can demonstrate that
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both in some combination of simulation,
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some combination of unit testing and decomposition testing,
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and then some part of it will be on road data.
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And I think the way we'll ultimately convey this
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to the public is there'll be clearly some conversation
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with the public about it,
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but we'll kind of invoke the kind of the trusted nodes
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and that we'll spend more time being able to go
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into more depth with folks like NHTSA
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and other federal and state regulatory bodies
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and kind of given that they are operating
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in the public interest and they're trusted
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that if we can show enough work to them
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that they're convinced,
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then I think we're in a pretty good place.
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That means that you work with people
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that are essentially experts at safety
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to try to discuss and show,
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do you think the answer is probably no,
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but just in case, do you think there exists a metric?
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So currently people have been using
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a number of disengagement.
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And it quickly turns into a marketing scheme
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to sort of you alter the experiments you run to.
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I think you've spoken that you don't like.
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No, in fact, I was on the record telling DMV
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that I thought this was not a great metric.
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Do you think it's possible to create a metric,
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a number that could demonstrate safety
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outside of fatalities?
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So I do and I think that it won't be just one number.
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So as we are internally grappling with this
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and at some point we'll be able to talk
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more publicly about it,
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is how do we think about human performance
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in different tasks, say detecting traffic lights
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or safely making a left turn across traffic?
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And what do we think the failure rates
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are for those different capabilities for people?
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And then demonstrating to ourselves
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and then ultimately folks in regulatory role
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and then ultimately the public,
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that we have confidence that our system
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will work better than that.
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And so these individual metrics
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will kind of tell a compelling story ultimately.
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I do think at the end of the day,
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what we care about in terms of safety
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is life saved and injuries reduced.
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And then ultimately kind of casualty dollars
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that people aren't having to pay to get their car fixed.
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And I do think that in aviation,
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they look at a kind of an event pyramid
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where a crash is at the top of that
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and that's the worst event obviously.
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And then there's injuries and near miss events and whatnot
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and violation of operating procedures.
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And you kind of build a statistical model
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of the relevance of the low severity things
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and the high severity things.
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And I think that's something
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where we'll be able to look at as well
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because an event per 85 million miles
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is statistically a difficult thing
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even at the scale of the US to kind of compare directly.
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And that event, the fatality that's connected
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to an autonomous vehicle is significantly,
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at least currently magnified
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in the amount of attention you get.
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So that speaks to public perception.
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I think the most popular topic
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about autonomous vehicles in the public
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is the trolley problem formulation, right?
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Which has, let's not get into that too much
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but is misguided in many ways.
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But it speaks to the fact that people are grappling
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with this idea of giving control over to a machine.
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So how do you win the hearts and minds of the people
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that autonomy is something
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that could be a part of their lives?
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I think you let them experience it, right?
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I think it's right.
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I think people should be skeptical.
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I think people should ask questions.
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I think they should doubt
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because this is something new and different.
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They haven't touched it yet.
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And I think it's perfectly reasonable.
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And but at the same time,
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it's clear there's an opportunity to make the road safer.
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It's clear that we can improve access to mobility.
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It's clear that we can reduce the cost of mobility.
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And that once people try that
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and understand that it's safe
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and are able to use in their daily lives,
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I think it's one of these things that will just be obvious.
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And I've seen this practically in demonstrations
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that I've given where I've had people come in
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and they're very skeptical.
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And they get in the vehicle.
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My favorite one is taking somebody out on the freeway
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and we're on the one on one driving at 65 miles an hour.
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And after 10 minutes, they kind of turn and ask,
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is that all it does?
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And you're like, it's self driving car.
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I'm not sure exactly what you thought it would do, right?
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But it becomes mundane,
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which is exactly what you want to technology
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like this to be, right?
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We don't really...
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When I turn the light switch on in here,
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I don't think about the complexity of those electrons
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being pushed down a wire from wherever it was
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and being generated.
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It's like, I just get annoyed if it doesn't work, right?
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And what I value is the fact
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that I can do other things in this space.
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I can see my colleagues.
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I can read stuff on a paper.
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I can not be afraid of the dark.
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And I think that's what we want this technology to be like
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is it's in the background
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and people get to have those life experiences
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So putting this technology in the hands of people
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speaks to scale of deployment, right?
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So what do you think the dreaded question about the future
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because nobody can predict the future?
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But just maybe speak poetically about
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when do you think we'll see a large scale deployment
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of autonomous vehicles, 10,000, those kinds of numbers.
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We'll see that within 10 years.
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I'm pretty confident.
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What's an impressive scale?
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What moment, so you've done the DARPA Challenge
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where there's one vehicle,
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at which moment does it become,
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wow, this is serious scale?
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So I think the moment it gets serious is when
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we really do have a driverless vehicle
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operating on public roads
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and that we can do that kind of continuously.
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Without a safety driver?
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Without a safety driver in the vehicle.
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I think at that moment,
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we've kind of crossed the zero to one threshold.
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And then it is about how do we continue to scale that?
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How do we build the right business models?
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How do we build the right customer experience around it
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so that it is actually a useful product out in the world?
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And I think that is really,
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at that point, it moves from a,
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what is this kind of mixed science engineering project
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into engineering and commercialization
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and really starting to deliver on the value
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that we all see here.
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And actually making that real in the world.
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What do you think that deployment looks like?
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Where do we first see the inkling of
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no safety driver, one or two cars here and there?
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Is it on the highway?
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Is it in specific routes in the urban environment?
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I think it's gonna be urban, suburban type environments.
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You know, with Aurora,
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when we thought about how to tackle this,
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it was kind of invoke to think about trucking
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as opposed to urban driving.
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And again, the human intuition around this
link |
is that freeways are easier to drive on
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because everybody's kind of going in the same direction
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and lanes are a little wider, et cetera.
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And I think that that intuition is pretty good,
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except we don't really care about most of the time.
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We care about all of the time.
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And when you're driving on a freeway with a truck,
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say 70 miles an hour and you've got 70,000 pound load
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to do with you, that's just an incredible amount
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of kinetic energy.
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And so when that goes wrong, it goes really wrong.
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And that those challenges that you see occur more rarely
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so you don't get to learn as quickly.
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And they're incrementally more difficult
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than urban driving, but they're not easier
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than urban driving.
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And so I think this happens in moderate speed,
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urban environments, because there,
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if two vehicles crash at 25 miles per hour,
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it's not good, but probably everybody walks away.
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And those events where there's the possibility
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for that occurring happen frequently.
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So we get to learn more rapidly.
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We get to do that with lower risk for everyone.
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And then we can deliver value to people
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that need to get from one place to another.
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And then once we've got that solved,
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then the kind of the freeway driving part of this
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just falls out, but we're able to learn
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more safely, more quickly in the urban environment.
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So 10 years and then scale 20, 30 years.
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I mean, who knows if it's sufficiently compelling
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experience is created, it can be faster and slower.
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Do you think there could be breakthroughs
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and what kind of breakthroughs might there be
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that completely change that timeline?
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Again, not only am I asking to predict the future,
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I'm asking you to predict breakthroughs
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that haven't happened yet.
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So what's the, I think another way to ask that would be
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if I could wave a magic wand,
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what part of the system would I make work today
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to accelerate it as quickly as possible?
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Don't say infrastructure, please don't say infrastructure.
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No, it's definitely not infrastructure.
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It's really that perception forecasting capability.
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So if tomorrow you could give me a perfect model
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of what's happening and what will happen
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for the next five seconds around a vehicle
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on the roadway, that would accelerate things
link |
pretty dramatically.
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Are you interested in staying up at night?
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Are you mostly bothered by cars, pedestrians, or cyclists?
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So I worry most about the vulnerable road users
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about the combination of cyclists and cars, right?
link |
Just cyclists and pedestrians
link |
because they're not in armor.
link |
The cars, they're bigger, they've got protection
link |
for the people and so the ultimate risk is lower there.
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Whereas a pedestrian or cyclist, they're out on the road
link |
and they don't have any protection.
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And so we need to pay extra attention to that.
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Do you think about a very difficult technical challenge
link |
of the fact that pedestrians,
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if you try to protect pedestrians by being careful
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and slow, they'll take advantage of that.
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So the game theoretic dance.
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Does that worry you from a technical perspective
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how we solve that?
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Because as humans, the way we solve that
link |
is kind of nudge our way through the pedestrians,
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which doesn't feel from a technical perspective
link |
as a appropriate algorithm.
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But do you think about how we solve that problem?
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Yeah, I think there's two different concepts there.
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So one is, am I worried that because these vehicles
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are self driving, people will kind of step on the road
link |
and take advantage of them.
link |
And I've heard this and I don't really believe it
link |
because if I'm driving down the road
link |
and somebody steps in front of me, I'm going to stop.
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Like even if I'm annoyed, I'm not gonna just drive
link |
through a person stood on the road.
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And so I think today people can take advantage of this
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and you do see some people do it.
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I guess there's an incremental risk
link |
because maybe they have lower confidence
link |
that I'm going to see them
link |
than they might have for an automated vehicle.
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And so maybe that shifts it a little bit.
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But I think people don't want to get hit by cars.
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And so I think that I'm not that worried
link |
about people walking out of the one on one
link |
and creating chaos more than they would today.
link |
Regarding kind of the nudging through a big stream
link |
of pedestrians leaving a concert or something.
link |
I think that is further down the technology pipeline.
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I think that you're right, that's tricky.
link |
I don't think it's necessarily,
link |
I think the algorithm people use for this is pretty simple.
link |
It's kind of just move forward slowly
link |
and if somebody's really close and stop.
link |
And I think that that probably can be replicated
link |
pretty easily and particularly given that it's,
link |
you don't do this at 30 miles an hour, you do it at one,
link |
that even in those situations,
link |
the risk is relatively minimal.
link |
But it's not something we're thinking
link |
about in any serious way.
link |
And probably that's less an algorithm problem
link |
more creating a human experience.
link |
So the HCI people that create a visual display
link |
that you're pleasantly as a pedestrian,
link |
nudged out of the way.
link |
That's an experience problem, not an algorithm problem.
link |
Who's the main competitor to Aurora today?
link |
And how do you out compete them in the long run?
link |
So we really focus a lot on what we're doing here.
link |
I think that, I've said this a few times
link |
that this is a huge difficult problem
link |
and it's great that a bunch of companies are tackling it
link |
because I think it's so important for society
link |
that somebody gets there.
link |
So we don't spend a whole lot of time
link |
like thinking tactically about who's out there
link |
and how do we beat that person individually?
link |
What are we trying to do to go faster ultimately?
link |
Well, part of it is the leisure team we have
link |
has got pretty tremendous experience.
link |
And so we kind of understand the landscape
link |
and understand where the cul de sacs are to some degree.
link |
And we try and avoid those.
link |
I think there's a part of it
link |
just this great team we've built.
link |
People, this is a technology and a company
link |
that people believe in the mission of.
link |
And so it allows us to attract just awesome people
link |
We've got a culture, I think,
link |
that people appreciate, that allows them to focus,
link |
allows them to really spend time solving problems.
link |
And I think that keeps them energized.
link |
And then we've invested heavily in the infrastructure
link |
and architectures that we think will ultimately accelerate us.
link |
So because of the folks we're able to bring in early on,
link |
because of the great investors we have,
link |
we don't spend all of our time doing demos
link |
and kind of leaping from one demo to the next.
link |
We've been given the freedom to invest in
link |
infrastructure to do machine learning,
link |
infrastructure to pull data from our on road testing,
link |
infrastructure to use that to accelerate engineering.
link |
And I think that early investment
link |
and continuing investment in those kind of tools
link |
will ultimately allow us to accelerate
link |
and do something pretty incredible.
link |
Chris, beautifully put.
link |
It's a good place to end.
link |
Thank you so much for talking today.
link |
Thank you very much.
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I hope you enjoyed it.