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

1. 0:00 – 42:25

   Intro

   1. LFLex Fridman0:00

      The following is a conversation with Chris Urmson. He was a CTO of the Google self-driving car team, a key engineer and leader behind the Carnegie Mellon University autonomous vehicle entries in the DARPA Grand Challenges, and the winner of the DARPA Urban Challenge. Today, he's the CEO of Aurora Innovation, an autonomous vehicle software company he started with Sterling Anderson, who was the former director of Tesla Autopilot, and Drew Bagnell, Uber's former autonomy and perception lead. Chris is one of the top roboticists and autonomous vehicle experts in the world, and a longtime voice of reason in a space that is shrouded in both mystery and hype. He both acknowledges the incredible challenges involved in solving the problem of autonomous driving and is working hard to solve it. This is the Artificial Intelligence Podcast. If you enjoy it, subscribe on YouTube, give it five stars on iTunes, support it on Patreon, or simply connect with me on Twitter at Lex Fridman, spelled F-R-I-D-M-A-N. And now, here's my conversation with Chris Urmson. You were part of both the DARPA Grand Challenge and the DARPA Urban Challenge teams at CMU with, uh, Red Whittaker. What technical or philosophical things have you learned from these races?

   2. CUChris Urmson1:22

      I think the, the high order bit was that it could be done. I think that was the thing that was incredible about the, first, the, the grand challenges.

   3. LFLex Fridman1:34

      Mm-hmm.

   4. CUChris Urmson1:35

      That I remember, you know, I was a grad student at Carnegie Mellon and there we, it was kind of this dichotomy of it seemed really hard, so that would be cool and interesting. But, you know, at the time, we were the only robotics institute around. And so, you know, if we went into it and fell on our faces, that would, that would be-

   5. LFLex Fridman1:56

      Okay.

   6. CUChris Urmson1:56

      ... embarrassing. Uh, so I think, you know, just having the will to go do it, to try to do this thing that at the time was marked as, you know, darn near impossible. And, and then after a couple of tries, be able to actually make it happen, I think that was, you know, that was really exciting.

   7. LFLex Fridman2:12

      But at which point did you believe it was possible? Did you from the very beginning? Did you personally? 'Cause you were one of the lead engineer, you actually had to do a lot of the work.

   8. CUChris Urmson2:21

      Yeah, I was the technical director there and did a lot of the work (laughs) , uh, along with a bunch of other really good people. Did I believe it could be done? Yeah, of course. Right? Like, why would you go do something you thought was impossible, completely impossible? Uh, we thought it was going to be hard. We didn't know how we were gonna be able to do it. We didn't know if we'd be able to do it the first time. (laughs) Turns out we couldn't. That, yeah, I guess you have to. I th- I think there's a certain benefit to naivete, right? That if you don't know how hard something really is, you, you try different things and, you know, it gives you an opportunity that others who are, you know, wiser maybe don't, don't have.

   9. LFLex Fridman3:04

      Well, what were the biggest pain points? Mechanical, sensors, hardware, software, algorithms for mapping, localization, uh, just general perception control? What, like hardware/software, first of all.

   10. CUChris Urmson3:15

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

   11. LFLex Fridman3:19

       (laughs) Yeah.

   12. CUChris Urmson3:19

       Um, and that you have to be good at, at, at each part of it. So for the first, for the urban challenges, uh, if I look back at it from today, uh, it should be easy today. That, you know, it was a static world, there weren't other actors moving through it, th- is what that means. Uh, it was out in the desert, so you get really good GPS. You know, so that, that when, and, you know, we could map it roughly. And so in retrospect now, it's, you know, it's, it's within the realm of things we could do. Back then, just actually getting the vehicle and the, you know, there was a bunch of engineering work to get the vehicle so that we could control it and drive it.

   13. LFLex Fridman4:02

       Mm-hmm.

   14. CUChris Urmson4:04

       That's, you know, that's still a pain today, but it was even more so back then. Uh, and then the uncertainty of exactly what they wanted us to do was, was part of the challenge as well.

   15. LFLex Fridman4:17

       Right, you didn't actually know the track heading in. You, you knew approximately, but you know, didn't actually know the route, the route that was gonna be taken.

   16. CUChris Urmson4:23

       That's right. We didn't know the route. We didn't even r- really, the way the rules had been described, you had to kind of guess. So i- if you think back to that challenge, the idea was to, uh, that the, the government would give us, uh, DARPA would give us, uh, a set of waypoints and kind of the width, um, that you had to stay within between the line that went between, you know, each of those waypoints. And so the, the most devious thing they could have done is set, you know, a kilometer-wide corridor across, you know, a field of scrub brush, um, and rocks and said, you know, "Go figure it out." Uh, fortunately, it really, it turned into basically driving along a set of trails, which, you know, is much more relevant to, to the application they were looking for. But no, it was, it was a hell of a thing back in the day.

   17. LFLex Fridman5:12

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

   18. CUChris Urmson5:16

       Yeah.

   19. LFLex Fridman5:16

       ... uh, in terms of just broadly speaking. So you're a leader now. What have you learned from Red about leadership?

   20. CUChris Urmson5:25

       I, I think there's a couple things. One is, you know, go and try those really hard things. That, that's where there is a, an incredible opportunity. Uh, I think the other big one though is to see people for who they can be, not who they are. It, it's one of the things that I actually, one of the deepest lessons I, I learned from Red was that he would look at, um, you know, undergraduates or graduate students and empower them, uh, to be leaders-

   21. LFLex Fridman5:56

       Mm-hmm.

   22. CUChris Urmson5:56

       ... to, to, you know, have responsibility, to do great things, that I think...... another person might look at them and think, "Oh, well, that's just, you know, an undergraduate student. What, what could they know?" And so I think that, that, you know, kind of trust but verify, have confidence in what people can become, I think is, is a really powerful thing.

   23. LFLex Fridman6:16

       So through that, let's just, like, fast-forward through the history. Can you maybe talk through the technical evolution of autonomous vehicle systems from the first two Grand Challenges to the Urban Challenge to today?

   24. CUChris Urmson6:30

       Sure.

   25. LFLex Fridman6:31

       Are there major shifts in your mind or is it the same kind of technology, just made more robust?

   26. CUChris Urmson6:37

       I think there's been some big, big steps. So the, for the Grand Challenge, the real technology that unlocked that was HD mapping.

   27. LFLex Fridman6:51

       Mm-hmm.

   28. CUChris Urmson6:51

       Prior to that, a lot of the off-road robotics work had been done without any real prior model of what the vehicle was going to encounter. And so that innovation, that... The fact that we could get, you know, decimeter resolution models was really a, a big deal. And, and that allowed us to, to kind of bound the complexity of the driving problem the vehicle had and allowed it to operate at speed, because we could assume things about the environment that it was going to encounter. So that was a, that was one of the... That was the big step there. For the Urban Challenge, you know, one of the big technological innovations there was the multi-beam LIDAR, and being able to generate, um, high-resolution, you know, mid-to-long range 3D models of the world and use that for, you know, f- for understanding the world around the vehicle. And that was really a, you know, kind of a game-changing technology. In parallel with that, we saw a bunch of other technologies that had been kind of converging, half their, their day in the sun. So, uh, Bayesian estimation-

   29. LFLex Fridman8:11

       Mm-hmm.

   30. CUChris Urmson8:12

       ... uh, had been... You know, SLAM had been a big field i- in robotics. You know, you, you would go to a conference, you know, a couple of years before that and, you know, every paper would effectively have SLAM somewhere in it.
2. 42:25 – 44:32

   Aurora path forward

   1. LFLex Fridman42:25

      And how do you out-compete them in the long run?

   2. CUChris Urmson42:28

      (laughs) So we, we really focus a lot on what we're doing here. I think that, you know, I've said this a few times, that this is a huge difficult problem, and it's great that a bunch of companies are tackling it, because I think it's so important for society that somebody gets there. So we, you know, we're, we don't spend a whole lot of time, like, thinking tactically about who's out there and, and how do we beat that, that, that person individually. Um, what are we trying to do to, to go faster ultimately?

   3. LFLex Fridman42:58

      Mm-hmm.

   4. CUChris Urmson42:59

      Uh, well part of it is, the leadership team we have has got pretty tremendous experience. Um, and so we kind of understand the landscape and understand where the cul-de-sacs are, to some degree, and, you know, we try and avoid those. I think there's a part of it's just this great team we've built. Uh, people... This is a technology and a company that people believe in the mission of, and so it allows us to attract just awesome people to go work. We've got a culture, I think, that people appreciate, that allows them to focus, allows them to really spend time solving problems. Uh, and I think that keeps them energized. Uh, and then we've invested hard, uh, uh, or invested heavily in the infrastructure and architectures that we think will ultimately accelerate us. So because of the folks we were able to bring in early on, because of the, the, the great investors we have, you know, w- we don't spend all of our time doing demos, a- and kind of leaping from one demo to the next. We've been given the freedom to invest in infrastructure to do machine learning, infrastructure to pull data from our on-road testing, infrastructure to use that to accelerate engineering. And I think that, that early investment and continuing investment in those kind of tools will ultimately allow us to accelerate and, and, and do something pretty incredible.

   5. LFLex Fridman44:22

      Chris, beautifully put. It's a good place to end. Thank you so much for talking today.

   6. CUChris Urmson44:26

      Oh, thank you very much. Really enjoyed it.

Episode duration: 44:47