WEBVTT 00:00.000 --> 00:03.120 The following is a conversation with Chris Ermsen. 00:03.120 --> 00:06.040 He was the CTO of the Google self driving car team, 00:06.040 --> 00:08.880 a key engineer and leader behind the Carnegie Mellon 00:08.880 --> 00:11.240 University, autonomous vehicle entries 00:11.240 --> 00:14.120 in the DARPA Grand Challenges and the winner 00:14.120 --> 00:16.160 of the DARPA Urban Challenge. 00:16.160 --> 00:19.480 Today, he's the CEO of Aurora Innovation, 00:19.480 --> 00:21.360 an autonomous vehicle software company. 00:21.360 --> 00:23.600 He started with Sterling Anderson, 00:23.600 --> 00:26.000 who was the former director of Tesla Autopilot 00:26.000 --> 00:30.160 and drew back now Uber's former autonomy and perception lead. 00:30.160 --> 00:33.160 Chris is one of the top roboticist and autonomous vehicle 00:33.160 --> 00:37.440 experts in the world and a long time voice of reason 00:37.440 --> 00:41.320 in a space that is shrouded in both mystery and hype. 00:41.320 --> 00:43.600 He both acknowledges the incredible challenges 00:43.600 --> 00:46.560 involved in solving the problem of autonomous driving 00:46.560 --> 00:49.680 and is working hard to solve it. 00:49.680 --> 00:52.440 This is the Artificial Intelligence Podcast. 00:52.440 --> 00:54.720 If you enjoy it, subscribe on YouTube, 00:54.720 --> 00:57.920 give it five stars on iTunes, support it on Patreon, 00:57.920 --> 00:59.760 or simply connect with me on Twitter 00:59.760 --> 01:03.280 at Lex Freedman spelled FRID MAN. 01:03.280 --> 01:09.160 And now, here's my conversation with Chris Ermsen. 01:09.160 --> 01:11.960 You were part of both the DARPA Grand Challenge 01:11.960 --> 01:17.040 and the DARPA Urban Challenge teams at CMU with Red Whitaker. 01:17.040 --> 01:19.720 What technical or philosophical things 01:19.720 --> 01:22.280 have you learned from these races? 01:22.280 --> 01:26.640 I think the high order bit was that it could be done. 01:26.640 --> 01:32.880 I think that was the thing that was incredible about the first 01:32.880 --> 01:36.440 of the Grand Challenges, that I remember I was a grad 01:36.440 --> 01:41.440 student at Carnegie Mellon, and there we 01:41.440 --> 01:46.320 was kind of this dichotomy of it seemed really hard, 01:46.320 --> 01:48.800 so that would be cool and interesting. 01:48.800 --> 01:51.720 But at the time, we were the only robotics 01:51.720 --> 01:54.960 institute around, and so if we went into it and fell 01:54.960 --> 01:58.320 in our faces, that would be embarrassing. 01:58.320 --> 02:01.160 So I think just having the will to go do it, 02:01.160 --> 02:03.360 to try to do this thing that at the time was marked 02:03.360 --> 02:07.120 as darn near impossible, and then after a couple of tries, 02:07.120 --> 02:11.360 be able to actually make it happen, I think that was really 02:11.360 --> 02:12.360 exciting. 02:12.360 --> 02:15.120 But at which point did you believe it was possible? 02:15.120 --> 02:17.000 Did you, from the very beginning, 02:17.000 --> 02:18.360 did you personally, because you're 02:18.360 --> 02:20.320 one of the lead engineers, you actually 02:20.320 --> 02:21.800 had to do a lot of the work? 02:21.800 --> 02:23.840 Yeah, I was the technical director there, 02:23.840 --> 02:26.120 and did a lot of the work, along with a bunch 02:26.120 --> 02:28.440 of other really good people. 02:28.440 --> 02:29.760 Did I believe it could be done? 02:29.760 --> 02:31.120 Yeah, of course. 02:31.120 --> 02:33.400 Why would you go do something you thought was impossible, 02:33.400 --> 02:34.880 completely impossible? 02:34.880 --> 02:36.280 We thought it was going to be hard. 02:36.280 --> 02:38.080 We didn't know how we're going to be able to do it. 02:38.080 --> 02:42.880 We didn't know if we'd be able to do it the first time. 02:42.880 --> 02:46.000 Turns out we couldn't. 02:46.000 --> 02:48.400 That, yeah, I guess you have to. 02:48.400 --> 02:52.920 I think there's a certain benefit to naivete, 02:52.920 --> 02:55.400 that if you don't know how hard something really is, 02:55.400 --> 02:59.560 you try different things, and it gives you an opportunity 02:59.560 --> 03:04.080 that others who are wiser maybe don't have. 03:04.080 --> 03:05.680 What were the biggest pain points? 03:05.680 --> 03:09.360 Mechanical, sensors, hardware, software, algorithms 03:09.360 --> 03:12.760 for mapping, localization, just general perception, 03:12.760 --> 03:15.440 control, like hardware, software, first of all. 03:15.440 --> 03:17.840 I think that's the joy of this field, 03:17.840 --> 03:20.040 is that it's all hard. 03:20.040 --> 03:25.160 And that you have to be good at each part of it. 03:25.160 --> 03:32.280 So for the urban challenges, if I look back at it from today, 03:32.280 --> 03:36.200 it should be easy today. 03:36.200 --> 03:38.880 That it was a static world. 03:38.880 --> 03:40.720 There weren't other actors moving through it. 03:40.720 --> 03:42.440 That is what that means. 03:42.440 --> 03:47.080 It was out in the desert, so you get really good GPS. 03:47.080 --> 03:51.320 So that went, and we could map it roughly. 03:51.320 --> 03:55.160 And so in retrospect now, it's within the realm of things 03:55.160 --> 03:57.800 we could do back then. 03:57.800 --> 03:59.200 Just actually getting the vehicle, 03:59.200 --> 04:00.680 and there's a bunch of engineering work 04:00.680 --> 04:04.200 to get the vehicle so that we could control and drive it. 04:04.200 --> 04:09.520 That's still a pain today, but it was even more so back then. 04:09.520 --> 04:12.920 And then the uncertainty of exactly what they wanted us 04:12.920 --> 04:17.080 to do was part of the challenge as well. 04:17.080 --> 04:19.360 Right, you didn't actually know the track hiding it. 04:19.360 --> 04:21.520 You knew approximately, but you didn't actually 04:21.520 --> 04:23.560 know the route that's going to be taken. 04:23.560 --> 04:26.560 That's right, we didn't even really, 04:26.560 --> 04:28.640 the way the rules had been described, 04:28.640 --> 04:29.840 you had to kind of guess. 04:29.840 --> 04:33.440 So if you think back to that challenge, 04:33.440 --> 04:37.000 the idea was that the government would give us, 04:37.000 --> 04:40.360 the DARPA would give us a set of waypoints 04:40.360 --> 04:44.240 and kind of the width that you had to stay within between the line 04:44.240 --> 04:46.840 that went between each of those waypoints. 04:46.840 --> 04:49.280 And so the most devious thing they could have done 04:49.280 --> 04:53.720 is set a kilometer wide corridor across a field of scrub 04:53.720 --> 04:58.520 brush and rocks and said, go figure it out. 04:58.520 --> 05:02.200 Fortunately, it turned into basically driving along 05:02.200 --> 05:06.800 a set of trails, which is much more relevant to the application 05:06.800 --> 05:08.760 they were looking for. 05:08.760 --> 05:12.080 But no, it was a hell of a thing back in the day. 05:12.080 --> 05:16.640 So the legend, Red, was kind of leading that effort 05:16.640 --> 05:19.120 in terms of just broadly speaking. 05:19.120 --> 05:22.040 So you're a leader now. 05:22.040 --> 05:25.000 What have you learned from Red about leadership? 05:25.000 --> 05:26.360 I think there's a couple of things. 05:26.360 --> 05:30.880 One is go and try those really hard things. 05:30.880 --> 05:34.760 That's where there is an incredible opportunity. 05:34.760 --> 05:36.560 I think the other big one, though, 05:36.560 --> 05:41.720 is to see people for who they can be, not who they are. 05:41.720 --> 05:46.080 It's one of the deepest lessons I learned from Red, 05:46.080 --> 05:51.000 was that he would look at undergraduates or graduate 05:51.000 --> 05:56.120 students and empower them to be leaders, 05:56.120 --> 06:01.400 to have responsibility, to do great things, 06:01.400 --> 06:04.760 that I think another person might look at them and think, 06:04.760 --> 06:06.600 oh, well, that's just an undergraduate student. 06:06.600 --> 06:08.720 What could they know? 06:08.720 --> 06:13.520 And so I think that trust, but verify, have confidence 06:13.520 --> 06:14.880 in what people can become, I think, 06:14.880 --> 06:16.680 is a really powerful thing. 06:16.680 --> 06:20.480 So through that, let's just fast forward through the history. 06:20.480 --> 06:24.200 Can you maybe talk through the technical evolution 06:24.200 --> 06:27.480 of autonomous vehicle systems from the first two 06:27.480 --> 06:30.920 Grand Challenges to the Urban Challenge to today? 06:30.920 --> 06:33.600 Are there major shifts in your mind, 06:33.600 --> 06:37.240 or is it the same kind of technology just made more robust? 06:37.240 --> 06:40.880 I think there's been some big, big steps. 06:40.880 --> 06:46.600 So for the Grand Challenge, the real technology 06:46.600 --> 06:51.400 that unlocked that was HD mapping. 06:51.400 --> 06:55.200 Prior to that, a lot of the off road robotics work 06:55.200 --> 06:58.920 had been done without any real prior model of what 06:58.920 --> 07:01.400 the vehicle was going to encounter. 07:01.400 --> 07:03.960 And so that innovation, that the fact 07:03.960 --> 07:11.320 that we could get decimeter resolution models, 07:11.320 --> 07:13.560 was really a big deal. 07:13.560 --> 07:17.480 And that allowed us to kind of bound 07:17.480 --> 07:19.680 the complexity of the driving problem the vehicle had 07:19.680 --> 07:21.040 and allowed it to operate at speed, 07:21.040 --> 07:23.800 because we could assume things about the environment 07:23.800 --> 07:26.400 that it was going to encounter. 07:26.400 --> 07:31.320 So that was one of the big step there. 07:31.320 --> 07:38.520 For the Urban Challenge, one of the big technological 07:38.520 --> 07:41.960 innovations there was the multi beam LiDAR. 07:41.960 --> 07:45.720 And be able to generate high resolution, 07:45.720 --> 07:48.680 mid to long range 3D models the world, 07:48.680 --> 07:54.120 and use that for understanding the world around the vehicle. 07:54.120 --> 07:59.120 And that was really kind of a game changing technology. 07:59.120 --> 08:02.880 And parallel with that, we saw a bunch 08:02.880 --> 08:06.640 of other technologies that had been kind of converging 08:06.640 --> 08:08.960 half their day in the sun. 08:08.960 --> 08:16.800 So Bayesian estimation had been, SLAM had been a big field 08:16.800 --> 08:18.600 in robotics. 08:18.600 --> 08:20.800 You would go to a conference a couple of years 08:20.800 --> 08:23.800 before that, and every paper would effectively 08:23.800 --> 08:25.640 have SLAM somewhere in it. 08:25.640 --> 08:31.560 And so seeing that Bayesian estimation techniques 08:31.560 --> 08:34.040 play out on a very visible stage, 08:34.040 --> 08:38.680 I thought that was pretty exciting to see. 08:38.680 --> 08:41.760 And mostly SLAM was done based on LiDAR at that time? 08:41.760 --> 08:42.400 Well, yeah. 08:42.400 --> 08:46.720 And in fact, we weren't really doing SLAM per se in real time, 08:46.720 --> 08:48.120 because we had a model ahead of time. 08:48.120 --> 08:51.560 We had a roadmap, but we were doing localization. 08:51.560 --> 08:54.080 And we were using the LiDAR or the cameras, 08:54.080 --> 08:55.920 depending on who exactly was doing it, 08:55.920 --> 08:58.080 to localize to a model of the world. 08:58.080 --> 09:00.720 And I thought that was a big step 09:00.720 --> 09:07.160 from kind of naively trusting GPS INS before that. 09:07.160 --> 09:10.400 And again, lots of work had been going on in this field. 09:10.400 --> 09:14.080 Certainly, this was not doing anything particularly 09:14.080 --> 09:17.400 innovative in SLAM or in localization, 09:17.400 --> 09:20.160 but it was seeing that technology necessary 09:20.160 --> 09:21.800 in a real application on a big stage. 09:21.800 --> 09:23.080 I thought it was very cool. 09:23.080 --> 09:25.600 So for the Urban Challenge, those already maps 09:25.600 --> 09:28.120 constructed offline in general? 09:28.120 --> 09:28.600 OK. 09:28.600 --> 09:30.920 And did people do that individually? 09:30.920 --> 09:33.600 Did individual teams do it individually? 09:33.600 --> 09:36.440 So they had their own different approaches there? 09:36.440 --> 09:41.720 Or did everybody kind of share that information, 09:41.720 --> 09:42.880 at least intuitively? 09:42.880 --> 09:49.560 So DARPA gave all the teams a model of the world, a map. 09:49.560 --> 09:53.720 And then one of the things that we had to figure out back then 09:53.720 --> 09:56.720 was, and it's still one of these things that trips people up 09:56.720 --> 10:00.240 today, is actually the coordinate system. 10:00.240 --> 10:03.000 So you get a latitude, longitude. 10:03.000 --> 10:05.120 And to so many decimal places, you 10:05.120 --> 10:07.800 don't really care about kind of the ellipsoid of the Earth 10:07.800 --> 10:09.520 that's being used. 10:09.520 --> 10:12.720 But when you want to get to 10 centimeter or centimeter 10:12.720 --> 10:18.480 resolution, you care whether the coordinate system is NADS 83 10:18.480 --> 10:22.720 or WGS 84, or these are different ways 10:22.720 --> 10:26.720 to describe both the kind of nonsphericalness of the Earth, 10:26.720 --> 10:31.560 but also kind of the actually, and I think when I can't remember 10:31.560 --> 10:33.560 which one, the tectonic shifts that are happening 10:33.560 --> 10:36.920 and how to transform the global datum as a function of that. 10:36.920 --> 10:40.400 So getting a map and then actually matching it 10:40.400 --> 10:41.880 to reality to centimeter resolution, 10:41.880 --> 10:44.000 that was kind of interesting and fun back then. 10:44.000 --> 10:46.800 So how much work was the perception doing there? 10:46.800 --> 10:52.440 So how much were you relying on localization based on maps 10:52.440 --> 10:55.720 without using perception to register to the maps? 10:55.720 --> 10:57.960 And I guess the question is how advanced 10:57.960 --> 10:59.720 was perception at that point? 10:59.720 --> 11:01.920 It's certainly behind where we are today. 11:01.920 --> 11:05.800 We're more than a decade since the urban challenge. 11:05.800 --> 11:13.080 But the core of it was there, that we were tracking vehicles. 11:13.080 --> 11:15.600 We had to do that at 100 plus meter range 11:15.600 --> 11:18.280 because we had to merge with other traffic. 11:18.280 --> 11:21.200 We were using, again, Bayesian estimates 11:21.200 --> 11:23.800 for state of these vehicles. 11:23.800 --> 11:25.560 We had to deal with a bunch of the problems 11:25.560 --> 11:28.240 that you think of today of predicting 11:28.240 --> 11:31.040 where that vehicle is going to be a few seconds into the future. 11:31.040 --> 11:33.680 We had to deal with the fact that there 11:33.680 --> 11:36.000 were multiple hypotheses for that because a vehicle 11:36.000 --> 11:37.640 at an intersection might be going right 11:37.640 --> 11:41.440 or it might be going straight or it might be making a left turn. 11:41.440 --> 11:44.080 And we had to deal with the challenge of the fact 11:44.080 --> 11:47.520 that our behavior was going to impact the behavior 11:47.520 --> 11:48.880 of that other operator. 11:48.880 --> 11:53.400 And we did a lot of that in relatively naive ways. 11:53.400 --> 11:54.720 But it kind of worked. 11:54.720 --> 11:57.000 Still had to have some kind of assumption. 11:57.000 --> 12:00.640 And so where does that 10 years later, where does that take us 12:00.640 --> 12:04.200 today from that artificial city construction 12:04.200 --> 12:06.920 to real cities to the urban environment? 12:06.920 --> 12:13.600 Yeah, I think the biggest thing is that the actors are truly 12:13.600 --> 12:18.680 unpredictable, that most of the time, the drivers on the road, 12:18.680 --> 12:24.000 the other road users are out there behaving well. 12:24.000 --> 12:27.040 But every once in a while, they're not. 12:27.040 --> 12:33.320 The variety of other vehicles is, you have all of them. 12:33.320 --> 12:35.760 In terms of behavior, or terms of perception, or both? 12:35.760 --> 12:38.320 Both. 12:38.320 --> 12:40.480 Back then, we didn't have to deal with cyclists. 12:40.480 --> 12:42.800 We didn't have to deal with pedestrians. 12:42.800 --> 12:46.240 Didn't have to deal with traffic lights. 12:46.240 --> 12:49.360 The scale over which that you have to operate is now 12:49.360 --> 12:52.240 as much larger than the airbase that we were thinking about back 12:52.240 --> 12:52.720 then. 12:52.720 --> 12:56.280 So what easy question? 12:56.280 --> 12:59.720 What do you think is the hardest part about driving? 12:59.720 --> 13:00.480 Easy question. 13:00.480 --> 13:01.320 Yeah. 13:01.320 --> 13:02.600 No, I'm joking. 13:02.600 --> 13:07.440 I'm sure nothing really jumps out at you as one thing. 13:07.440 --> 13:12.920 But in the jump from the urban challenge to the real world, 13:12.920 --> 13:16.200 is there something that's a particular euphorcy 13:16.200 --> 13:18.480 as a very serious, difficult challenge? 13:18.480 --> 13:21.120 I think the most fundamental difference 13:21.120 --> 13:28.960 is that we're doing it for real, that in that environment, 13:28.960 --> 13:31.840 it was both a limited complexity environment, 13:31.840 --> 13:33.240 because certain actors weren't there, 13:33.240 --> 13:35.360 because the roads were maintained. 13:35.360 --> 13:38.720 There were barriers keeping people separate from robots 13:38.720 --> 13:40.880 at the time. 13:40.880 --> 13:44.480 And it only had to work for 60 miles, which looking at it 13:44.480 --> 13:48.960 from 2006, it had to work for 60 miles. 13:48.960 --> 13:52.720 Looking at it from now, we want things 13:52.720 --> 13:57.200 that will go and drive for half a million miles. 13:57.200 --> 14:00.960 And it's just a different game. 14:00.960 --> 14:06.080 So how important, you said Lyder came into the game early on, 14:06.080 --> 14:08.880 and it's really the primary driver of autonomous vehicles 14:08.880 --> 14:10.240 today as a sensor. 14:10.240 --> 14:12.880 So how important is the role of Lyder in the sensor suite 14:12.880 --> 14:14.760 in the near term? 14:14.760 --> 14:18.680 So I think it's essential. 14:18.680 --> 14:20.520 But I also believe that cameras are essential, 14:20.520 --> 14:22.160 and I believe the radar is essential. 14:22.160 --> 14:27.400 I think that you really need to use the composition of data 14:27.400 --> 14:28.920 from these different sensors if you 14:28.920 --> 14:32.600 want the thing to really be robust. 14:32.600 --> 14:35.440 The question I want to ask, let's see if we can untangle it, 14:35.440 --> 14:40.240 is what are your thoughts on the Elon Musk provocative statement 14:40.240 --> 14:45.840 that Lyder is a crutch, that is a kind of, I guess, 14:45.840 --> 14:49.600 growing pains, and that much of the perception 14:49.600 --> 14:52.160 task can be done with cameras? 14:52.160 --> 14:56.920 So I think it is undeniable that people walk around 14:56.920 --> 14:59.680 without lasers in their foreheads, 14:59.680 --> 15:01.840 and they can get into vehicles and drive them. 15:01.840 --> 15:05.560 And so there's an existence proof 15:05.560 --> 15:10.840 that you can drive using passive vision. 15:10.840 --> 15:12.680 No doubt, can't argue with that. 15:12.680 --> 15:14.320 In terms of sensors, yeah. 15:14.320 --> 15:14.800 So there's proof. 15:14.800 --> 15:15.960 Yes, in terms of sensors, right? 15:15.960 --> 15:18.720 So there's an example that we all 15:18.720 --> 15:23.280 go do it at many of us every day. 15:23.280 --> 15:28.200 In terms of Lyder being a crutch, sure. 15:28.200 --> 15:33.080 But in the same way that the combustion engine 15:33.080 --> 15:35.240 was a crutch on the path to an electric vehicle, 15:35.240 --> 15:40.840 in the same way that any technology ultimately gets 15:40.840 --> 15:44.640 replaced by some superior technology in the future. 15:44.640 --> 15:47.720 And really, the way that I look at this 15:47.720 --> 15:51.720 is that the way we get around on the ground, the way 15:51.720 --> 15:55.280 that we use transportation is broken. 15:55.280 --> 15:59.720 And that we have this, I think the number I saw this morning, 15:59.720 --> 16:04.040 37,000 Americans killed last year on our roads. 16:04.040 --> 16:05.360 And that's just not acceptable. 16:05.360 --> 16:09.440 And so any technology that we can bring to bear 16:09.440 --> 16:12.840 that accelerates this technology, self driving technology, 16:12.840 --> 16:15.720 coming to market and saving lives, 16:15.720 --> 16:18.280 is technology we should be using. 16:18.280 --> 16:24.040 And it feels just arbitrary to say, well, I'm not 16:24.040 --> 16:27.800 OK with using lasers, because that's whatever. 16:27.800 --> 16:30.760 But I am OK with using an 8 megapixel camera 16:30.760 --> 16:32.880 or a 16 megapixel camera. 16:32.880 --> 16:34.640 These are just bits of technology, 16:34.640 --> 16:36.880 and we should be taking the best technology from the tool 16:36.880 --> 16:41.600 bin that allows us to go and solve a problem. 16:41.600 --> 16:45.160 The question I often talk to, well, obviously you do as well, 16:45.160 --> 16:48.320 to automotive companies. 16:48.320 --> 16:51.880 And if there's one word that comes up more often than anything, 16:51.880 --> 16:55.320 it's cost and drive costs down. 16:55.320 --> 17:01.440 So while it's true that it's a tragic number, the 37,000, 17:01.440 --> 17:04.880 the question is, and I'm not the one asking this question, 17:04.880 --> 17:07.160 because I hate this question, but we 17:07.160 --> 17:11.680 want to find the cheapest sensor suite that 17:11.680 --> 17:13.400 creates a safe vehicle. 17:13.400 --> 17:18.240 So in that uncomfortable trade off, 17:18.240 --> 17:23.680 do you foresee lidar coming down in cost in the future? 17:23.680 --> 17:28.000 Or do you see a day where level 4 autonomy is possible 17:28.000 --> 17:29.880 without lidar? 17:29.880 --> 17:32.880 I see both of those, but it's really a matter of time. 17:32.880 --> 17:35.080 And I think, really, maybe I would 17:35.080 --> 17:38.760 talk to the question you asked about the cheapest sensor. 17:38.760 --> 17:40.440 I don't think that's actually what you want. 17:40.440 --> 17:45.720 What you want is a sensor suite that is economically viable. 17:45.720 --> 17:49.480 And then after that, everything is about margin 17:49.480 --> 17:52.320 and driving cost out of the system. 17:52.320 --> 17:55.400 What you also want is a sensor suite that works. 17:55.400 --> 18:01.280 And so it's great to tell a story about how it would be better 18:01.280 --> 18:04.560 to have a self driving system with a $50 sensor instead 18:04.560 --> 18:08.720 of a $500 sensor. 18:08.720 --> 18:11.560 But if the $500 sensor makes it work and the $50 sensor 18:11.560 --> 18:15.680 doesn't work, who cares? 18:15.680 --> 18:21.680 So long as you can actually have an economic opportunity there. 18:21.680 --> 18:23.760 And the economic opportunity is important, 18:23.760 --> 18:27.800 because that's how you actually have a sustainable business. 18:27.800 --> 18:30.440 And that's how you can actually see this come to scale 18:30.440 --> 18:32.520 and be out in the world. 18:32.520 --> 18:36.400 And so when I look at lidar, I see 18:36.400 --> 18:41.200 a technology that has no underlying fundamentally expense 18:41.200 --> 18:43.240 to it, fundamental expense to it. 18:43.240 --> 18:46.120 It's going to be more expensive than an imager, 18:46.120 --> 18:51.400 because CMOS processes or FAP processes 18:51.400 --> 18:56.200 are dramatically more scalable than mechanical processes. 18:56.200 --> 18:58.160 But we still should be able to drive cost 18:58.160 --> 19:00.440 out substantially on that side. 19:00.440 --> 19:05.880 And then I also do think that with the right business model, 19:05.880 --> 19:08.440 you can absorb more, certainly more cost 19:08.440 --> 19:09.480 on the below 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 cost down to zero. 19:15.480 --> 19:17.120 It's a basic economics. 19:17.120 --> 19:18.840 You've talked about your intuition 19:18.840 --> 19:22.720 at level two autonomy is problematic because 19:22.720 --> 19:27.280 of the human factor of vigilance, decrement, complacency, 19:27.280 --> 19:29.600 overtrust, and so on, just us being human. 19:29.600 --> 19:33.000 With the overtrust system, we start doing even more 19:33.000 --> 19:36.480 so partaking in the secondary activities like smartphone 19:36.480 --> 19:38.720 and so on. 19:38.720 --> 19:42.960 Have your views evolved on this point in either direction? 19:42.960 --> 19:44.760 Can you speak to it? 19:44.760 --> 19:48.240 So I want to be really careful, because sometimes this 19:48.240 --> 19:53.000 gets twisted in a way that I certainly didn't intend. 19:53.000 --> 19:59.360 So active safety systems are a really important technology 19:59.360 --> 20:03.400 that we should be pursuing and integrating into vehicles. 20:03.400 --> 20:05.680 And there's an opportunity in the near term 20:05.680 --> 20:09.400 to reduce accidents, reduce fatalities, and that's 20:09.400 --> 20:13.400 and we should be pushing on that. 20:13.400 --> 20:17.280 Level two systems are systems where 20:17.280 --> 20:19.480 the vehicle is controlling two axes, 20:19.480 --> 20:24.800 so breaking and thrall slash steering. 20:24.800 --> 20:27.200 And I think there are variants of level two systems that 20:27.200 --> 20:30.200 are supporting the driver that absolutely we 20:30.200 --> 20:32.560 should encourage to be out there. 20:32.560 --> 20:37.920 Where I think there's a real challenge is in the human factors 20:37.920 --> 20:40.800 part around this and the misconception 20:40.800 --> 20:44.920 from the public around the capability set that that enables 20:44.920 --> 20:48.000 and the trust that they should have in it. 20:48.000 --> 20:53.880 And that is where I'm actually incrementally more 20:53.880 --> 20:55.800 concerned around level three systems 20:55.800 --> 20:59.960 and how exactly a level two system is marketed and delivered 20:59.960 --> 21:03.240 and how much effort people have put into those human factors. 21:03.240 --> 21:07.000 So I still believe several things around this. 21:07.000 --> 21:10.760 One is people will over trust the technology. 21:10.760 --> 21:12.720 We've seen over the last few weeks 21:12.720 --> 21:16.280 a spate of people sleeping in their Tesla. 21:16.280 --> 21:23.240 I watched an episode last night of Trevor Noah talking 21:23.240 --> 21:27.160 about this, and this is a smart guy 21:27.160 --> 21:31.040 who has a lot of resources at his disposal describing 21:31.040 --> 21:32.880 a Tesla as a self driving car. 21:32.880 --> 21:35.640 And that why shouldn't people be sleeping in their Tesla? 21:35.640 --> 21:38.800 It's like, well, because it's not a self driving car 21:38.800 --> 21:41.120 and it is not intended to be. 21:41.120 --> 21:48.400 And these people will almost certainly die at some point 21:48.400 --> 21:50.400 or hurt other people. 21:50.400 --> 21:52.640 And so we need to really be thoughtful about how 21:52.640 --> 21:56.280 that technology is described and brought to market. 21:56.280 --> 22:00.760 I also think that because of the economic issue, 22:00.760 --> 22:03.320 economic challenges we were just talking about, 22:03.320 --> 22:06.960 that technology path will, these level two driver system 22:06.960 --> 22:08.400 systems, that technology path will 22:08.400 --> 22:11.560 diverge from the technology path that we 22:11.560 --> 22:15.800 need to be on to actually deliver truly self driving 22:15.800 --> 22:19.120 vehicles, ones where you can get in it and sleep 22:19.120 --> 22:21.480 and have the equivalent or better safety 22:21.480 --> 22:24.600 than a human driver behind the wheel. 22:24.600 --> 22:28.440 Because, again, the economics are very different 22:28.440 --> 22:29.800 in those two worlds. 22:29.800 --> 22:32.720 And so that leads to divergent technology. 22:32.720 --> 22:36.920 So you just don't see the economics of gradually 22:36.920 --> 22:41.520 increasing from level two and doing so quickly enough 22:41.520 --> 22:44.400 to where it doesn't cost safety, critical safety concerns. 22:44.400 --> 22:48.600 You believe that it needs to diverge at this point 22:48.600 --> 22:50.600 into different, basically different routes. 22:50.600 --> 22:53.760 And really that comes back to what 22:53.760 --> 22:56.840 are those L2 and L1 systems doing? 22:56.840 --> 22:59.800 And they are driver assistance functions 22:59.800 --> 23:04.360 where the people that are marketing that responsibly 23:04.360 --> 23:07.960 are being very clear and putting human factors in place 23:07.960 --> 23:12.400 such that the driver is actually responsible for the vehicle 23:12.400 --> 23:15.200 and that the technology is there to support the driver. 23:15.200 --> 23:19.880 And the safety cases that are built around those 23:19.880 --> 23:24.320 are dependent on that driver attention and attentiveness. 23:24.320 --> 23:30.360 And at that point, you can kind of give up, to some degree, 23:30.360 --> 23:34.280 for economic reasons, you can give up on, say, false negatives. 23:34.280 --> 23:36.200 And so the way to think about this 23:36.200 --> 23:40.760 is for a four collision mitigation braking system, 23:40.760 --> 23:45.080 if half the times the driver missed a vehicle in front of it, 23:45.080 --> 23:47.640 it hit the brakes and brought the vehicle to a stop, 23:47.640 --> 23:51.200 that would be an incredible, incredible advance 23:51.200 --> 23:52.960 in safety on our roads, right? 23:52.960 --> 23:55.080 That would be equivalent to seatbelts. 23:55.080 --> 23:57.560 But it would mean that if that vehicle wasn't being monitored, 23:57.560 --> 24:00.560 it would hit one out of two cars. 24:00.560 --> 24:05.080 And so economically, that's a perfectly good solution 24:05.080 --> 24:06.200 for a driver assistance system. 24:06.200 --> 24:07.360 What you should do at that point, 24:07.360 --> 24:09.200 if you can get it to work 50% of the time, 24:09.200 --> 24:11.040 is drive the cost out of that so you can get it 24:11.040 --> 24:13.320 on as many vehicles as possible. 24:13.320 --> 24:16.920 But driving the cost out of it doesn't drive up performance 24:16.920 --> 24:18.840 on the false negative case. 24:18.840 --> 24:21.480 And so you'll continue to not have a technology 24:21.480 --> 24:25.720 that could really be available for a self driven vehicle. 24:25.720 --> 24:28.480 So clearly the communication, 24:28.480 --> 24:31.640 and this probably applies to all four vehicles as well, 24:31.640 --> 24:34.440 the marketing and the communication 24:34.440 --> 24:37.080 of what the technology is actually capable of, 24:37.080 --> 24:38.440 how hard it is, how easy it is, 24:38.440 --> 24:41.040 all that kind of stuff is highly problematic. 24:41.040 --> 24:45.680 So say everybody in the world was perfectly communicated 24:45.680 --> 24:48.400 and were made to be completely aware 24:48.400 --> 24:50.040 of every single technology out there, 24:50.040 --> 24:52.880 what it's able to do. 24:52.880 --> 24:54.160 What's your intuition? 24:54.160 --> 24:56.920 And now we're maybe getting into philosophical ground. 24:56.920 --> 25:00.040 Is it possible to have a level two vehicle 25:00.040 --> 25:03.280 where we don't overtrust it? 25:04.720 --> 25:05.840 I don't think so. 25:05.840 --> 25:10.840 If people truly understood the risks and internalized it, 25:11.200 --> 25:14.320 then sure you could do that safely, 25:14.320 --> 25:16.200 but that's a world that doesn't exist. 25:16.200 --> 25:17.560 The people are going to, 25:19.440 --> 25:20.800 if the facts are put in front of them, 25:20.800 --> 25:24.480 they're gonna then combine that with their experience. 25:24.480 --> 25:28.400 And let's say they're using an L2 system 25:28.400 --> 25:31.040 and they go up and down the one on one every day 25:31.040 --> 25:32.800 and they do that for a month 25:32.800 --> 25:35.200 and it just worked every day for a month. 25:36.320 --> 25:37.400 Like that's pretty compelling. 25:37.400 --> 25:41.880 At that point, just even if you know the statistics, 25:41.880 --> 25:43.520 you're like, well, I don't know, 25:43.520 --> 25:44.840 maybe there's something a little funny about those. 25:44.840 --> 25:47.000 Maybe they're driving in difficult places. 25:47.000 --> 25:49.960 Like I've seen it with my own eyes, it works. 25:49.960 --> 25:52.480 And the problem is that that sample size that they have, 25:52.480 --> 25:54.000 so it's 30 miles up and down, 25:54.000 --> 25:58.800 so 60 miles times 30 days, so 60, 180, 1,800 miles. 26:01.720 --> 26:05.240 That's a drop in the bucket compared to the one, 26:05.240 --> 26:07.640 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 --> 26:17.720 because it's hard not to, it worked for a month. 26:17.720 --> 26:18.640 What's gonna change? 26:18.640 --> 26:21.600 So even if you start a perfect understanding of the system, 26:21.600 --> 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 --> 26:29.480 Over a year, over two years even, it doesn't have to be months. 26:29.480 --> 26:33.720 And I think that as this technology moves from, 26:35.440 --> 26:37.800 what I would say is kind of the more technology savvy 26:37.800 --> 26:41.480 ownership group to the mass market, 26:41.480 --> 26:44.640 you may be able to have some of those folks 26:44.640 --> 26:46.320 who are really familiar with technology, 26:46.320 --> 26:48.880 they may be able to internalize it better. 26:48.880 --> 26:50.840 And you're kind of immunization 26:50.840 --> 26:53.400 against this kind of false risk assessment 26:53.400 --> 26:56.960 might last longer, but as folks who aren't as savvy 26:56.960 --> 27:00.200 about that read the material 27:00.200 --> 27:02.200 and they compare that to their personal experience, 27:02.200 --> 27:08.200 I think there that it's gonna move more quickly. 27:08.200 --> 27:11.320 So your work, the program that you've created at Google 27:11.320 --> 27:16.320 and now at Aurora is focused more on the second path 27:16.640 --> 27:18.520 of creating full autonomy. 27:18.520 --> 27:20.920 So it's such a fascinating, 27:21.800 --> 27:24.600 I think it's one of the most interesting AI problems 27:24.600 --> 27:25.640 of the century, right? 27:25.640 --> 27:28.320 It's a, I just talked to a lot of people, 27:28.320 --> 27:30.400 just regular people, I don't know, my mom 27:30.400 --> 27:33.840 about autonomous vehicles and you begin to grapple 27:33.840 --> 27:38.080 with ideas 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 sound bite 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 --> 28:14.360 as part of that is like, here's the way we did the work. 28:15.320 --> 28:17.200 That means that we were very thorough. 28:17.200 --> 28:20.560 So, if you believe that we, what we said about, 28:20.560 --> 28:21.480 this is the way we did it, 28:21.480 --> 28:23.440 then you can have some confidence that we were thorough 28:23.440 --> 28:27.000 in the engineering work we put into the system. 28:27.000 --> 28:30.160 And then on top of that, to kind of demonstrate 28:30.160 --> 28:32.000 that we weren't just thorough, 28:32.000 --> 28:34.000 we were actually good at what we did. 28:35.320 --> 28:38.240 There'll be a kind of a collection of evidence 28:38.240 --> 28:40.480 in terms of demonstrating that the capabilities 28:40.480 --> 28:43.960 work the way we thought they did, statistically 28:43.960 --> 28:47.200 and to whatever degree we can demonstrate that 28:48.200 --> 28:50.320 both in some combination of simulation, 28:50.320 --> 28:54.720 some combination of unit testing and decomposition testing, 28:54.720 --> 28:57.000 and then some part of it will be on road data. 28:58.200 --> 29:03.200 And I think the way we'll ultimately convey this 29:03.320 --> 29:06.800 to the public is there'll be clearly some conversation 29:06.800 --> 29:08.240 with the public about it, 29:08.240 --> 29:12.080 but we'll kind of invoke the kind of the trusted nodes 29:12.080 --> 29:14.360 and that we'll spend more time being able to go 29:14.360 --> 29:17.280 into more depth with folks like NHTSA 29:17.280 --> 29:19.760 and other federal and state regulatory bodies 29:19.760 --> 29:22.600 and kind of given that they are operating 29:22.600 --> 29:25.120 in the public interest and they're trusted 29:26.240 --> 29:28.680 that if we can show enough work to them 29:28.680 --> 29:30.040 that they're convinced, 29:30.040 --> 29:33.840 then I think we're in a pretty good place. 29:33.840 --> 29:35.040 That means that you work with people 29:35.040 --> 29:36.960 that are essentially experts at safety 29:36.960 --> 29:39.040 to try to discuss and show, 29:39.040 --> 29:41.800 do you think the answer is probably no, 29:41.800 --> 29:44.360 but just in case, do you think there exists a metric? 29:44.360 --> 29:46.360 So currently people have been using 29:46.360 --> 29:48.200 a number of disengagement. 29:48.200 --> 29:50.160 And it quickly turns into a marketing scheme 29:50.160 --> 29:54.320 to sort of you alter the experiments you run to. 29:54.320 --> 29:56.320 I think you've spoken that you don't like. 29:56.320 --> 29:57.160 Don't love it. 29:57.160 --> 29:59.720 No, in fact, I was on the record telling DMV 29:59.720 --> 30:02.000 that I thought this was not a great metric. 30:02.000 --> 30:05.360 Do you think it's possible to create a metric, 30:05.360 --> 30:09.480 a number that could demonstrate safety 30:09.480 --> 30:12.400 outside of fatalities? 30:12.400 --> 30:16.640 So I do and I think that it won't be just one number. 30:16.640 --> 30:21.320 So as we are internally grappling with this 30:21.320 --> 30:23.600 and at some point we'll be able to talk 30:23.600 --> 30:25.080 more publicly about it, 30:25.080 --> 30:28.560 is how do we think about human performance 30:28.560 --> 30:32.200 in different tasks, say detecting traffic lights 30:32.200 --> 30:36.240 or safely making a left turn across traffic? 30:37.720 --> 30:40.040 And what do we think the failure rates 30:40.040 --> 30:42.520 are 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 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 --> 30:54.800 will work better than that. 30:54.800 --> 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 --> 31:11.640 is life saved and injuries reduced. 31:11.640 --> 31:15.320 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 and the high severity things. 31:45.280 --> 31:46.120 And I think that's something 31:46.120 --> 31:48.240 where we'll be able to look at as well 31:48.240 --> 31:51.920 because an event per 85 million miles 31:51.920 --> 31:54.480 is statistically a difficult thing 31:54.480 --> 31:59.440 even at the scale of the US to kind of compare directly. 31:59.440 --> 32:02.280 And that event, the fatality that's connected 32:02.280 --> 32:07.280 to an autonomous vehicle is significantly, 32:07.480 --> 32:09.160 at least currently magnified 32:09.160 --> 32:12.320 in the amount of attention you get. 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 --> 32:19.520 about autonomous vehicles in the public 32:19.520 --> 32:23.080 is the trolley problem formulation, right? 32:23.080 --> 32:27.040 Which has, let's not get into that too much 32:27.040 --> 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 --> 32:43.600 that autonomy is something 32:43.600 --> 32:45.480 that could be a part of their lives? 32:45.480 --> 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 --> 32:52.720 I think people should be skeptical. 32:52.720 --> 32:55.680 I think people should ask questions. 32:55.680 --> 32:57.000 I think they should doubt 32:58.040 --> 33:00.960 because this is something new and different. 33:00.960 --> 33:01.960 They haven't touched it yet. 33:01.960 --> 33:03.680 And I think it's perfectly reasonable. 33:03.680 --> 33:07.360 And but at the same time, 33:07.360 --> 33:09.360 it's clear there's an opportunity to make the road safer. 33:09.360 --> 33:12.480 It's clear that we can improve access to mobility. 33:12.480 --> 33:15.160 It's clear that we can reduce the cost of mobility. 33:16.680 --> 33:19.520 And that once people try that 33:19.520 --> 33:22.800 and understand that it's safe 33:22.800 --> 33:24.480 and are able to use in their daily lives, 33:24.480 --> 33:28.080 I think it's one of these things that will just be obvious. 33:28.080 --> 33:32.280 And I've seen this practically in demonstrations 33:32.280 --> 33:35.640 that I've given where I've had people come in 33:35.640 --> 33:38.600 and they're very skeptical. 33:38.600 --> 33:39.960 And they get in the vehicle. 33:39.960 --> 33:42.640 My favorite one is taking somebody out on the freeway 33:42.640 --> 33:46.080 and we're on the one on one driving at 65 miles an hour. 33:46.080 --> 33:48.560 And after 10 minutes, they kind of turn and ask, 33:48.560 --> 33:49.560 is that all it does? 33:49.560 --> 33:52.160 And you're like, it's self driving car. 33:52.160 --> 33:54.920 I'm not sure exactly what you thought it would do, right? 33:54.920 --> 33:57.960 But it becomes mundane, 33:58.920 --> 34:01.560 which is exactly what you want to technology 34:01.560 --> 34:02.760 like this to be, right? 34:02.760 --> 34:04.680 We don't really... 34:04.680 --> 34:07.320 When I turn the light switch on in here, 34:07.320 --> 34:12.040 I don't think about the complexity of those electrons 34:12.040 --> 34:14.240 being pushed down a wire from wherever it was 34:14.240 --> 34:15.880 and being generated. 34:15.880 --> 34:19.120 It's like, I just get annoyed if it doesn't work, right? 34:19.120 --> 34:21.440 And what I value is the fact 34:21.440 --> 34:23.120 that I can do other things in this space. 34:23.120 --> 34:24.600 I can see my colleagues. 34:24.600 --> 34:26.200 I can read stuff on a paper. 34:26.200 --> 34:29.240 I can not be afraid of the dark. 34:29.240 --> 34:32.840 And I think that's what we want this technology to be like 34:32.840 --> 34:34.160 is it's in the background 34:34.160 --> 34:36.520 and people get to have those life experiences 34:36.520 --> 34:37.880 and do so safely. 34:37.880 --> 34:41.600 So putting this technology in the hands of people 34:41.600 --> 34:45.800 speaks to scale of deployment, right? 34:45.800 --> 34:50.360 So what do you think the dreaded question about the future 34:50.360 --> 34:52.840 because nobody can predict the future? 34:52.840 --> 34:57.080 But just maybe speak poetically about 34:57.080 --> 35:00.600 when do you think we'll see a large scale deployment 35:00.600 --> 35:05.600 of autonomous vehicles, 10,000, those kinds of numbers. 35:06.360 --> 35:08.280 We'll see that within 10 years. 35:09.280 --> 35:10.600 I'm pretty confident. 35:10.600 --> 35:11.920 We... 35:13.920 --> 35:15.920 What's an impressive scale? 35:15.920 --> 35:19.000 What moment, so you've done the DARPA Challenge 35:19.000 --> 35:20.240 where there's one vehicle, 35:20.240 --> 35:22.000 at which moment does it become, 35:22.000 --> 35:23.720 wow, this is serious scale? 35:23.720 --> 35:27.960 So I think the moment it gets serious is when 35:27.960 --> 35:32.040 we really do have a driverless vehicle 35:32.040 --> 35:33.880 operating on public roads 35:34.760 --> 35:37.760 and that we can do that kind of continuously. 35:37.760 --> 35:38.640 Without a safety driver? 35:38.640 --> 35:40.240 Without a safety driver in the vehicle. 35:40.240 --> 35:41.320 I think at that moment, 35:41.320 --> 35:44.160 we've kind of crossed the zero to one threshold. 35:45.720 --> 35:50.000 And then it is about how do we continue to scale that? 35:50.000 --> 35:53.720 How do we build the right business models? 35:53.720 --> 35:56.040 How do we build the right customer experience around it 35:56.040 --> 35:59.680 so that it is actually a useful product out in the world? 36:00.720 --> 36:03.360 And I think that is really, 36:03.360 --> 36:05.720 at that point, it moves from a, 36:05.720 --> 36:08.960 what is this kind of mixed science engineering project 36:08.960 --> 36:12.120 into engineering and commercialization 36:12.120 --> 36:15.600 and really starting to deliver on the value 36:15.600 --> 36:18.000 that we all see here. 36:18.000 --> 36:20.680 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 --> 36:24.920 Where do we first see the inkling of 36:24.920 --> 36:28.600 no safety driver, one or two cars here and there? 36:28.600 --> 36:29.760 Is it on the highway? 36:29.760 --> 36:33.200 Is it in specific routes in the urban environment? 36:33.200 --> 36:36.960 I think it's gonna be urban, suburban type environments. 36:37.920 --> 36:38.920 You know, with Aurora, 36:38.920 --> 36:41.560 when we thought about how to tackle this, 36:42.400 --> 36:45.040 it was kind of invoke to think about trucking 36:46.000 --> 36:47.760 as opposed to urban driving. 36:47.760 --> 36:51.240 And again, the human intuition around this 36:51.240 --> 36:55.360 is that freeways are easier to drive on 36:57.040 --> 36:59.240 because everybody's kind of going in the same direction 36:59.240 --> 37:01.560 and lanes are a little wider, et cetera. 37:01.560 --> 37:03.280 And I think that that intuition is pretty good, 37:03.280 --> 37:06.000 except we don't really care about most of the time. 37:06.000 --> 37:08.360 We care about all of the time. 37:08.360 --> 37:10.840 And when you're driving on a freeway with a truck, 37:10.840 --> 37:15.840 say 70 miles an hour and you've got 70,000 pound load 37:15.840 --> 37:17.840 to do with you, that's just an incredible amount 37:17.840 --> 37:18.840 of kinetic energy. 37:18.840 --> 37:21.440 And so when that goes wrong, it goes really wrong. 37:22.600 --> 37:27.600 And that those challenges that you see occur more rarely 37:27.760 --> 37:31.040 so you don't get to learn as quickly. 37:31.040 --> 37:33.640 And they're incrementally more difficult 37:33.640 --> 37:35.920 than urban driving, but they're not easier 37:35.920 --> 37:37.400 than urban driving. 37:37.400 --> 37:41.600 And so I think this happens in moderate speed, 37:41.600 --> 37:43.840 urban environments, because there, 37:43.840 --> 37:46.560 if two vehicles crash at 25 miles per hour, 37:46.560 --> 37:50.040 it's not good, but probably everybody walks away. 37:51.000 --> 37:53.680 And those events where there's the possibility 37:53.680 --> 37:55.720 for that occurring happen frequently. 37:55.720 --> 37:57.920 So we get to learn more rapidly. 37:57.920 --> 38:01.320 We get to do that with lower risk for everyone. 38:02.440 --> 38:04.280 And then we can deliver value to people 38:04.280 --> 38:05.800 that need to get from one place to another. 38:05.800 --> 38:08.160 And then once we've got that solved, 38:08.160 --> 38:10.000 then the kind of the freeway driving part of this 38:10.000 --> 38:12.440 just falls out, but we're able to learn 38:12.440 --> 38:15.160 more safely, more quickly in the urban environment. 38:15.160 --> 38:18.480 So 10 years and then scale 20, 30 years. 38:18.480 --> 38:21.440 I mean, who knows if it's sufficiently compelling 38:21.440 --> 38:24.320 experience is created, it can be faster and slower. 38:24.320 --> 38:27.120 Do you think there could be breakthroughs 38:27.120 --> 38:29.880 and what kind of breakthroughs might there be 38:29.880 --> 38:32.360 that completely change that timeline? 38:32.360 --> 38:35.320 Again, not only am I asking to predict the future, 38:35.320 --> 38:37.280 I'm asking you to predict breakthroughs 38:37.280 --> 38:38.280 that haven't happened yet. 38:38.280 --> 38:41.800 So what's the, I think another way to ask that would be 38:41.800 --> 38:44.240 if I could wave a magic wand, 38:44.240 --> 38:46.640 what part of the system would I make work today 38:46.640 --> 38:48.600 to accelerate it as quickly as possible? 38:48.600 --> 38:49.440 Right. 38:52.080 --> 38:54.080 Don't say infrastructure, please don't say infrastructure. 38:54.080 --> 38:56.280 No, it's definitely not infrastructure. 38:56.280 --> 39:00.520 It's really that perception forecasting capability. 39:00.520 --> 39:04.760 So if tomorrow you could give me a perfect model 39:04.760 --> 39:07.520 of what's happening and what will happen 39:07.520 --> 39:11.400 for the next five seconds around a vehicle 39:11.400 --> 39:14.480 on the roadway, that would accelerate things 39:14.480 --> 39:15.320 pretty dramatically. 39:15.320 --> 39:17.560 Are you interested in staying up at night? 39:17.560 --> 39:21.680 Are you mostly bothered by cars, pedestrians, or cyclists? 39:21.680 --> 39:25.920 So I worry most about the vulnerable road users 39:25.920 --> 39:28.000 about the combination of cyclists and cars, right? 39:28.000 --> 39:29.480 Just cyclists and pedestrians 39:29.480 --> 39:31.880 because they're not in armor. 39:33.240 --> 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. 39:39.440 --> 39:44.080 Whereas a pedestrian or cyclist, they're out on the road 39:44.080 --> 39:46.520 and they don't have any protection. 39:46.520 --> 39:49.760 And so we need to pay extra attention to that. 39:49.760 --> 39:54.120 Do you think about a very difficult technical challenge 39:55.760 --> 39:58.560 of the fact that pedestrians, 39:58.560 --> 40:01.400 if you try to protect pedestrians by being careful 40:01.400 --> 40:04.600 and slow, they'll take advantage of that. 40:04.600 --> 40:07.560 So the game theoretic dance. 40:07.560 --> 40:10.880 Does that worry you from a technical perspective 40:10.880 --> 40:12.520 how we solve that? 40:12.520 --> 40:14.600 Because as humans, the way we solve that 40:14.600 --> 40:17.280 is kind of nudge our way through the pedestrians, 40:17.280 --> 40:20.040 which doesn't feel from a technical perspective 40:20.040 --> 40:22.320 as a appropriate algorithm. 40:23.240 --> 40:25.960 But do you think about how we solve that problem? 40:25.960 --> 40:30.960 Yeah, I think there's two different concepts there. 40:31.400 --> 40:35.880 So one is, am I worried that because these vehicles 40:35.880 --> 40:37.640 are self driving, people will kind of step on the road 40:37.640 --> 40:38.680 and take advantage of them. 40:38.680 --> 40:43.680 And I've heard this and I don't really believe it 40:43.800 --> 40:46.000 because if I'm driving down the road 40:46.000 --> 40:48.920 and somebody steps in front of me, I'm going to stop. 40:48.920 --> 40:49.760 Right? 40:49.760 --> 40:53.720 Like even if I'm annoyed, I'm not gonna just drive 40:53.720 --> 40:55.200 through a person stood on the road. 40:55.200 --> 40:56.440 Right. 40:56.440 --> 41:00.440 And so I think today people can take advantage of this 41:00.440 --> 41:02.600 and you do see some people do it. 41:02.600 --> 41:04.200 I guess there's an incremental risk 41:04.200 --> 41:05.920 because maybe they have lower confidence 41:05.920 --> 41:06.760 that I'm going to see them 41:06.760 --> 41:09.360 than they might have for an automated vehicle. 41:09.360 --> 41:12.080 And so maybe that shifts it a little bit. 41:12.080 --> 41:14.400 But I think people don't want to get hit by cars. 41:14.400 --> 41:17.120 And so I think that I'm not that worried 41:17.120 --> 41:18.800 about people walking out of the one on one 41:18.800 --> 41:21.840 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 --> 41:33.480 I think that is further down the technology pipeline. 41:33.480 --> 41:36.920 I think that you're right, that's tricky. 41:36.920 --> 41:38.560 I don't think it's necessarily, 41:40.320 --> 41:43.360 I think the algorithm people use for this is pretty simple. 41:43.360 --> 41:44.200 Right? 41:44.200 --> 41:45.040 It's kind of just move forward slowly 41:45.040 --> 41:47.600 and if somebody's really close and stop. 41:47.600 --> 41:50.840 And I think that that probably can be replicated 41:50.840 --> 41:54.040 pretty easily and particularly given that it's, 41:54.040 --> 41:57.240 you don't do this at 30 miles an hour, you do it at one, 41:57.240 --> 41:59.080 that even in those situations, 41:59.080 --> 42:01.200 the risk is relatively minimal. 42:01.200 --> 42:03.440 But it's not something we're thinking 42:03.440 --> 42:04.560 about in any serious way. 42:04.560 --> 42:08.000 And probably that's less an algorithm problem 42:08.000 --> 42:10.160 more creating a human experience. 42:10.160 --> 42:14.320 So the HCI people that create a visual display 42:14.320 --> 42:16.280 that you're pleasantly as a pedestrian, 42:16.280 --> 42:17.680 nudged out of the way. 42:17.680 --> 42:21.960 That's an experience problem, not an algorithm problem. 42:22.880 --> 42:25.480 Who's the main competitor to Aurora today? 42:25.480 --> 42:28.600 And how do you out compete them in the long run? 42:28.600 --> 42:31.200 So we really focus a lot on what we're doing here. 42:31.200 --> 42:34.440 I think that, I've said this a few times 42:34.440 --> 42:37.960 that this is a huge difficult problem 42:37.960 --> 42:40.280 and it's great that a bunch of companies are tackling it 42:40.280 --> 42:42.320 because I think it's so important for society 42:42.320 --> 42:43.760 that somebody gets there. 42:45.200 --> 42:49.040 So we don't spend a whole lot of time 42:49.040 --> 42:51.560 like thinking tactically about who's out there 42:51.560 --> 42:55.480 and how do we beat that person individually? 42:55.480 --> 42:58.680 What are we trying to do to go faster ultimately? 42:58.680 --> 43:02.600 Well, part of it is the leisure team we have 43:02.600 --> 43:04.160 has got pretty tremendous experience. 43:04.160 --> 43:06.400 And so we kind of understand the landscape 43:06.400 --> 43:09.120 and understand where the cul de sacs are to some degree. 43:09.120 --> 43:10.920 And we try and avoid those. 43:12.600 --> 43:14.240 I think there's a part of it 43:14.240 --> 43:16.240 just this great team we've built. 43:16.240 --> 43:19.040 People, this is a technology and a company 43:19.040 --> 43:22.280 that people believe in the mission of. 43:22.280 --> 43:24.760 And so it allows us to attract just awesome people 43:24.760 --> 43:25.680 to go work. 43:26.760 --> 43:28.000 We've got a culture, I think, 43:28.000 --> 43:30.440 that people appreciate, that allows them to focus, 43:30.440 --> 43:33.080 allows them to really spend time solving problems. 43:33.080 --> 43:35.880 And I think that keeps them energized. 43:35.880 --> 43:40.880 And then we've invested heavily in the infrastructure 43:43.520 --> 43:46.520 and architectures that we think will ultimately accelerate us. 43:46.520 --> 43:50.640 So because of the folks we're able to bring in early on, 43:50.640 --> 43:53.520 because of the great investors we have, 43:53.520 --> 43:56.760 we don't spend all of our time doing demos 43:56.760 --> 43:58.680 and kind of leaping from one demo to the next. 43:58.680 --> 44:02.800 We've been given the freedom to invest in 44:03.960 --> 44:05.480 infrastructure to do machine learning, 44:05.480 --> 44:08.600 infrastructure to pull data from our on road testing, 44:08.600 --> 44:11.480 infrastructure to use that to accelerate engineering. 44:11.480 --> 44:14.480 And I think that early investment 44:14.480 --> 44:17.320 and continuing investment in those kind of tools 44:17.320 --> 44:19.400 will ultimately allow us to accelerate 44:19.400 --> 44:21.920 and do something pretty incredible. 44:21.920 --> 44:23.400 Chris, beautifully put. 44:23.400 --> 44:24.640 It's a good place to end. 44:24.640 --> 44:26.520 Thank you so much for talking today. 44:26.520 --> 44:27.360 Thank you very much. 44:27.360 --> 44:57.200 I hope you enjoyed it.