Chris Urmson: Self-Driving Cars at Aurora, Google, CMU, and DARPA

The following is a conversation with Chris Urmson. He was the 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 long time 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 lexfriedman, spelled F R I D M A N. And now here's my conversation with Chris Armsson.

You were part of both the DARPA Grand Challenge and the DARPA Urban Challenge teams at CMU with Red Whitaker. What technical or philosophical things have you learned from these races?

I think the high order bit was that it could be done. I think that was the thing that was incredible about the first, the Grand Challenges that I remember. 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 at the time we were the only robotics institute around and so if we went into it and fell on our faces, that would be embarrassing. So I think just having the will to go do it, to try to do this thing that at the time was marked as darn near impossible, and. And then after a couple of tries, be able to actually make it happen, I think that was really exciting.

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

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

What were the biggest pain points? Mechanical sensors, hardware software, algorithms for mapping, localization, just general perception control, like hardware software.

First of all, I think that's the joy of this field, is that it's all hard and that you have to be good at each part of it. So for the urban challenges, if I look back at it from today, it should be easy today that it was a static world, there weren't other actors moving through it, is what that means. It was out in the desert, so you get really good gps. So that went. And we could map it roughly. And so in retrospect, now it's within the realm of things we could do back then, just actually getting the vehicle and the, you know, there's a bunch of engineering work to get the vehicle so that we could control it and drive it. That's, you know, that's still a pain today. But it was even more so back then. And then the uncertainty of exactly what they wanted us to do was, was part of the challenge as well.

Right. You didn't actually know the track heading in here. You knew approximately, but, you know, didn't actually know the route, the route that it's going to be taken. That's right.

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

So the legend Red was kind of leading that effort in terms of just broadly speaking. So you're a leader now. What have you learned from Red about leadership?

I think there's a couple of things. One is go and try those really hard things. That's where there is an incredible opportunity. I think the other big one though is to see people for who they can be, not who they are. It's one of the things that I actually one of the deepest lessons I learned from Red was that he would look at undergraduates or graduate students and empower them to be leaders, to have responsibility to do great things, that I think another person might look at them and think, oh well, that's just an undergraduate student. What could they know? And so I think that kind of trust but verify, have confidence in what people can become, I think is a really powerful thing.

So through that, let's just fast forward through the history. Can you maybe talk through the technical evolution of autonomous vehicle systems from the first two grand challenges to the Urban Challenge to today? Are there major shifts in your mind or is it the same kind of technology just made more robust?

I think there's been some big steps. So the, for the grand challenge, the real technology that unlocked that was HD mapping. Prior to that, a lot of the off road robotics work had been done without any real prior model of what the vehicle was going to encounter. And so that innovation that the fact that we could get decimeter resolution models was really a big deal and that allowed us to kind of bound the complexity of the driving problem the vehicle had and allowed it to operate at speed because we could assume things about the environment that it was going to encounter. So that was the big step there for the Urban Challenge. One of the big technological innovations there was the multi beam LIDAR and being able to generate high resolution mid to long range 3D models of the world and use that for understanding the world around the vehicle. And that was really kind of a game changing technology. In parallel with that, we saw a bunch of other technologies that had been kind of converging half their day in the sun. So Bayesian estimation had been. SLAM had been a big field in robotics. You would go to a conference a couple years before that and every paper would effectively have slammed somewhere in it. And and so seeing those Bayesian estimation techniques play out on a very visible stage, I thought that was pretty exciting to see.

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

Well, yeah, and in fact we weren't really doing SLAM per se in real time because we had a model ahead of time, we had a roadmap, but we were doing localization and we were using the LIDAR or the cameras, depending on who exactly was doing it, to localize to a model of the world. And I thought that was a big step from naively trusting GPS ins. Before that, again, lots of work had been going on in this field. Certainly this was not doing Anything particularly innovative in SLAM or in localization, but it was seeing that technology necessary in a real application on a big stage, I thought was very cool.

So for the urban challenge, there was already maps constructed offline? Yes, in general. Okay. And did people do that? Individual. Individual teams do it individually, so they had their own different, different approaches there, or did everybody kind of share that information, at least intuitively?

So, so the DARPA gave all the teams a model of the world, a map. And then one of the things that we had to figure out back then was, and it's still one of these things that trips people up today is actually the coordinate system. So you get a latitude, longitude, and to so many decimal places, you don't really care about the ellipsoid of the earth that's being used. But when you want to get to 10 centimeter or centimeter resolution, you care whether the, the coordinate system is NADS 83 or WGS 84 or these are different ways to describe both the kind of non sphericalness of the Earth, but also I can't remember which one the tectonic shifts that are happening and how to transform the global datum as a function of that. So getting a map and then actually matching it to reality, to centimeter resolution, that was kind of interesting and fun, fun back then.

So how much work was the perception doing there? So how much were you relying on localization based on maps without using perception to register to the maps? And I guess the question is how advanced was perception at that point?

It's certainly behind where we are today. We're more than a decade since the urban challenge, but the core of it was there that we were tracking vehicles. We had to do that at 100 plus meter range because we had to merge with other traffic. We were using again, Bayesian estimates for state of these vehicles. We had to deal with a bunch of the problems that you think of today of predicting where that vehicle is going to be a few seconds into the future. We had to deal with the fact that there were multiple hypotheses for that, because a vehicle at an intersection might be going right or it might be going straight or might be making a left turn. And we had to deal with the challenge of the fact that our behavior was going to impact the behavior of that other operator. And we did a lot of that in relatively naive ways, but it kind of worked.

Still had to have some kind of solution. And so where does that, 10 years later, where does that take us today? From that artificial city construction to real cities to the urban Environment.

Yeah. I think the biggest thing is that the actors are truly unpredictable. That most of the time the drivers on the road, the other road users are out there behaving well, but every once in a while they're not the variety of other vehicles. You have all of them in terms.

Of behavior, in terms of perception or.

Both, that back then we didn't have to deal with cyclists, we didn't have to deal with pedestrians, didn't have to deal with traffic lights. The scale over which that you have to operate is now is much larger than the air base that we were thinking about back then.

So, easy question. What do you think is the hardest part about driving?

Easy question.

Yeah.

No, I'm joking. I'm sure nothing really jumps out at you as one thing, but in the jump from the urban challenge to the real world, is there something that's a particular. You foresee as very serious, difficult challenge?

I think the most fundamental difference is that we're doing it for real. That in that environment, it was both a limited complexity environment because certain actors weren't there because the roads were maintained. There were barriers keeping people separate from robots at the time. And it only had to work for 60 miles, which, looking at it from 2006, it had to work for 60 miles. Looking at it from now, you know, we. We want things that will go and drive for, you know, half a. Half a million miles. And, you know, it's just a. It's a different game.

So how important. You said lidar came into the game early on, and it's really the primary driver of autonomous vehicles today as a sensor. So how important is the role of lidar in the sensor suite in the near term?

So I think it's. I think it's essential, but I also believe the cameras are essential and I believe the radar is essential. I think that you really need to use the composition of data from these different sensors if you want the thing to really be robust.

The question I want to ask, let's see if we can untangle it, is what are your thoughts on the Elon Musk provocative statement that LIDAR is a crutch? That is a kind of, I guess, growing pains and that much of the perception task can be done with cameras.

So I think it is undeniable that people walk around without lasers in their foreheads and they can get into vehicles and drive them. And so there's an existence proof that you can drive using passive vision. No doubt. Can't argue with that.

In terms of sensors.

Yeah, in terms of sensors. Right. So there's an example that we all go do it, many of us every day. In terms of lidar being a crutch, sure. But in the same way that the combustion engine was a crutch on the path to an electric vehicle, in the same way that you, any technology ultimately gets replaced by some superior technology in the future. And really the way that I look at this is that the way we get around on the ground, the way that we use transportation is broken and that we have this, I think the number I saw this morning, 37,000Americans killed last year on our roads. And that's just not acceptable. Any technology that we can bring to bear that accelerates this self driving technology coming to market and saving lives is technology we should be using. It feels just arbitrary to say, well, I'm not okay with using lasers because that's whatever, but I am okay with using an 8 megapixel camera, a 16 megapixel camera. You know, like it's just, these are just bits of technology and we should be taking the best technology from the tool bin that allows us to go and, you know, and solve a problem.

The question I often talk to, well obviously you do as well to the sort of automotive companies and you know, if there's one word that comes up more often than anything, it's cost and trying to drive cost down. So while it's true that it's, it's a tragic number, the 37,000. The question is what? And I'm not the one asking this question because I hate this question, but we want to find the cheapest sensor suite that creates a safe vehicle. In that uncomfortable trade off, do you foresee lidar coming down in cost in the future or do you see a day where level 4 autonomy is possible without lidar?

I see both of those, but it's really a matter of time. And I think really maybe I would talk to the question you asked about the cheapest sensor. I don't think that's actually what you want. What you want is a sensor suite that is economically viable. And then after that everything is about margin and driving cost out of the system. What you also want is a sensor suite that works. It's great to tell a story about how it'd be better to have a self driving system with a $50 sensor instead of a $500 sensor. But if the $500 sensor makes it work and the $50 sensor doesn't work, who cares so long as you can actually have an economic. There's an economic opportunity there and important because that's how you actually have a sustainable business and that's how you can actually see this come to scale and be out in the world. When I look at lidar, I see a technology that has no underlying fundamental expense to it. It's going to be more expensive than an imager because CMOS processes or FAT processes are dramatically more scalable than mechanical processes. But we still should be able to drive costs down substantially on that side. And then I also do think that with the right business model you can absorb more, certainly more cost on the bill of materials.

Yeah. If the sensor suite works, extra value is provided thereby you don't need to drive cost down to zero. It's basic economics. You've talked about your intuition that level 2 autonomy is problematic because of the human factor of vigilance, decrement, complacency over trust and so on. Just us being human, we over trust the system, we start doing even more so partaking in the secondary activities like smartphones and so on. Have your views evolved on this point in either direction? Can you speak to it?

I want to be really careful because sometimes this gets twisted in a way that I certainly didn't intend. So active safety systems are a really important technology that we should be pursuing and integrating into vehicles and there's an opportunity in the near term to reduce accidents, reduce fatalities and that's. And we should be pushing on that. Level 2 systems are systems where the vehicle is controlling two axes, so braking and throttle steering. And I think there are variants of level 2 systems that are supporting the driver that absolutely we should encourage to be out there. Where I think there's a real challenge is in the human factors part around this and the misconception from the public around the capability set that that enables and the trust they should have in it. And that is where I'm actually incrementally more concerned around Level 3 systems and how exactly a Level 2 system is marketed and delivered and how much effort people have put put into those human factors. So I still believe several things around this. One is people will over trust the technology. We've seen over the last few weeks a spate of people sleeping in their Tesla. I watched an episode last night of Trevor Noah talking about this and him, this is a smart guy who's has a lot of resources at his disposal describing a Tesla as a self driving car and that why shouldn't people be sleeping in their Tesla? It's like, well, because it's not a self driving car and it is not intended to be. And these People will almost certainly die at some point or hurt other people. And so we need to really be thoughtful about how that technology is described and, and brought to market. I also think that because of the economic issue, economic challenges, we were just talking about that that technology path will, these level two driver assistance systems, that technology path will diverge from the technology path that we need to be on to actually deliver truly self driving vehicles, ones where you can get in and sleep and have the equivalent or better safety than a human driver behind the wheel. Because again, the are very different in those two worlds. And so that leads to divergent technology.

So you just don't see the economics of gradually increasing from level two and doing so quickly enough to where it doesn't cause safety critical safety concerns. You believe that it needs to diverge at this point into basically different routes.

And really that comes back to what are those L2 and L1 systems doing? And they are driver assistance functions where the people that are marketing that responsibly are being very clear and putting human factors in place such that the driver is actually responsible for the vehicle and that the technology is there to support the driver. And the safety cases that are built around those are dependent on that driver attention and attentiveness. And at that point you can kind of give up to some degree for economic reasons. You can give up on say false negatives. The way to think about this is for a Ford collision mitigation braking system. If half the times the driver missed a vehicle in front of hit the brakes and brought the vehicle to a stop, that would be an incredible, incredible advance in safety on our roads. That would be equivalent to seat belts. But it would mean that if that vehicle wasn't being monitored, it would hit one out of two cars. And so economically that's a perfectly good solution for a driver assistance system. What you should do at that point, if you can get it to work 50% of the time, is drive the cost out of that so you can get it on as many vehicles as possible. But driving the cost out of it doesn't drive up performance on the false negative case. And so you'll continue to not have a technology that could really be available for a self driven vehicle.

So clearly the communication, and this probably applies to level four vehicles as well. The marketing and the communication of what the technology is actually capable of, how hard it is, how easy it is, all that kind of stuff is highly problematic. But say everybody in the world was perfectly communicated and were made to be completely aware of every single technology out there. What they what it's able to do, what's your intuition? And now maybe getting into philosophical ground. Is it possible to have a level two vehicle where we don't over trust it?

I don't think so. If people truly understood the risks and internalized it, then sure, you could do that safely. But that's a world that doesn't exist, that people are going to, if the facts are put in front of them, they're going to then combine that with their experience. Let's say they're using an L2 system and they go up and down the 101 every day and they do that for a month and it just worked every day for a month. That's pretty compelling at that point. Just even if you know the statistics, you're like, well, I don't know, maybe there's something a little funny about those. Maybe they're driving in difficult places. I've seen it with my own eyes, it works. And the problem is that that sample size that they have, so it's 30 miles up and down, so 60 miles times 30 days. So 60, 180, 1,800 miles, that's a drop in the bucket compared to the 1, what, 85 million miles between fatalities. And so they don't really, really have a true estimate based on their personal experience of the real risks. But they're going to trust it anyway because it's hard not to. It worked for a month, what's going to change?

So even if you start a perfect understanding of the system, your own experience will make it drift. That's a big concern. Over a year, over two years even. It doesn't have to be months.

And I think that as this technology moves from what I would say is the more technology savvy ownership group to the mass market, you may be able to have some of those folks who are really familiar with technology, they may be able to internalize it better. And your immunization against this false risk assessment might last longer. But as folks who aren't as savvy about that read the material and they compare that to their personal experience, I think there that it's going to move more quickly.

So your work, the program that you've created at Google and now at Aurora is focused more on the second path of creating full autonomy. So it's such a fascinating, I think it's one of the most interesting AI problems of the century. Right. It's. I just talked to a lot of people, just regular people, I don't know my mom about autonomous vehicles. And you begin to Grapple with ideas of giving your life control over to a machine. It's philosophically interesting. It's practically interesting. So let's talk about safety. How do you think we demonstrate. You've spoken about metrics in the past. How do you think we demonstrate to the world that an autonomous vehicle, an Aurora system, is safe?

This is one where it's difficult because there isn't a sound bite answer that we have to show a combination of work that was done diligently and thoughtfully. And this is where something like a functional safety process is. Part of that is like, here's the way we did the work. That means that we were very thorough. So if you believe what we said about this is the way we did it, then you can have some confidence that we were thorough in. In the engineering work we put into the system. And then on top of that, to kind of demonstrate that we weren't just thorough, we were actually good at what we did, there'll be kind of a collection of evidence in terms of demonstrating that the capabilities work the way we thought they did statistically and to whatever degree. We can demonstrate that both in some combination of simulation, some combination of. Of unit testing and decomposition testing, and then some part of it will be on road data. And I think the way we'll ultimately convey this to the public is there'll be clearly some conversation with the public about it, but we'll kind of invoke the trusted nodes and that we'll spend more time being able to go into more depth with folks like NHTSA and other federal and state regulatory bodies and kind of given that they are operating in the public interest and they're trusted, that if we can show enough work to them that they're convinced, then I think we're in a pretty good place.

That means you work with people that are essentially experts at safety to try to discuss. And so do you think the answer is probably no. But just in case, do you think there exists a metric. So currently people have been using number of disengagements.

Yeah.

And it quickly turns into a marketing scheme to just sort of. You alter the experiments you run to adjust. I think you've spoken that you don't like, don't love it.

No. In fact, I was on the record telling DMV that I thought this was not a great metric.

Do you think it's possible to create a metric, a number that. That could demonstrate safety outside of fatalities?

So I do, and I think that it won't be just one number. So as we are internally grappling with this and at some point we'll be able to talk more publicly about it is how do we think about human performance in different tasks, say detecting traffic lights or safely making a left turn across traffic. And what do we think the failure rates are for those different capabilities for people and then demonstrating to ourselves and then ultimately folks in regulatory role and then ultimately the public that we have confidence that our system will work better than that. And so these individual metrics will tell a compelling story ultimately. I do think at the end of the day, what we care about in terms of safety is lives saved and injuries reduced and then ultimately kind of casualty dollars that people aren't having to pay to get their car fixed. And I do think that you can. In aviation they look at a kind of an event pyramid where a crash is at the top of that and that's the worst event, obviously. And then there's injuries and near miss events and whatnot and, and violation of operating procedures. And you kind of build a statistical model of the relevance of the low severity things to the high severity things. And I think that's something we'll be able to look at as well. Because an event per 85 million miles is statistically a difficult thing even at the scale of the US to kind of compare directly.

And that event of fatality that's connected to an autonomous vehicle is significantly, at least currently magnified in the amount of attention it gets. So that speaks to public perception. I think the most popular topic about autonomous vehicles in the public is the trolley problem formulation. Right?

Sure.

Which has not get into that too much, but is misguided in many ways. But it speaks to the fact that people are grappling with this idea of giving control over to a machine. So how do you win the hearts and minds of the people that autonomy is something that could be a part of their lives?

I think you let them experience it. Right? I think it's right. I think people should be skeptical. I think people should ask questions, I think they should doubt because this is something new and different. They haven't touched it yet. And I think it's perfectly reasonable. But at the same time, it's clear there's an opportunity to make the road safer. It's clear that we can improve access to mobility. It's clear that we can reduce the cost of mobility. And that once people try that and understand that it's safe and are able to use it in their daily lives, I think it's one of these things that will just be obvious. And I've Seen this practically in demonstrations that I've given where I've had people come in and they're very skeptical and they get in a vehicle. My favorite one is taking somebody out on the freeway. And we're on the 101 driving at 65 miles an hour, and after 10 minutes they kind of turn and ask, is that all it does? And you're like, it's a self driving car. I'm not sure exactly what you thought it would do, right? But it becomes mundane, which is exactly what you want a technology like this to be. We don't really, when I turn the light switch on in here, I don't think about the complexity of those electrons, you being pushed down a wire from wherever it was and being generated. It's like I just get annoyed if it doesn't work. Right. And what I value is the fact that I can do other things in this space. I can see my colleagues, I can read stuff on a paper. I can not be afraid of the dark. And I think that's what we want this technology to be like, is it's in the background and people get to have those life experiences and do so safely.

So putting this technology in the hands of people speaks to scale of deployment, right? So what do you think? The dreaded question about the future, because nobody can predict the future, but just maybe speak poetically about when do you think we'll see a large scale deployment of autonomous vehicles? 10,000, those kinds of numbers, we'll see.

That within 10 years. I'm pretty confident. We.

What's an impressive scale? What moment? So you've done DARPA challenge where there's one vehicle, at which moment does it become, wow, this is serious scale.

So I think the moment it gets serious is when we really do have a driverless vehicle operating on public roads and that we can do that kind of continuously without a safety driver, Without a safety driver in the vehicle. I think at that moment we've kind of crossed the 0 to 1 threshold. And then it is about how do we continue to scale that? How do we build the right business models? How do we build the right customer experience around it so that it is actually a useful product out in the world? I think that is really, at that point it moves from what is this mixed science engineering project into engineering and commercialization and really starting to deliver on the value that we all see here and actually making that real in the world.

What do you think that deployment looks like? Where do we first see the inkling of no safety driver, one or two cars Here and there. Is it on the highway, is it in specific routes in the urban environment?

I think it's going to be urban, suburban type environments. With Aurora, when we thought about how to tackle this, it's kind of en vogue to think about trucking as opposed to urban driving. Again, the human intuition around this is that freeways are easier to drive on because everybody's kind of going in the same direction and the lanes are a little wider, et cetera. And I think that that intuition is pretty good, except we don't really care about most of the time, we care about all of the time. And when you're driving on a freeway with a truck, say at 70 miles an hour, and you've got 70,000 pound load with you, that's just an incredible amount of kinetic energy. And so when that goes wrong, it goes really wrong. Those challenges that you see occur more rarely, so you don't get to learn as quickly. And they're incrementally more difficult than urban driving, but they're not easier than urban driving. And so I think this happens in moderate speed urban environments because if two vehicles crash at 25 miles per hour, it's not good, but probably everybody walks away and those events where there's the possibility for that occurring happen frequently. So we get to learn more rapidly, we get to do that with lower risk for everyone. And then we can deliver value to people that need to get from one place to another. And then once we've got that solved, then the kind of the freeway driving part of this just falls out. But we are able to learn more safely, more quickly in the urban environment.

So 10 years and then scale, 20, 30 year, I mean, who knows? If a sufficiently compelling experience is created, it could be faster and slower. Do you think there could be breakthroughs? And what kind of breakthroughs might there be that completely change that timeline? Again, not only am I asking to predict the future. Oh yeah, I'm asking you to predict breakthroughs that haven't happened yet.

So what's the. I think another way to ask that was would be if I could wave a magic wand, what part of the system would I make work today to accelerate it as quick as possible, quickly as possible?

Don't say infrastructure. Please don't say infrastructure.

No, it's definitely not infrastructure. It's really that perception forecasting capability. So if tomorrow you could give me a perfect model of what is happening and what will happen for the next five seconds around a vehicle on the roadway, that would accelerate things pretty dramatically.

Are you in terms of staying up at night, are you mostly bothered by cars, pedestrians or cyclists?

So I worry most about the vulnerable road users, about the combination of cyclists and cars, cyclists and pedestrians, because they're not in armor, the cars, they're bigger, they've got protection for the people and so the ultimate risk is lower there. Whereas a pedestrian or cyclist, they're out on the road and they don't have any protection. And so we need to pay extra attention to that.

Do you think about a very difficult technical challenge of the fact that pedestrians, if you try to protect pedestrians by being careful and slow, they'll take advantage of that. So the game theoretic dance, does that worry you of how from a technical perspective, how we solve that? Because as humans the way we solve that is kind of nudge our way through the pedestrians, which doesn't feel from a technical perspective as a appropriate algorithm. But do you think about how we solve that problem?

Yeah, I think there's, I think that was actually there's two different concepts there. So one is am I worried that because these vehicles are self driving people kind of step in the road and take advantage of them. And I've heard this and I don't really believe it because if I'm driving down the road and somebody steps in front of me, I'm going to stop even if I'm annoyed. I'm not going to just drive through a person stood on the road. And so I think today people can take advantage of this. And you do see some people do it. I guess there's an incremental risk because maybe they have lower confidence that I'm going to see them than they might have for an automated vehicle. And so maybe that shifts it a little bit. But I think people don't want to get hit by cars. And so I think that I'm not that worried about people walking out of the one on one and creating chaos more than they would today. Regarding kind of the nudging through a big stream of pedestrians, leaving a concert or something, I think that is further down the technology pipeline. I think that you're right, that's tricky. I don't think it's necessarily. I think the algorithm people use for this is pretty simple. It's kind of just move forward slowly and if somebody's really close then stop. And I think that that probably can be replicated pretty easily. And particularly given that you don't do this at 30 miles an hour, you do it at 1. That even in those situations the risk is relatively minimal. But it's not something we're thinking about in any serious way.

And probably that's less an algorithm problem and more creating a human experience. The HCI people that create a visual display that you're pleasantly as a pedestrian nudged out of the way, that's an experience problem, not an algorithm problem. Who's the main competitor to Aurora today and how do you out compete them in the long run?

So we really focus a lot on what we're doing here. I think that I've said this a few times, that this is a huge difficult problem and it's great that a bunch of companies are tackling it because I think it's so important for society that somebody gets there. So we don't spend a whole lot of time thinking tactically about who's out there and how do we beat that person individually. What are we trying to do to go faster ultimately? Well, part of it is the leadership team we have has got pretty tremendous experience and so we kind of understand the landscape and understand where the cul de sacs are to some degree and we try and avoid those. I think there's a part of it just this great team we've built people, this is a technology and a company that people believe in the mission of and so it allows us to attract just awesome people to go work. We've got a culture I think that people appreciate that allows them to focus, allows them to really spend time solving problems and I think that keeps them energized. And then we've invested hard invested heavily in the infrastructure and architectures that we think will ultimately accelerate us. So because of the folks we're able to bring in early on, because of the great investors we have, we don't spend all of our time doing demos and kind of leaping from one demo to the next. We've been given the freedom to invest in infrastructure, to do machine learning infrastructure, to pull data from our on road testing infrastructure, to use that to accelerate engineering. And I think that that early investment and continuing investment in those kind of tools will ultimately allow us to accelerate and do something pretty incredible.

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

Thank you very much. Really enjoyed it.

Sa.