Chris Urmson: Self-Driving Cars at Aurora, Google, CMU, and DARPA | Lex Fridman Podcast #28
the following is a conversation with Chris Urmson he was a CTO of the Google self-driving car team a key engineer and leader behind the Carnegie Mellon University autonomous vehicle entries in the DARPA Grand Challenges and the winner of the DARPA urban challenge today he's the CEO of Aurora innovation and the autonomous vehicle software company he started with sterling Anderson who was the former director of Tesla autopilot and drew back now Uber's former autonomy and perception lead chris is one of the top roboticists and autonomous vehicle experts in the world and a longtime voice of reason in a space that is shrouded in both mystery and hype he both acknowledges the incredible challenges involved in solving the problem of autonomous driving and is working hard to solve it this is the artificial intelligence podcast if you enjoy it subscribe on YouTube give it five stars and iTunes supported on patreon or simply connect with me on Twitter at Lex Friedman spelled Fri D ma a.m. and now here's my conversation with Chris Armisen you were part of both the DARPA Grand Challenge and the DARPA urban challenge teams at CMU with red Whittaker what technical or philosophical things have you learned from these races I think the the high order bit was that it could be done I think that was the thing that was incredible about the first the the Grand Challenges that I remember you know I was a grad student at Carnegie Mellon and there we was kind of this dichotomy of it seemed really hard so that'd be cool and interesting but you know at the time we were the only robotics Institute around and so if we went into it and fell on our faces that would that would be embarrassing so I think you know just having the will to go do it to try to do this thing that at the time was marked as you know darn near impossible and and then after a couple of tries be able to actually make it happen I think that was you know that was really exciting 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 al huddle 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 gonna be hard we didn't know how we're gonna 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 that yeah I guess you have to I think there's a certain benefit to naivete right that if you don't know how hard something really is you you try different things and you know gives you an opportunity that others who are you know wiser maybe don't don't have what were the biggest pain points mechanical sensors hardware software algorithms for mapping localization just general perception control what the hardware soft first of all I think that's the joy of this field is that it's all hard and that's you have to be good at at each part of it so for the first for the urban challenges if I look back at it from today it should be easy today that you know it was a static world there weren't other actors moving through it that is what that means it was out in the desert so you get really good GPS you know so that that went in you know we could map it roughly and so in retrospect now it's you know it's it's within the realm of things we could do back then just actually getting the vehicle and the you know there's a bunch of engineering work to get the vehicle so that we could control 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 you know approximately but you know it didn't actually know the route the route that's gonna be taken that's right we didn't know the route 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 to that the the government would give us the 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 you know each of those waypoints and so the the most devious thing they could have done is set you know a kilometer wide corridor across you know a field of scrub brush and rocks and said you know go figure it out fortunately it really it turned into basically driving along a set of trails which you know is much more relevant to to the application they were looking for but no it was it was a hell of a thing back in the day so the legend read was kind of leading that effort in terms just broadly speaking so you're a leader now what have you learned from reading about leadership I think there's a couple things one is you know go and try those really hard things that 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 read was that he would look at you know undergraduates or graduate students and empower them to be leaders to to you know have responsibility to do great things that I think another person might look at them and think oh that's just you know another graduate student what could they know and so I think that that you know kind of trust but verify I have confidence and what people can become I think is a really powerful thing so through that it's just like fast-forward through the history can you maybe talk through the technical evolution of autonomous vehicle systems from the first to 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 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 you know decimeter resolution models was really a big deal and that allowed us to to kind of bound the complexity of the driving problem the vehicle had and allowed it to operate at speed because we could assume things about the environment that it was going to encounter so that was a that was one of the that was the big step there for the urban challenge you know one of the big technological innovations there was the multi beam lidar and being able to generate a high resolution you know mid to long range 3d models the world and use that for you know for understanding the world around the vehicle and that was really a you know kind of a game-changing technology in parallel with that we saw a bunch of other technologies that have been kind of converging half their their day in the Sun so Bayesian estimation had been you know slam had been a big field in robotics you know you would go to a conference you know a couple years before that and every paper would effectively have slams somewhere in it and so seeing that you know that looks Bayesian estimation techniques you know play out on a very visible stage you know I thought that was that was pretty exciting to see and mostly slam was done based on lidar that time well yeah and in fact we weren't really doing slam per se you know it you know in real time because we had a model ahead of time we had a road map but we were doing localization and we were using you know the lidar or the cameras depending on you know who exactly was doing it to localize to a model of the world and I thought that was that was a big step from kind of naively trusting GPS I and s before that and and again like lots of work had been going on in this field certainly this was not doing anything particularly innovative in slam over 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 that was already maps constructed offline yes in general okay and did people do that individually individual teams do it individually so they had their own difference of different approaches there or they never really kind of share that information at least intuitively so so the DARPA gave all the teams a a model of the world they you know a map and then you know 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 you know - so many decimal places you don't really care about kind of the ellipsoid of the earth that's being used but when you want to get to ten centimeter or centimeter resolution you care whether the the core system is you know Nats 83 or wgs84 or you know these are different ways to describe both the the kind of non spherical nosov the earth but also kind of the actually in I think I can't remember which one the tectonic shifts that are happening and how to transform you know the the global datum as a function of that so you're getting a map and then actually matching it to reality two centimeter resolution that was kind of interesting and fun back then so how much work was the perception doing there so how how much were you relying on localization based on maps without using perception to register to the maps and how I guess the question is how advanced was perception at that point it's certainly behind where we are today right we're we're more than a decade since the graph or the urban challenge but the the core of it was there that we were tracking vehicles we had to do that at a hundred plus meter range because we had to merge with other traffic we were using you know Bayesian again Bayesian estimates for for state of these vehicles we had to deal with a bunch of the problems that you you think of today of predicting what that 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 I'd 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 upper operator and you know we did a lot of that in relative Najee relatively naive ways but it caused third still had to have some kind of Thanos yeah and so where does that ten years later where does that take us today from that artificial city construction to real cities to the urban environment yeah I think the the biggest thing is that the you know the the actors are truly unpredictable that most of the time you know the drivers on the road the other road users are out there behaving well but everyone's father or not the variety of other vehicles is you know you have all of the intended behavior in terms of perception or both that we have you know 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 you know the scale over which that you have to operate us now you know is much larger than you know the airbase that we were thinking about back then so what easy question what do you think is the hardest part about driving easy question yeah no I'm joking I I'm sure no nothing really jumps out at you as one thing but in in the jump from the urban challenge to the real world is there something that's a particularly for seus very serious difficult challenge I think the most fundamental difference is that were doing it for real and that in that environment it was both a limited complexity environment because certain actors weren't there because you know the roads were maintained there were barriers keeping people separate from from robots at the time and it only had to work for 60 miles which looking at it from you know 2006 it had to work for 60 miles yeah right 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 he said leiter 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 sense of suite in the near term so I think it's I think it's essential you know I believe it but I also believe is the cameras are essential and I believe the radars is essential I think that you you really need to use the composition of data from from these different sensors if you want the thing to to really be robust the question I want to ask let's see if we kind of tangle is what are your thoughts on the Elon Musk provocative statement that lidar is a crutch that is the kind of I guess growing pains and that's much of the perception tasks can be done with cameras so I think it is undeniable that people walk around without you know lasers in their forehead and they can get into vehicles and drive them and and so there's an existence proof that you can drive using you know passive fission no doubt can't argue with that in terms of sensors yeah so yes maybe sensors right so like there's there's an example that we all go do it have many of us everyday in terms of latter being a crutch sure but but you know in the same way that you know the combustion engine was a crutch on the path to an electric vehicle on the same way that you know any technology ultimately gets replaced by some superior technology in the future and really what with 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 you know this this you know what was I think the number I saw this morning 37,000 Americans killed last year on our roads and that's just not acceptable and so tech any technology that we can bring to bear that accelerates the this techno you know self-driving technology coming to market and saving lives is technology we should be using and it feels just arbitrary to say well you know I'm I'm not okay with using lasers because that's whatever but I am okay with using an 8 megapixel camera or 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 sort of automotive companies and you know if there's one word that comes up more often than anything is costs and and trying to drive cost down so while it's it's true that it's a tragic number the 37,000 the the question is what and I'm not the one asking these questions I hate this question but yeah we want to find the cheapest sensor suite that the creates a safe vehicle so in that uncomfortable trade-off do you foresee lidar coming down in cost in the future or do you see a day where level for autonomy is possible without lighter I see both of those but it's really a matter of time and I think really maybe the I would talk to the question you asked about you know the cheapest set certainly 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 sense suite that were and so it's great to tell a story about how you know how it'd be better to have a self-driving system with a $50 sensor instead of a you know a $500 dancer but if the $500 sensor makes it work and the $50 sensor doesn't work you know who cares the longest you can actually you have an economic offer you know there's an economic opportunity there and the economic opportunity is important because that's how you actually have a sustainable business and that's how you can actually see this come to scale and and and be out in the world and so when I look at lidar I see a technology that has no underlying fundamentally you know expense to it fundamental expense to it it's it's going to be more expensive than an imager because you know CMOS processes or you know fab processes are dramatically more scalable than mechanical processes but we still should be able to drive cost out substantially on that side and then I also do think that with the right business model you can absorb more you know certainly more cost on the Bill of Materials yeah if the sense of sweetie works extra values provided thereby you don't need to drive costs down to zero it's the basic economics you've talked about your intuition that level to autonomy is problematic because of the human factor of vigilance that command complacency over trust and so on just us being human yeah we trust the system we start doing even more so partaking in the secondary activities like smart phone and so on have your views evolved on this point in either direction can you can you speak to it so and I want to be really careful because sometimes this gets twist in a way that's that 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 we should be pushing on that level two systems are systems where the vehicle is controlling two axes so in breaking and braking and throttle / steering and I think there are variants of level two systems that are supporting the driver that absolutely like we should we should encourage to be out there where I think there's a real challenge is in the 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 you know I kind of I am actually incremental II more you know concerned around level three systems and you know how exactly a level two system is marketed and delivered and you know how people how much effort people have 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 you know a spate of people sleeping in their Tesla you know I watched an episode last night of Trevor Noah talking about this and you know him you know this is a smart guy who's has a lot of resources at his disposal describing a Tesla's a self-driving car and that why shouldn't people be sleeping in their Tesla there's like well because it's not a self-driving car and it is not intended to be and you know these people will almost certainly you know die at some point or hurt other people and so we need to really be thoughtful about how that technology is described and brought to market I also think that because of the economic issue you know Iike my economic challenges we were just talking about that that technology path will ugly 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 it and sleep and have the equivalent or better safety then you know a human driver behind the wheel because the again the economics are very different in those two worlds and so that leads to you know 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 cost safety critical safety concerns you believe that the it needs to diverge at this point in two different basically different routes and really that comes back to what are those l2 and l1 systems doing and and they are driver systems functions where the the 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 or dependence on that driver attention and attentiveness and at that point you you can kind of give up to some degree for economic reasons you can give up on safe false negatives and so and the way to think about this is for a for collision mitigation braking system if it half the times the driver missed a vehicle in front of it it hit the brakes and brought the vehicle to a stop that would be an incredible incredible advance and in safety on our roads right that would be equivalent to seatbelts 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 percent 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 you know really be available for for a self driven vehicle so clearly the communication and this probably applies though for vehicles as well the marketing and a 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 so but it's 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 2 vehicle where we don't over trust it I don't think so if people truly understood the risks and internalized it then then sure you could do that safely but that that's a world that doesn't exist that people are going to they're gonna you know if the facts are put in front of them they're gonna then combine that with their experience and you know let's say they're 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 like that's pretty compelling at that point you know just even if you know the statistics like well I don't know maybe there's something funny about those maybe they're you know driving in difficult places like 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 but now so 60 miles times 30 days so 60 180 a thousand eight hundred miles that's that's a drop in the bucket compared to the one you know what eighty-five million miles between fatalities and so they don't really have a true estimate based on their personal experience of the real risks but they're gonna trust it anyway because it's hard not to work for a month West what's gonna change so even if you start at perfect understanding of the system your own experience will make it drift and that's a big concern you know 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 say it's kind of the more technology savvy ownership group to you know 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 you know you're kind of immunization against this kind of false risk assessment might last longer but as folks who are who aren't as savvy about that you know read the material and they compare that to their personal experience I think there that you know it's it's going to it's gonna move more quickly so your work the program that you've created a 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 a 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 is philosophically interesting it's practically interesting so let's talk about safety how do you think we demonstrate you 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 soundbite 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 as part of that is like here's here's the way we did the work that means that we were very thorough so you know if you believe that we what we said about this is the way we did it then you can have some confidence that we were thorough in in in the engineering work we put into the system and then on top of that the you know to kind of demonstrate that we weren't just thorough we were actually good at what we did there'll be a kind of a collection of evidence in terms of demonstrating that the capabilities work the way we thought they did you know statistically and and to whatever degree we can we can demonstrate that both in some combination of simulations some combination of unit testing and decomposition testing and then some part of it will be on Road data and and I think the the way we will ultimately convey this to the public is they'll be clearly some conversation with the public about it but we'll you know kind of invoke the the kind of the trusted nodes and that will spend more time being able to go into more depth with folks like like nitsa 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 you know show enough work to them that they're convinced then you know I think we're in a in a pretty good place that means you work with people that are essentially experts at safety to try to discuss and show 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 disengagement 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 no Mohammed no in fact I 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 so I I do and I think that it won't be just one number so as we are internally grappling with us and at some point we'll be 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 the regulatory role and and then ultimately the public that we have confidence that our system will work better than that and so these these individual metrics will can tell a compelling story ultimately I do think at the end of the day what we care about in terms of safety is life saved and injuries reduced and then and then ultimately you know kind of casualty dollars that people aren't having to pay to get their car fixed and I do think that you can you know we in aviation they look at a kind of an event pyramid where you know a crash is at the top of that and that's the worst event obviously and then there's injuries and you know near-miss events and whatnot and and you know violation of operating procedures and and you kind of build a statistical model of the relevance of the low severity things to the high spirit of things I think that's something where we'll be able to look at as well because you know an event per 85 million miles that you know statistically a difficult thing even at the scale of the u.s. to to to kind of compare directly and that event the fatality that's connected to an autonomous vehicle is significantly at least currently magnified in the amount of attention and yet so that speaks to public perception I think the most popular topic about autonomous vehicles in the public is the trolley problem formulation right which has let's not get into that too much but is misguided but 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 thank you let them experience it alright I think it's I think 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 and but at the same time it's clear there's an opportunity to make the roads 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 are you know understand that it's safe and are able to use in their daily lives I think it's one of these things that will will just be obvious and I've seen this practically in you know demonstrations that I've you know given where I've had people come in and you know they're very skeptical they again in a vehicle you know my favorite one is taking somebody out on the freeway and we're on the 101 driving at 65 miles an hour and after ten minutes they they kind of turn and ask is that all it does and you're like yeah it's self-driving car not sure exactly which I thought it would be right but they you know they it becomes mundane which is which is exactly what you want a technology like this to be right we don't really when I turn the light switch on in here I don't think about the complexity of you know the those electrons you know being pushed down a wire from wherever it was and being generated it's not like it's just it's like I just get annoyed if it doesn't work right and and what I value is the fact that I can do other things in this space I can you know see my colleagues I can read stuff on a paper I can you know not be afraid of the dark and I think that's what we want this technology to be like is it's it's in the background and people get to have those those life experiences and and do so safely so putting this technology in the hands of people speaks to scale the deployment all right so what do you think the the dreaded question about the future because nobody can predict the future yeah but just maybe speak poetically about when do you think we'll see a large-scale deployment of autonomous vehicles ten thousand those kinds of numbers you will see that within ten years I'm pretty confident we what's an impressive scale what moment so you've done DARPA Challenger there's one vehicle at which moment does it become wow this is serious scale so so I think the moment it gets serious is when we really do have driverless vehicle operating on public roads and that we can do that kind of continuously without a safety dry without a safety driver in the vehicle I think at that moment we've we've kind of crossed the zero to one throw shoulde 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 you know a useful product out in the world and I think that is really at that point it moves from a you know what is this kind of mixed science engineering project into engineering and commercialization and really starting to deliver on the value that we all see here and you know 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 roads in the urban environment I think it's going to be urban suburban type environments you know with a roar when we we thought about how to tackle this I is kind of enfoque to think about trucking as opposed to urban driving and and you know the 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 you know lanes are wider etc and I think that that intuition is pretty good except we don't really care about most of the time we we care about all of the time and when you're driving on a freeway with a truck say 70 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 and that those those challenges that you see occur more rarely so you don't get to learn as all as quickly and there you know 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 they're you know if if two vehicles crash at 25 miles per hour it's not good but probably everybody walks away those those events where there's the possibility for that occurring happened 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 they 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 were able to learn it's more safely more quickly in the urban environment so ten years and then scale twenty thirty year I mean who knows if if it's sufficiently compelling experience is created it can be faster and slower do you think there could be breakthroughs and what kind of break throughs might there be that completely changed that timeline again not only am I asked 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 as quickly as possible don't say infrastructure please don't say infrastruc no it's definitely not infrastructure it's really that car that perception forecasting capability so if if tomorrow you could give me a perfect model of what's happened what is happening and what will happen for the next five seconds around a vehicle on the roadway that would accelerate things pretty dramatically how you in terms of staying up at night are you mostly bothered by cars pedestrians or cyclists so I I worry most about the vulnerable road users about the combination of cyclists and cars right just because I khlyst and pedestrians because you know they're not in armor you know with the cars they're bigger they've got protection for the people and so the ultimate risk is is lower there whereas a pedestrian or cyclist they're out in the road you know they they don't have any protection and so you know 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 yeah does that worry you of how from a technical perspective how we solve that because as humans the way we solve that it's 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 I think there's 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 right like a even if I'm annoyed I'm not going to just drive through a person stood on the road right and so I think today people can take advantage of this and you 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 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 101 and you know 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 Yeah right it's kind of just move forward slowly and if somebody's really close and stop and and I think that that probably can be replicated pretty pretty easily and particularly given that it's you don't do this at 30 miles an hour you do it at one that even in those situations the risk is relatively minimal but I you know it's not something we're thinking about in any serious way and probably the that's less an algorithm problem or creating a human experience so they see AI people that create a visual display that you're pleasantly as a pedestrian nudged out of the way yes that's a that's a yeah that's an experienced problem not an algorithm problem who's the main competitor to Arora today and how do you out-compete them in the long run so we really focus a lot of what we're doing here I think that you know I've said this a few times that this is a huge difficult problem and it's great that a bunch of companies are tackling it because I think it's so important for society that somebody gets there so we you know we're we don't spend a whole lot of time like thinking tactically about who's out there and and how do we beat that that that person individually 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 coldest acts are to some degree and you know 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 were able to bring in early on because the the the great investors we have you know 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 and 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 oh thank you very much really enjoyed it you