The Future of Transportation – Panel Discussion @ AI Commercialization Conference 2019

The Future of Transportation - Panel Discussion @ AI Commercialization Conference 2019

So our panel discussion that we're going to
bridge into now is called The Future of Transportation. I would like each of you to introduce yourself
probably within two minutes, and also your ideas about the future of transportation. I will start from Louay. Certainly. I'm Louay Eldada,
the CEO and co-founder of Quanergy. We are a Silicon Valley company
based here in Sunnyvale. We are known for making solid state LiDAR,
but we do a lot more than that. Our solid-state LiDAR that goes in the vehicle
and we also make LiDARs that support smart infrastructure and smart cities for smart transport. We also do the software that relates to autonomous
vehicles, so we work with people throughout the ecosystem for autonomous vehicles. We get exposed to the landscape
from different perspective. We operate globally, we sell globally, and
we have 12 offices globally. So, happy to share our thoughts today. Thank you. How's your opinion for the future of transportation? Can you mention and share some of
these insights a little bit? Well, I mean, the autonomous vehicles are going to
happen soon, regardless of different opinions. Now, the question is about whether you have
to use this or that sensor or this or that approach when you build your software stack. That's just the process, right? The outcome, there's no doubt about it. We will have fully autonomous vehicles. Today, fully autonomous level five vehicles
are available in geo fenced areas. The challenge is when you are on public roads,
in a chaotic environment, what hardware and software really do the job and avoid unnecessary
accidents and actually eliminates accidents that would have otherwise happened. So, the timing for fully autonomous vehicles
is around 2025. But level two, three, four, with level five
being full autonomy, are happening starting in two years. Level three, four, level two already exists. Okay. Thank you. Nalin? Yeah, thanks. My name is Nalin. I'm Co-founder of Auro Robotics, which after
acquisition by another company called Ridecell, we have essentially become its autonomous
driving division within Ridecell. And we work on autonomous vehicles, more specifically,
we work on developing the software backbone of autonomous driving technology. I will keep it short for the questions, but
speaking about the future, I couldn't be more excited about it. Not just in terms of autonomous, but also
all the things which are happening in shared mobility and electric mobility. I think we are at in a very fantastic time
where all these innovations are coming together. So, I'm really very excited about the whole industry. I won't put a specific time on when we will
see autonomous vehicles because there are so many arguments and opinions about it. But yeah, in some cases, we will see autonomous
vehicles much sooner than some other cases. So you guys mostly work on the geo fenced,
ride-share services. Is that how you define it? That's how we actually started back in 2015. When we started this company, we were focusing
on developing autonomous vehicles for campus like environments like a university campus
or a retirement community. But since then, as our technology has become
more and more matured, we have moved away from those campus like vehicles
to more public road vehicles. And now we are focusing more on large fleets,
how we can enable low hanging fruits like empty car logistics across these
massive fleets on public roads. Okay, thank you. I'm Ruslan Belkin. I'm CTO of Nauto, and it actually
stands for Network Auto. You can go on the website, What we do is make driving safer for commercial
fleets by monitoring inside and outside of the vehicle. We are in more than 250 fleets right now across
Europe, Japan and the United States. And I think my opinion on the future – actually
the future’s very hard to predict, but we are 90% there as far as autonomous vehicles
and we have 90% to go. In terms of use specifically, I think it's
a great career move because it's a secular trend. Whenever you're getting into anything having
to do with autonomous vehicles, you'll benefit by one way or the other. In terms of investors and making money, I
think it's not going to be as straight forward. And I think people who are, you have to pick
the right vertical markets. If you maybe consider things like working
on tools, working on things that may enable autonomous vehicle development, in my opinion,
that thing is a more fruitful pass to monetization or something what we do, which is a fairly
direct pass to monetization, rather than barging in straight forward and trying
to do on autonomous car, although some people will obviously succeed in that as well. I'm Tao Wang and I have given
a presentation just now. But I'll talk about myself a little more. I graduated from Stanford, having
a master's in CS degree. And I actually went into the PhD program,
but didn't finish because my colleague and I wanted to do a startup. The startup is called, and we are
doing level four self-driving. So, I left a couple months ago, and
I'm exploring new things to do right now. So, in terms of the future of self-driving,
I actually agree with the guests that the self-driving will eventually become reality,
but I think it's going to take a while. Right now, AI is not sufficient to support
level four, level five, ubiquitous self-driving everywhere. You might see some of the niche market deploying
self-driving cars in the next few years, but large-scale deployments are just not there yet. I think we as an industry might need to take
some of the more contrary methods to achieve self-driving, both in terms of technology
and go-to-market strategy. Okay, thank you. Thank you very much. We talked about autonomous driving
in the future transportation. And one of the questions is how important
is urban infrastructure? What is the role of the infrastructure? It might be easier for many Asian countries,
but probably not for the U.S. And how do you see the combination of autonomous
driving cars and the future infrastructure? Yeah, happy to talk about this. We actually are deploying Smart City infrastructure
globally, we see it as a prerequisite to having fully autonomous vehicles,
you need vehicle connectivity. And when you say that, that implies having
an IoT infrastructure with 5G connectivity that will allow smart transport vehicles to
communicate to each other, vehicles we can see around the corner. You can also control traffic flow when you
have visibility for what's happening on the road. So, deploying the hardware and software that
support smart cities is something that's necessary before we can see autonomous vehicles on the road. I just came back from China literally
a couple of hours ago. Just to show you the role that legislation
and regulation can play. There are self-driving lanes on the roads
in China already. Smart City infrastructure is being built right
now in China and 5G is being rolled out. So regardless of how highly we think about
ourselves in Silicon Valley, I see China as leading the way here. Well, what he just said totally makes a lot
of point that all the smart infrastructure and V2X communication is definitely
going to help. But, there are also a lot of other things
that we can do in terms of infrastructure. For example, one of the reasons of doing this
self-driving car is to give them the freedom of mobility back to people with disabilities
and elderly people. And in that sense, I think there's a lot of
work that has to be done in making the infrastructure more accessible for people with disabilities. Even very basic things like the pavement linings,
for example, if you have to develop an AV which can be deployed across multiple geographies,
you have to keep some standardization in how the lane markings are done. For example, there are four inches, six inches,
different categorizations, and if we have a minimum reflectivity and minimum width,
then it adds a lot of value. Also, like there are some companies who are
working in putting QR codes and traffic signs and having special materials for lane markings,
which can be read not only by humans, but also by smart cameras in otherwise very challenging
conditions like rain or fog, or a very glaring situation. So, these type of things would also need to
happen to make these autonomous vehicles which can work very nicely with human driven vehicles. It is difficult to disagree with
what my colleagues over here. It's going to happen outside the United States. In the United States, we don't invest in infrastructure,
and therefore nothing's going to happen in terms of smart cities here. And it could be an opportunity, because the
reason you see so much advance coming out of Silicon Valley, including self-driving,
is because our infrastructure is so bad. So, there is a silver lining here. But if I had to say where the opportunity
is here, it is probably in parking. If you think of self-driving vehicles will
be able to park, not even fully autonomous vehicles And parking space being another premium being
able to stack the cars in the parking lot without human involvement, and making infrastructure
within the parking structures for navigation, or around the parking structures from a restaurant too. I think that's where the first infrastructure
opportunity is going to happen because it will enable private investment. Yeah, I think I'm going to take a slightly
different view. I think infrastructure is important,
and it can really help self-driving. But on the other side, infrastructure
is expensive, right? There's a reason why we sit here and talk
about infrastructure, and nothing's happening out there. It's because nobody wants to put the money in. And I think we really need to think about
which part of the infrastructure gives us the biggest marginal improvements on self-driving. For example, if you try to pave the road a
little better, does that actually help? Or I think right now, self-driving faces issues,
not just on the roads, but more on the other participants on the road. And can you actually make the infrastructure
such that other agents on the road are much more predictable than they are right now. Let's say if we can rule out jaywalkers in
cities, with some infrastructure change, I don't know how, then the whole point of pedestrian
detection becomes irrelevant, because nobody is going to just walk in front of the car,
because it's just physically impossible for you to jaywalk. And you only cross on bridges or designated
areas where there's crosswalk, which can be mapped, and the car knows about it in advance. Another example is like, can you actually
make the V2X on the traffic lights? So, I think traffic light detection is —
many of us think it's solved. But if you really want to solve to 100%,
it's not solved today. And a platform camera detection, can you have
another layer of redundancy that gives you that redundant signal such that you can at
least compare your prediction with what the wireless is telling you. So, I think we really need to think about
what's the highest leverage in terms of changing the infrastructure. So Tao, I have a follow up question to you. I'm curious, when you do this kind of learning
process, how do you see the infrastructure change into the whole process? How do we get those updated
information of the infrastructure? Sorry, can you say that again? How do you include infrastructure change
into the learning process? We don't really need necessarily to
approach this in a learning way. For example, the traffic light, if your system
is modular enough, you should be able to just plug in another signal into your system, and
say, I compare my traffic light recognition with this signal. And if it's different then something's wrong. Okay. The next question is about the famous person
Elon Musk once said, “Anyone relying LiDAR is doomed”. That's the famous statement. However, Aurora just announced the acquisition
of Blackmore for its LiDAR technology. So, what is your comment on those smart sensors? Do you think LiDAR is needed? I will start from Louay. Yeah, I mean it's a comment that
almost does not deserve a reply. Because what is the logic, or what are the
physics that are being really presented as a reason for that statement? None. Zero. Because there are none, right? So, Elon Musk is completely off; he was
completely wrong with that statement. He made the statement three years ago, and
I supported him when he said, LiDAR is the most capable sensor best in overkill. So by saying an overkill, you're saying
it's more capable than other sensors, which we agree with But it's too expensive. Three years ago, that was the case,
it was too expensive, when you had to pay tens of thousands of dollars for LiDAR,
it doesn't make sense. When the LiDAR price is similar to the price
of the car, obviously, it does not make sense. Today, for instance, we make solid state LiDAR
at quantity based on silicon CMOS, and the price is between several hundred dollars to
a few hundred dollars, depending on volume. So, the fundamental premise of his argument,
which is too expensive for the job, is completely wrong today. So, he needs to stay up to date with the development
and really listen to his own engineers. Maybe I'll continue. So, I think it depends on in
what position you look at it. If you look at it from Elon’s point of view,
I think there's merit to what he's saying. If you're doing a production car, that is
mainly not fully autonomous. You have a driver there and
the car has to look good. LiDAR is ugly, right? And especially if you have several of them,
it's going to add the cost and you are willing to live with the safety margin that
perhaps has no full proof. That's the business trade off. You can say, I'm going to try to do it without LiDAR. Obviously, I'll be disadvantaged at night,
and maybe some other sensors will appear in the pictures. I'm personally excited about shortwave infrared
sensors that are coming. But if you're doing a robot taxi, do you really
want a bus, or for example a truck, where actually look doesn't matter? Cost doesn't matter as much on balance of
the vehicle and it may not be a safety driver or safety requirement is simply higher. You have a school bus. Would you want to have a school bus
without all sensors possible? Probably not. So, it depends on how much risk
you are willing to take. I think Elon is willing to take more risk than
other people and you can see his point of view here. So that's kind of a more nuanced answer, if you will. I encourage people to take different approaches,
and ultimately will come down to if statistically you can prove that your self-driving car is
100% or 200% safer than the human beings. The main arguments that are here against LiDAR
are the cost and the moving parts in some cases. And the industry, to be honest, is making
immense progress in both these fronts. The third type of argument, which I sometimes
hear is that LiDARs are very poor in detecting or interpreting the behavior of a pedestrian
who is standing on a curb side in comparison to a camera. Using a camera, you can detect whether he
is just going to fidget on his mobile phone, or is going to cross over. And in that response, no one is saying that
LiDAR is the only sensor which is going to be on the car, it's going to be supplement
sensor in addition to the cameras, so no one is going to remove cameras out of the picture. And I can easily give hundreds of examples
where cameras are going to fail, like a sun which is glaring right on to the camera lens
or a heavy rain situation. These are some scenarios where camera is definitely
going to fail and that's the reason why even Tesla uses radar. But again, radar also fails, like radars have
very poor cross detection capabilities, and they have a very limited vertical field of
view, which was the reason of that infamous Tesla accident. So definitely, we need more sensors, besides
just camera, eventually Tesla is collecting a mass amount of data, and there are computer
vision algorithms and deep learning and they are making immense progress in that. So maybe they might be able to break that
barrier and maybe have algorithms to a stage where they can do everything using algorithms. But we are not there yet and we don't know
when we will be there. So, if we have to deploy safe and reliable
autonomous vehicles now or then in the next couple of years, I think we need more sensors
than just camera and radars. Yeah, I think I'm going to probably make a
strong argument here that anyone who only uses cameras will not be able to
remove that driver eventually. So, this is my own take. And the reason I think if you have seen my presentation,
you probably have seen how deep-learning based computer vision can fail in very funny ways. And with a reasonable patch of thing on yourself,
you can actually appear invisible to a pedestrian detection algorithm. And this kind of thing scares me a little bit. If I think about a car without a driver, trying
to detect all human beings around using this kind of technology, not that I'm against it,
I think it's a very cool and great technology, and it gets us to the 99%. But 99% is just not enough for
level four self-driving cars. The reason I think Tesla and other OEMs are
able to use camera-only systems is because right now the liability is still on the driver. When anything happens in these systems,
even if it's in autopilot, the driver is still responsible for anything that happens. I think eventually we need some physical guarantee
on detection of human or whatever object that is. And I think nowadays, LiDAR just gives us
this guarantee on the 3D position detection, and maybe one day some other
sensors will get there. I know some radar companies are working towards
higher spatial resolution, maybe one day they will be as good as LiDAR. So, I can’t say LiDAR will definitely be
the solution, but right now, I don't see another sensor that can beat LiDAR in its own merits. If I may add, a LiDAR is the only 3D sensor,
with the camera being 2D and with the radar being 1D. Then you add the fact that with the laser
based system that where you have a minimally divergent laser beam, you maintain resolution,
you maintain accuracy, you see in the dark. Look at one scenario, for instance, a car
breaks down under a bridge at night. The radar cannot tell the car from the bridge,
the car sees absolutely nothing in the dark. There you go. So, what do you do? Is that an acceptable situation? Do you want to have your kid in the car that's
driving without a driver, and it runs into that situation, and you agree to do that,
because statistically there's an advantage? Vehicles that are autonomous would have to
be a lot more responsible than vehicles that have a driver. We cannot just say the fatality rate will drop. That's not acceptable. Because when software engineers that have
all the time in the world to think about how do I design this algorithm, decide here is
who I'm going kill when I have the situation, you have a big, big moral issue. Whereas when a driver does their best under
the situation, and maybe still ends up hitting someone, that's more common and acceptable. So, the future has to be much, much safer,
not just safer than the present state of things. Okay. Thank you. For many L2 or L3 applications, like more
than recognizing driver behavior based on driving data is critical to improve fleet safety. And one of the questions is how can we actually
convince drivers to trust the system? For instance, I have some features in my car that
provides me some aid, but I'm a little bit hesitant. Well, I can give you an example. One of my colleagues, he was almost feeling
drowsy, and such an advanced system started beeping when he started falling asleep. And that feature literally saved his life. So, for these people, it will be very difficult
to argue why they won't trust such a system. We actually encountered this problem frequently
when we introduce our products in the commercial fleets. So initially drivers don't trust it, or actually
they don't want to be watched, they don't want to be monitored in any way and only when
something happens close to a collision when people say, Okay, well yeah,
I actually didn't feel good. I was drowsy and it basically saved me. So, it's going to simply naturally take time. And you're right, understanding driver behavior
and understanding of the other actors’ behavior is going to become increasingly more and more
important than if you see there's lots of research going on around drivers’ intent,
actors’ intent and missing all these components are going to be very important to understand
what people are going to do around you, especially as studying a mix of autonomous vehicles and
non-autonomous vehicles. I remember you guys just released a prevent
feature, can you explain a little bit about that? It was released earlier actually a while back. So, what it does is, it actually monitors
the driver inside the cabin, including where is the driver looking directionally, is driver
drowsy, is driver on the cell phone and actually also looks outside, am I tailgating somebody,
am I merging on the freeway, am I in a situation that requires – there might be other various
things, not just visual, but also basically measure reaction time, other things, is that
tolerance enough for the driver’s current response conditions to react to the situation
and we alert in different ways. Yeah, and that actually does reduce – and
we have insurance proof that it does reduce at fault collisions. That's how we make money as a matter of fact. Okay. Then another question is, as safety is a primary
focus of all future transportations, how to promote safety without
sacrificing innovative opportunities? And, what is your perspective on this? One side, you want to try something new, but
you also need to consider about the safety issues. On, one side would be long. On the other side? Look, I think one of you said it that while
the liability is on the driver today, I think you can get away with a lot of things. As liability shifts towards the company that
owns the car fleet, or the vehicle manufacturers, you're going to get into Boeing 737 situation. So it's going to be a lot more
stringent certification requirements. So you have to work this balance,
and that shift will start occurring. Do you want to be on the right side? And I think the opportunity is not only in
improving software, but also in building software, for example, for certification. There're a lot of hidden opportunities here,
and the things will present themselves as this transition starts happening. I think the rollout of autonomous vehicles
will be gradual not only in terms of levels two, three, four, five, but also of how you
actually roll out fully autonomous vehicles. Even the most innovative companies, they don't
say we have the most innovative hardware software solution, and we can take the driver out. What's being done today, and I've seen those,
you have really control rooms where for instance, one maybe not too expensive person, instead
of a driver, sits in front of multiple screens with three being the average. And they take over when there's a disengagement. We are very far from getting to a disengagement
rate of zero, where the car never says, I have no idea what to do here. That's going to take a very, very long time,
possibly decades. So, till then you can have a single person
that is looking at multiple screens, and can actually take over and make the decision for
the car when you still run into a scenario where a human can still make a better decision. And this is actually one of the many reasons
why self-driving cars will not mean that there will be lots of people out of work. There are so many jobs related to rolling
out self-driving car, not only on the innovation side, creating the hardware and software,
but also the whole ecosystem of supporting the rollout in a very responsible way. I agree with them. And frankly, this is not the first time
I think we are seeing this tug of war between innovation and safety. Like one gentleman over there, he mentioned
in one of the previous Q&As about airline industry, right? It's the same case. There was a lot of discussion between how
you innovate, and at the same time, how do you balance out the safety in that aspect. The example which Louay gave about having
a closed loop system, where even though you have a safety driver, but you have another
person, whether it's a tally, or remote operator or a second passenger, and they're sitting
on the passenger seat. That's really crucial. And if that would have been the case, the
infamous Uber incident would have been avoided. A lot of those things can be done. GM super cruise is a very good example where
they've rolled out an autopilot system. But unlike Tesla, they are doing it in a much
more responsible way where they have an IR camera, which is tracking the state of the driver. So in that case you cannot fool the car by
just putting your hand on the steering wheel, but really looking out the window. So those are the things where you can do it, where
you balance out the safety and the innovation of it. I believe you guys are working with lots of
OEM companies to provide this kind of ride service. What will be your way to manage risk? Yeah, so we work with a lot of
insurance companies to figure out. In fact, the largest insurance company Munich,
we were the first company to kick start the autonomous vehicle insurance program. And we work closely with them to figure out
how statistically we can measure the risk of introducing these autonomy
features in the cars. I think the whole industry needs to answer
one question, that is, is the current testing procedure safe? So, all the self-driving car companies right
now, how are we testing the self-driving cars? We are just putting our technology onto the
road and test the system to its limit until it cannot handle any more,
then the human driver takes over. We call that one disengagement. And every year, all the companies reports
these disengagement numbers to DMV. And I think this is not a very good way of
testing because it's essentially using all the road users as lab rats for the technology
that's not mature enough. So, I think this is analogous to car manufacturers
rolling out their car without any testing onto the public road, and until they somehow
kill someone, and then they try to fix this problem in their car, that's just not acceptable. So, I think the direction the industry should
push to is more standardized tests, but in more closed or private environments, such
that you can test a bunch of different scenarios and not jeopardize the complexity of
these scenarios. But at the same time, you don't cause like
unsafe conditions on public roads. This is something that everyone needs to think about. And then I want to add something to it because
it's close to what we are trying to do as well. Indeed, this engagement rate is, like Facebook,
or that type of approach to testing. There's nothing wrong with Facebook. But if you think of car companies, automotive
vendors, there are simulators with hardware in the loop that you could test what they
use today for testing of the cars, the simulation software is just not very good. And the second problem is, there’s not enough
realistic data for the simulation software, that looks just not fully working. So, I think there's an opportunity to acquire
a lot of data from driving environments without taking over control to feed into simulation
software, and to make certification actually much more realistic by using
actually happened scenarios. And that will come. If you look at the regulation, where does
it normally originate? It usually originates in Europe. And if you can see the European agencies are
working with this type of regulation, you will absolutely have certification for AV
software and once it’s in Europe, it will come here. So, Tao in your previous presentation, you mentioned the rest of the 10% of work might take forever. Then, how do you actually solve the unlimited
number of the corner cases. What will be the solution? I don't have a solution right now, it’s
really a multi trillion dollar question. And if you have the answer, come to me and
we can work out something together. But I think at a high level, what I'm envisioning
is that maybe there's a way to capture all those scenarios with one unified method
without seeing the examples. So, I think one example would be, if you have
someone standing right in front of you, then your LiDAR should pick up that's
something out there. I don't know if it's a person or a tree or
a garbage can, but I don't care. My vehicle doesn't want to hit that thing. So that kind of guarantee based on physics
and mathematics, I think that's going to be very important. Yeah, it's a very difficult problem to solve. And for me personally, that's the whole excitement
about working in this field, that it's still a very difficult problem. And every single day, I get to work on these
very challenging problems. Though there might be, as Tao mentioned very
briefly in the very beginning, there might be very niche applications, where the number
of search corner cases might be very less compared to the L4 or L5 autonomy. And in those cases, I think it might be fairly
low risk to deploy autonomous vehicles in the beginning. One of the examples is, of course, the way
I started my own company, which is deploying in these control campus-like environments,
where the speed of the vehicle is very low, the pedestrian traffic is not that much, the
speed of the other vehicles is not more than 10 or 15 miles per hour. So that's one. The other type of these low hanging fruits
application is, empty car logistics, where let's say a rental car is being delivered
to your doorstep autonomously or the repositioning car from a cold demand area to a hot demand
area autonomously with no passengers sitting inside it, or just for the delivery applications
like the neuro type of robots where these are really small delivery robots. So these type of applications are low hanging
fruits, where the number of corner cases or the risk might be potentially less. The last question before I open the floor. Since L4 or L5 autonomous driving is so hard,
so people choose a maybe more realistic way to monetize and commercialize this technology,
such as you have a restricted scenario and environment, geo fences,
driving truck and maybe logistics. What is your opinion of the possible commercialization? Maybe I'll start then. Since we actually saw that in the beginning
of the company, and I think they made the right call and decided, okay, we have to find
a way to make money while this thing is going on here. So that's one task we do. I think there're other paths. If I'd say even more successful path here
was labeling companies. If you think about who made money in all this
gold rush, it's been tool makers, right? And labeling companies caught the wind of
if you do it right, for all the labelings for autonomous use cases, that's another pass. I think certification, simulation, you don't
see quite this number of simulation startups, but it kind of didn't get to a full tooling chain. But I think that there's a good opportunity
in those type of tools. I think that'd be another pass to commercialization. I don't know what you guys think. Yeah, I totally agree that when there's a
gold rush, you just sell the shovels, and you can make a ton. But I also think this business model is only
sustainable if there is actually gold there. So, after a while, if nobody gets gold, then
you can’t sell the shovels anymore. Also, I think in the entire value chain, it's
important to position yourself, your company as the part that captures most of the value. And I think, if there's not too much differentiation
in the thing that you're doing, then you will be in a pretty bad spot. Because there will be like 20 other
competitors doing the same thing. And at that point, you're just
competing on price, right? So, what's the core technology or access to
market or access to customers, whatever it is, what's the core competitiveness of your
company, that's also something to be considered. Commercialization of some capabilities is
happening already, right? Autonomous valet parking, traffic jam assist,
and autopilot on highways for long rides. And those also happens to be the situations
where you get the most accidents. When you have a traffic jam, that's when people
are texting that they're running late or something. When you're on a long, boring road, that is
when you might fall asleep, or actually be tempted to actually do some work and so on. Many of the scenarios that caused lots of
fatalities and accidents are actually the easiest one to address soon and to address
them in a very responsible way. So, commercialization is already happening. You might call all that basically partial level five. I think full level five in any environment
and Mumbai will happen beyond 2050. Just to make sure that you all realize that
I'm not saying that this is going to happen soon but because the day people in – Sorry,
I'm picking on Mumbai now – the day people in Mumbai stay in their lane, do not run red lights. You look right and left while wait? And pedestrians actually only across when
it says “walk” in pedestrian crossings. I don't have any visibility for that ever
happening in my lifetime. So you have to think globally, because you're
going to sell the same car globally and so on. However, with valet parking, the traffic jam
assist and so on, all of that can be addressed with given the environment and given the limited
capability needed that can be commercialized today globally. I agree with him over here. Such applications like the valet parking or
traffic jam assist or highway driving, they can be commercialized today, or maybe in the
near future compared to L4 and L5. Also, as I was mentioning earlier, the empty
car logistics or the delivery applications. Again, some applications which can be enabled today. As far as the question of making money is
concerned, I think the autonomous vehicle industry is going to disrupt so many other
industries that making money is going to be the least concern of anyone. Like we can always come up with advertisement
based revenue models, content based revenue models or just making a moving office area
just within a vehicle. It's like making money out of this technology
is not going to be an issue. I think the whole industry is right now trying
to figure out how to make it safer than the human driver. Thank you very much for your insights.

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