(wondrous electronic music)
- Welcome to CES 2021.
My name is Aaron Jefferson, VP of Product.
And I'm here today to talk to you about needs for speed,
enabling highway autonomy.
And I want to key in on that word speed.
Many of the systems today are limited
in terms of the ODD or the speed capability of the system,
whether it's Level 2 or Level 2+.
Those systems have the limited say,
sensing and perception technology
to enable you to take your hands off the wheel,
take your eyes off the road,
and really be able to drive autonomously.
First, let me give you a little background about Luminar,
some of our company highlights, that is.
So we came up with this really great
breakthrough technology for Lidar,
really enabling us to see long distance
high resolution sensing technology
to enable highway autonomy.
Our belief is that and our vision
is to make highway autonomy safe and ubiquitous,
safety being a key goal there,
and ubiquitous meaning available for all.
So if we look at overall,
our integrated autonomous and safety solution,
we are the world's first autonomous solution
for series production.
There are a lot of different activities for development
and spinning systems
that aren't really designed
to go into a vehicle for production.
From day one, our concept was development activity
leading to a production platform,
which includes all the aspects
of what it takes to qualify for automotive grade.
We're not just built on sensor technology.
We're also built on our software.
What our sensor does is really unlock,
I would say, a clarity of the environment
and the environmental space
more than any other sensor this out there.
But with that clarity,
you can take that information
and really develop software
and unlock features and capabilities
that have never been on the market before.
And the key thing is
you don't want to just provide the function or the feature,
but you also want to do it in a robust way
with great confidence.
And that's what our sensor delivers.
And then we have our top tier,
which is essentially the software stack
that enables the functionality.
Path, planning, and vehicle control.
We have the capability to develop that.
We're working with several partners to develop that.
And we work in conjunction with our tier one.
So it really just depends on the model
being used by our end customer,
but essentially, we are being built,
and we are partnering,
and we're developing a solution
that goes from the sensing
all the way to the full stack development
and releasing the function at a vehicle level.
Now, if you take a look at the market,
we like to look at it in two halves, I would say.
One is ADAS.
ADAS includes everything from Level 0, basic AEB,
all the way up to say,
the systems you see really on the market today,
Level 2, Level 2+ systems
where the driver still has to remain engaged,
maybe can take their hands off,
but their eyes always have to remain on the vehicle,
I mean, on the road.
What we've noticed in this space,
and if you look at the numbers on the bottom left there,
it hasn't really been effective
at reducing the number of fatalities.
Still over a million fatalities globally,
still tons, I think 50 million accidents.
And if you look at the United States in particular,
35,000 deaths on the highway every year.
And that's a real problem.
And that number has remained stagnant for a while.
Our belief is that with Lidar,
you can greatly improve the effectiveness of these systems
and really start to hit those numbers and knock those down.
The other half of the market is autonomous driving.
And autonomous really means driver out of the loop.
Anything under that is automated or ADAS.
For autonomous driving,
we are really looking at unlocking the capability
of allowing the driver to disengage
and do that in a safe manner,
meaning the vehicle knows what to do.
It can detect objects or threats well ahead of its path.
It can understand the maneuverability
and what it can and can't do.
And it has a redundancy in the safety behind it
to really deliver the capability.
So when we look at those two halves,
we say, okay, there's a really great opportunity
to greatly improve active safety.
And we call that proactive safety.
And then there's a,
I mean, we are the ones,
we believe, to unlock the autonomous space.
And we're really focused on Level 3 for a highway,
again, because once you get to the city
and you're talking Level 4 or 5,
and no steering wheels, and pedals, and brakes,
you get into a situation
where you have an extremely difficult ODD.
Over time, we believe that will be solved,
but the immediate benefit to an end consumer,
to someone buying a private vehicle is the highway autonomy.
So next, I would like to talk to you
about our industry leading performance.
We've talked a lot about unlocking autonomy
and it's important that we understand
in terms of where Luminar is
in relation to the rest of the market.
If you look at the graph to the right,
this will really explain how we founded
and designed our architecture from the beginning
we knew that the problem to solve
was high resolution at a long distance.
And we couldn't allow for that trade off.
If you look at the rest of the market,
and for a lot of reasons,
architecture selection, a solid state selection,
other things of that nature,
everybody has to make a trade off.
And what you don't see here is also a field of view.
There are some companies that can see really far,
say 150 meters.
However, their resolution is extremely low.
What that means is
if there are two objects out there 150 meters away,
they're gonna detect one
or they're going to highlight in their specifications
that they have a range detection of 150 meters.
But without the resolution,
it really doesn't make much of a difference.
Same way the other way.
If you were to say, hey,
I have the resolution, the high resolution.
I can detect two small objects separately from one another.
That's great.
But if you can only do that at 50 meters,
well, then I can do that with the radar.
So, do I need a Lidar in my system?
So, it's really important
that we provided the high resolution,
and the range, and also the field of view.
This just digs into that a little bit further.
I won't go into too much detail,
but essentially, if you look at the competition
and you look at what's required
to deliver into the market, right,
all these different factors play a part.
And if you're missing any of these,
it really does hinder your chances
at delivering the performance
and the say, automotive grade product
that the market needs.
And so this is just, again,
a deeper dive into that look.
Some of this, if you look at the competitors' side,
some of it's because of the selected 905 wavelength.
Some of it is because of the selected architecture.
And that really goes into why we selected what we did.
We built it from the standpoint
of what problem are we solving
and let's not make compromises around certain capability
that we know is gonna unlock the capability in the market.
Speaking of those architectures,
if you look at our system,
we really built things from the ground up
as was mentioned before.
If you look at our structure,
it is the scanning, the (indistinct), and the laser.
It's a very simple architecture,
again, that allows us to see
all the information that we need in a scene,
versus what we typically compare to
or have been compared to in the past
was a spinning Lidar,
which requires several lasers, several receivers.
And yeah, there's a ton of data there,
but there's a ton of cost associated with that.
And while that has developed some,
it still isn't an architecture
that will get you an affordable solution,
a robust solution in the market
that you can easily create an auto grade product
and then sell into the market.
It's one of the main reasons
you really don't see any spinning Lidar
in any production vehicles.
And it's also where we understood there were limitations
and we wanted to make sure
that we set up our architecture to solve the problem,
but also think about scalability,
think about cost,
think about manufacturability and production.
And we've done a really good job with that.
We really love this slide.
This is one of our favorite slides from my product group.
It is requirements that matter.
And essentially, what we want to explain to everybody
is everything on this page matters.
If you drop one ball here,
you essentially limit the chances
of your product being effective
and going into production.
The top row is really around performance.
You really want to bring that Level 3,
Level 4, hands off, eyes off value to the consumer.
You need that full top row.
Range, resolution, fidelity,
and the precision of the information,
the right field of view, the weather performance.
We need all of those pieces to work well.
What we're finding now is the bottom row,
when you really start
to get into discussions with customers,
you really start to explain to them what you have
and what you can deliver,
they also have a concern from the bottom row.
You know, how viable is your supply chain?
What's your manufacturing strategy?
Do you understand how to manufacture this
to where you can get yields that makes sense
and that will allow everybody
to feel safe about their product being developed?
Is it quality product?
Is it automotive grade?
You know, there are a lot of companies
that really focus on the performance
and a lot of companies fall short there,
and they're not even yet at the position
where they can think about the bottom row.
When you talk to the customers,
the premium OEMs, the volume OEMs,
every OEM that has
understands what it takes
to put a quality product into the market,
especially the automotive market.
That bottom row is just as important.
We were awarded a Volvo win.
And I want to be clear.
This Volvo win is a production award for SLP 2022.
This is not an experiment.
This is not a potential award.
This is a solid award.
And the nice thing about it is with Volvo.
Volvo has a history
of releasing new technology into the market,
and ADAS, and other places.
And again, their DNA is safety.
And so for us,
it's the perfect partner to collaborate and work with,
co-develop,
and deliver some pretty sweet technology,
and deliver the Level 4 highway autonomy
that we believe our consumers want.
In addition to Volvo,
we still have our Daimler Truck Award,
which is our activity to really work with Daimler Truck,
and again, their AV partner, Torc,
and deliver automated driving functionality and capability.
We're able to come alongside them
and really unlock what's necessary
to deliver highway autonomy.
And then lastly is a commercial agreement with MobilEye.
And you know, this award
and this partnership is really important
because if you look at where MobilEye started,
it's a similar story in terms of a startup
growing and transitioning
into essentially an automotive supplier.
And the leading automotive supplier
for ADAS computer vision technology
with very unique hardware capability,
and really the heart
and the science behind what they're doing
with their computer vision algorithms.
Those mated together have really done an amazing job
in pushing the market forward,
and delivering ADAS,
and certain levels
of Level 2 automated highway driving function.
So to work alongside them,
to be recognized as the Lidar leader
and the chosen company to partner with,
to deliver the new capability for robotaxi is really great.
And we're looking forward to that partnership continuing on.
One unique thing about the market
is a lot of people talk about data and data collection.
And they really look to the say,
robotaxi spaces
and a lot of the data being collected out in the market.
If you look at the robotaxi space,
there are a lot of big players,
a lot of money invested,
but they are dedicated routes.
There's only a certain amount of data
you can collect in those spaces.
You run the same routes,
you collect the same weather data,
you collect the same corner cases.
We believe that by 2025,
we could have a million vehicles on the road.
The nice thing about that iteration
is it doesn't take a change in hardware.
Our resolution, and our range,
and our capability today is gonna be good enough
for future upgrades of performance and time tomorrow.
We developed a single product
that can stretch into each vertical.
So whether it's past car, commercial vehicle, robotaxi,
we are not changing our architecture for each customer.
We have developed a product that we feel
is a solution for the market
to unlock that long range high resolution capability,
to unlock Level 3, Level 4 autonomy.
So, let's talk about, you know,
a bit more specific about the capability,
and the technology, and what we unlocked.
A dark tire on the road is one of the most widely used
use cases from our customers.
I want to be able to detect this dark object
5% reflectivity as far away as possible,
200 meters when I'm traveling at high speeds
and understand that it's there,
it's in my lane, and I want to avoid it.
And this is just a tire.
It could include a tire with the actual wheel or rim,
but essentially,
it's can you detect these small objects?
You know, we like to differentiate the fact
that we don't necessarily have to classify it as a tire.
We just have to understand what it is in terms of its size,
that it's in our way,
and is it a threat for our driving functionality?
If you look at what we've done
in terms of evaluating and analyzing things,
it really is a good measure of,
can I detect an object in time
for me to actually make a decision?
Is a drive over able?
Do I need to maneuver around it?
We also used say, a wooden pallet.
So if a wooden pallet falls off a truck
and you can't see that,
or if it's on the road,
you want to be able to avoid that.
Well, if we look at our performance today,
we're probably detecting say, a tire.
Around 200 meters is when we first detect the object
and we say, okay, there's something here we want to avoid.
And then when we really understand
and feel good about a target
is when we have six points on target.
I'll go back a slide.
If you look at our nearest competitor
from what we're doing on Iris,
we are approximately 125 meters ahead
of anybody else on what they can detect
in terms of that small tire on the road.
That 125 meters alone is far enough to,
I'd say, be a problem for others to be able to detect it
and then do something about it.
That's what our customers want
and that's what we plan to deliver.
So another aspect of our design
and what we're seeing in the market is,
do I have to put the Lidar in the crown of the vehicle?
We chose and we worked very closely with Volvo
to basically establish the fact
that in the crown of the vehicle
is where you really want your Lidar
to be able to see as much of the environment as possible.
If you think about yourself as a driver,
you're in the position where you can see
a fair amount of degrees,
a field of view around yourself,
especially right in front of you.
And we're not laying down in the vehicle
with our head in the bumper or in the grill.
We really have a vantage point where we can see the road,
we can see crossing objects,
we can see the lanes in front of us,
the objects in front of us.
And that's what we felt we needed for Lidar
to get the most benefit out of it.
Here, we show a few of the trade-offs
and understand that, you know,
if we're trying to see obstacles on the road,
if we're trying to see trucks, cars,
motorcycles, things of that nature,
we can still do that.
We can do that in a grill at the same distance.
If you have a clear sight of a road ahead of you,
then you can see the same object
whether it's in a crown or the roof of the vehicle
as it is in the bumper of the vehicle
or in the grill of the vehicle.
However, your vantage point up
does give you some advantages.
The first advantage is really the road
and drivable free space.
So, we essentially double the drivable free space
if we go from being in a grill
to being in the crown of the vehicle.
And what that really means is from your vantage point,
you can see more of the ground,
you have a better angle,
you can understand the road topology.
So, sinkholes, huge potholes, things of that nature,
you can understand what that road is.
And then in terms of free space,
which is really important
when you talk about highway autonomy,
especially Level 3,
if there's something that you want to avoid
and you want to safely pull over to the side of the road,
you actually need to know
that there's side of the road to pull over into.
And the longer you can see that,
the more important it is.
So, we really believe again,
that that vantage point provided,
and you can see from the data,
that it does extend the free space capability.
The other way,
the other thing is the lane markers.
Lane markers is important
because I'd say it's one of the most neglected aspects
of automated driving that people don't think about.
Usually, people think about avoiding small objects.
But where most Level 2 systems fail today is
when they can't detect the lane,
they throw the control back over to the driver
and now you have to take back control of the wheel
or you have to be paying better attention
because either the camera was blinded
or the lanes were faint
and that the confidence level was low.
What the Lidar does is really allow you
to detect those lane markings,
detect road edges really well.
And if you're low on the ground,
there's only so far you can see.
If you're high on the crown
or on the roof line of the vehicle,
then again, you have more of a vertical aspect
and you have a better vantage point
to be able to detect those lines.
So, we're talking from 75 meters of lane detection
up to 250 meters of lane detection.
And that is, again, that's significant
because if you want to identify an object
and classify it as in your lane or not,
you want to do that as long as possible
so that that object stops.
You don't stop inadvertently
if it's in an adjacent lane.
And if it isn't in your lane,
that you appropriately stop.
So either way, we really feel like, again,
that is a correct vantage point.
However, we also understand that
there are cycle plans to vehicles
and being able to design a vehicle
with the latter in the crown
takes some decision making and some time.
So for those that require it in the grill,
we share our performance specs.
And again, what we are able to do
is still detect those small objects far away
such that in an automated driving scenario,
we still understand what's ahead of us
as far out as possible
and we decide to drive over or avoid it.
And again, that's the critical measure
that some of our customers expect from us.
So, I want to talk a little bit
about the point cloud itself
before I get into the perception software
and what the software can do.
Your perception software
is only as good as your point cloud
or your data you're getting from the Lidar.
So if you take a look at this image,
this is essentially an image on the highway
and it very clearly highlights a few things.
One, the vehicles ahead.
We put bounding boxes around it,
but that's based off of point cloud data.
It clearly identifies the lane markers
which helps us identify lanes,
the guard rails on the side of the highway,
and then also, the infrastructure around you,
whether it's the opposite road, brush, trees,
things of that nature.
So, it shows you the granularity
and the clarity of the data
that we get into our perception algorithms.
Another thing to point here is
what can you see and how far can you see it?
So if you take a look at the first thing,
it's really what's the road topology?
What am I on?
What does that road look like?
And out to 75 meters, again,
this is at the crown of the vehicle.
Out to 75 meters,
you can really see the performance of the road,
understand exactly what it is, what it's doing,
curves and all different aspects of the road.
We have that data coming back to us.
And then if you say, okay, well, what's next?
Beyond that, you have say 150 meters
where you actually detect lanes.
We talked about, at the crown of the vehicle,
you can detect lanes out to 150 meters.
What that really does is allow you
to identify what lane you're in
as well as what lane those objects are in.
So all those boxes that you see there
and all those vehicles,
we can do a lane assignment for each of those
out to 150 meters,
mainly because we have the capability
to detect the lanes reliably
and confidently out to that range.
And then if you look at vehicles,
motorcycles, things of that nature,
we can see those out to 250.
And it's not just the first detection,
hey, there might be something out there.
It is actually identified as a cluster of points
that equates to a vehicle,
whether it's a car, a motorcycle, a truck.
It's our ability to be able to see that far
and understand, again,
not necessarily the lane assignment,
but exactly what it is
and the fact that it's something that we want to track.
So once we know that there's an object out there
and we can understand,
and we'll talk about segmentation in a minute,
we can understand what it is,
then we can track it.
And we can basically check for it as a threat
as we're driving with it,
as we're going past it,
whatever the case may be.
But your point cloud is the foundation
for what you do in software.
Another aspect of our point cloud which is really neat
is the reflectance and the resolution of the reflectance.
Imagine that you're on the road,
your headlights go out and it's completely dark.
You can only see as far as your eyes
can see through the darkness.
We don't have a reflectivity measure that comes back to us,
but the Lidar does.
So the Lidar can basically see the scene
in the same way that we see with our eyes
but without the light.
And that's the reflectivity of all aspects of the road.
The road edge is different than the ground
is different than the grass
is different than the trees.
The lane markings, vehicles, license plates, tail lights.
So all these things give us,
I'd say, a third dimension of data
that we can then use to develop
and further develop our algorithms.
So if you look here,
you'll see that there are vehicles detected
on the right side or in front of the vehicle
where we're seeing tail lights and license plates.
We can see that.
We can see those vehicles
whether it's dark or whether it's daylight.
There's a reflectivity measure that comes back
and allows us to perform at an even better level
than, say, cameras,
where we're looking at 2D information
and are really dependent on a light source
to be able to detect objects.
What we really love about this aspect
is that it clearly shows our ability
to also perform in inclement weather or at nighttime.
So, it goes back to availability.
If it's dark and your lights aren't on
or say, you have a malfunction,
it's cloudy, if it's dark, rainy, foggy,
and we'll talk a little bit about that,
there's a reflectance measure
that helps us determine what's in front of us
and then helps us control the vehicle still.
So we're not limited in terms of availability
and we're not limited say, by our visibility,
and what our vehicle performance can be.
We talked about weather a bit.
This is an image of penetrating rain.
And you see the rain level.
So this is a heavy rain that we're showing here.
The camera image is a bit skewed
because typically, you won't have water drops
on a camera that's behind the windscreen.
So you'll clean that off.
But still, we've all driven in heavy rain
where our visibility is limited
to maybe a few cars in front of us
or it's at least heavily restricted.
Whereas if you look at the Lidar,
the Lidar can see further out,
and we're at 1550, so we have less effect by weather.
And we're able to see objects, obstacles, road edges, lanes,
all the things that are important
for us to continue driving.
Today, I think a lot of people
disengage systems when it's raining
or when they're not comfortable
because they don't trust the system that is driving them,
even for a Level 2 system.
Our goal is that we continue the same performance,
maybe a slower performance.
Maybe you want to degrade or slow down
because it's not the same safety level
to drive at 80 miles per hour
as it is at 40 miles per hour in rain.
So, we all acknowledge that.
But we still want to be able to detect
and understand our environment
and perform accordingly in an automated driving mode
or an autonomous driving mode.
The next thing we're looking at is penetrating snow.
And this is a really neat picture.
What's really unique about our system is at 1550
in our high range and our high resolution,
what we're able to do is detect the snow
and not necessarily be distracted
or have our signal dispersed by the snow.
So we detect the snow where there is snow.
And then where there's not snow, we detect the object.
And so what you see here is an image of a snowy day
with the home behind that
and the trees and the brush there.
We can basically detect where the snow falling.
You can see that snow falling there.
And then we detect the object behind it.
So it really does show you
that we're not worried about weather
degrading our performance in terms of our detection.
Of course, heavier snow,
you have more detection of snow,
but the goal is that we still
get enough points on the object,
whether it's a vehicle for the lanes,
road edge, things of that nature,
and we can still perform
and deliver the autonomous driving function.
Another good example is fog.
I really like this one in particular because for fog,
there's different varying levels of fog, of course,
but the moderate fog is where
you have a little bit of confidence in what you see,
but you're not really sure about what's out there
and you are a little bit more hesitant
to maybe move slower,
but not as restricted as a heavy fog situation.
So in this instance, even with the camera,
you can see the vehicle off to the left.
You can see some resemblance of lane edge.
You may be able to see,
may not be able to see road markings.
But you definitely usually can't see
the divided road sign.
And you just have a limitation
in terms of your range and your distance.
But again, with Lidar, we can see the image.
We can see the information,
be it the Lidar point cloud directly,
be it the reflectance.
And we get the right amount of data
that says, hey, this is a road.
These are the boundaries.
Here are the poles.
Here's a car that's parked there.
We can understand whether it's moving or not.
And you can also see the divided road sign,
so you know that there's a split coming up.
So what's really important is that again,
in a moderate scenario,
you might have some confidence,
but the Lidar has the same amount of confidence.
The next image is of, say, penetrating heavy fog.
And for heavy fog,
if you've ever been in this driving scenario,
it makes you a little bit nervous
mainly because you really can't see what's in front of you.
What's worse is you don't know
what the objects in front of you are doing.
Are they braking?
Are they slowing down?
Is somebody getting over in your lane?
Things of that nature.
For me, this scenario, and say, dust,
a heavy dust storm
where you really can't see what's in front of you
and you have no idea if somebody is braking
because they're nervous.
You know, those scenarios
where you're almost completely blind
but you can't stop
because you know you have people behind you
and they can't see either,
so you need to proceed forward.
This is where I think our Lidar really shines
in terms of being able to see the lane markings
and the lanes out to a certain distance
to be able to detect objects.
If you look, there's a car that's essentially detected
because of the signal returns from the Lidar at 80 meters.
And so, it's really important that,
you know, we would all want to degrade our speed.
We would all want to be more cautious,
but you definitely want to be able to see
beyond what your sight
and beyond what the camera can see.
I want to know what's going on.
I want to know if a vehicle is stopped ahead.
I want to know if there's road ahead
or something has changed.
And so what's really impressive is,
and these are real world scenarios.
These aren't things that don't happen.
And ironically, with the camera,
it would basically say, hey, system not available.
Driver, you must take control.
And if you've ever driven, say,
East Coast, Virginia, things of that nature,
you can go up a hill and be in fog
and then come down and be out of fog.
So, that toggling back and forth of the system,
system enabled, system disabled,
we don't want that.
We really want our Lidar to be able to function
no matter what the environment states.
And this is a good example
of even in a heavy fog situation,
we can understand what's ahead of us
and we can basically enable the vehicle to act accordingly.
Now, we want to talk a little bit about perception.
So, that's the point cloud information.
We get really solid point cloud information,
which then helps us deliver the function.
And the perception here is understanding a complete scene.
This is like a first understanding of what is all around me.
What am I looking at?
What does the system understand?
What do I need to do?
And what we do is call that semantic segmentation,
which is basically what our mind does anyway.
We identify this is the road,
that's a rail, up there as a bridge,
that is a vehicle, this is a motorcycle.
It's not just necessarily classification,
but it's the separation of the different objects.
Even if you see a pedestrian in front of a vehicle,
you understand that there's a pedestrian there
and a vehicle,
and you don't miss the pedestrian.
That's what semantic segmentation is,
developing a complete understanding of the scene
and the environment around you,
so then you know what to do with that data.
The other thing about Lidar that is different from camera
is we are basically getting information in 3D,
so we know the size of an object, angle angle,
and we know the range of the distance of that object,
which is extremely important.
It's not just that it's there
and we're looking for an image to get bigger over time
or smaller over time.
We're measuring the exact distance from that object
so we know are we approaching it and it's slowing down,
are they moving away and speeding up?
Typical systems today use a combination of camera
and radar fusion to do that.
We can do that all in one sensor.
And that 3D data gives us all the information
we need to be able to unlock the perception
and the control functionality for autonomous driving.
The other aspect of our perception software
is being able to detect and classify objects.
This becomes very important
because you want to understand,
again, we talked about semantic segmentation
and understanding the entire scene.
Now, it is, what is it
and do I need to pay attention to it or not?
Do I care about it
or do I allow my algorithm to go look
and worry about other things?
So it's the detection of vehicles, pedestrians,
a road edge, fixed objects, crosswalks,
things of that nature
where you can actually really detect and classify,
and then put, say, a confidence level
to whether or not you need to do something about it or not.
Again, this is only unlocked
and enabled by the point cloud that we provide.
It's the same thing with the camera.
A camera does the same thing.
It sees things,
it classifies objects as pedestrians or vehicles.
But the nice thing about our data
is we get that understanding
as well as the range estimation
and velocity estimation of that data
all on the same sensor.
So when you think about the highway,
I want to know what's out there.
I want to be able to classify it as a vehicle,
as a motorcycle, as a pedestrian,
that also tells me how quickly
things can move out of my way.
It tells me a lot of information
the same way we use our information today.
If I see a pedestrian on the side of the road
and I'm traveling at a certain speed
and they're far enough away,
I know I don't have to worry about them
as an obstacle coming in front of my vehicle.
Classifying them as that in my mind
is the same way that we classify it as a Lidar based system
and understand again what are threats and what are not.
So, very important that we have
that capability within our system.
The next set of information is the road information.
And we talked a bit about
our ability to extend the availability of lanes
and be able to see lanes.
Ultimately, you want to understand where your lane is going,
where it's headed,
so that you can control the vehicle safely.
Are you coming up on a curve?
Is there an intersection?
Whatever you need to know about the lane
and topology of the road is very important.
From a highway perspective, highway driving perspective,
I want to know what's in my lane,
how far it's out there,
and am I approaching it or is it moving away from me?
One of the more difficult things for a Level 2 system
and for an ADAS system in general
is for a camera to understand that there's an object ahead,
but not necessarily be able to understand
whether or not it's in the lane
or if it's in an adjacent lane
just because of the egomotion of the vehicle
and not necessarily being able to measure the location.
It can estimate the location, but it can't measure.
Usually, you use radar
to understand the steering wheel angle,
where you're headed, you're heading,
how far an object is.
And you try to fuse that data
and come up with an estimation for where it is in your lane.
The nice thing about our Lidar is
we're detecting lanes real time with our 3D data.
We're detecting the vehicle real time with our 3D data.
So we get coordinate data back
that tells us exactly if that vehicle is in our lane
and in our egopath
based off of the vehicle steering control information.
So with that,
we have the ability to understand maybe 150 meters away
that this object is out there,
it's in my lane,
and if I'm approaching that vehicle quickly,
maybe I need to make a lane change now
before I come up on that vehicle.
Or if there's a stopped vehicle,
or if there's a vehicle that's slowing down,
we're coming up on that vehicle too quickly,
I don't necessarily have to figure out over time
if that vehicle was in my lane.
I know exactly that it is
and then I can basically enable the vehicle
to do something about it.
Very unique capability with Lidar.
Very unique capability with our Lidar,
because again, we see much further
and we see in enough time for you to make a safe maneuver
and allow that vehicle to make a safe decision.
Safe autonomy, don't forget that.
Here's an example.
High range vehicle to lane association.
So this is just a small example
where there's a vehicle out there.
And again, we're giving it a lane assignment.
We're not depending on other sensors
to help give that assignment.
And again, we can do that further out.
You see the further range is 110, 145.
We have that capability.
And what it really does is makes us a wholly owned sensor
that can deliver highway autonomy
in terms of lane, object detection,
and understanding of the complete scene.
So, I've said a lot about Luminar, Lidar, and our system,
what we unlock, what we enable,
a long range, high resolution sensing,
really the only sensor on the market
that can really unlock and deliver that capability.
If you look at everything we've talked about today,
it's what problem do we solve?
And the problem we solve is the hands off,
eyes off functionality on the highway.
We focus on highway
because that is the most value to the consumer today.
We really feel like we're enabling Level 3,
Level 4 driving.
And again, with our partnership
and our business awards with Volvo, Daimler Truck,
and with MobilEye,
that should be clear that we are the market leader there
and we have a great foundation on which to grow upon.
We also talk about the performance of active safety.
My focus today was on highway autonomy.
Well, what's important to remember
is that safety is the underlying requirement there,
safe and ubiquitous.
So when we talk about high speed driving,
we want to make sure
that we can safely control the vehicle,
that we can safely brake the vehicle.
When you talk about existing systems today for ADAS,
a lot of those are low speed.
We're trying to mitigate the accident,
lessen the severity.
We want to eliminate things.
If the Lidar can see in inclement weather,
if it can see during nighttime conditions,
if it can see in fog and things of that nature,
why shouldn't your AEB system
work in those conditions as well?
A matter of fact, it may be more pertinent
that it works and detects things earlier
because the ground is slicker
or the system is compromised
because of the environmental conditions.
So the earlier I can detect something in my way as a threat,
the better I can do something about that.
So proactive safety is something
we don't want to forget about.
And lastly, I want to reiterate the all requirements matter.
Again, you'll hear a lot of noise in the market
around range and resolution
and what these different capabilities are.
We talked about the top row,
which is really the performance,
every aspect of performance,
range, resolution, fidelity,
field of view, weather performance.
It's all important.
And then you can't forget about the bottom row.
We are the automotive grade,
serious production development program,
serious production program for Volvo.
So that is going to be the product
that I would say is going to really unlock
the capability at a consumer level
that is robust, safe, and has high quality.
We have the advanced manufacturability there
so that we'll get the yield
and the performance that we need
in order continue to supply the automotive market
with scale as we continue to grow our volume
and our capability there.
And it's also important that we understand
in order to deliver that,
it takes an organization that is sized and skilled for that.
So you have to have the right capability
in terms of quality systems.
You have to have the right engineering resources.
You have to have the right leadership and mindset.
We are delivering highway autonomy.
Thank you for joining us in this special presentation today.
To learn more, please visit LuminarTech.com.
Thank you.