I'm the senior vice president of product development,

here at Luminar.

Today. We're going to talk about Lidar design,

integration and performance.

The evolution that we're going through

in autonomous vehicles

is like a metamorphosis for the driver.

They're going to change the way

in which they interact with the car.

Human drivers are pretty amazing.

They have the ability to have situational awareness

that autonomous cars don't have right now.

Specifically ADAS especially.

So if you look at the,

the sign here on the left,

you can tell, hey,

there may be a deer coming soon down the road,

and a driver may, you know, change the way they're driving

based off of that.

The picture below, there's an icy bridge,

and that may affect

how somebody is going to approach that bridge,

or how they're going to make the turn on that bridge.

Predictability is very difficult in autonomous cars as well.

So if you look at the picture on the right,

you see that car has a, a mattress that may come flying off,

and it may affect where you drive,

or how you drive around that vehicle.

The user experience transformation that's going to happen

is going to be pretty dramatic.

It's going to be one from

hands on, eyes on,

focused,

you know, paying attention to everything that's happening to

hands off, eyes off, relaxed,

and more enjoying the experience.

So, when it comes to autonomous driving,

you have to put safety first.

So we're going to walk through some details around safety.

This is a study that was done by AAA a little while ago.

Basically went through, you know,

how current ADAS systems perform

when it comes to pedestrians.

And we're going to talk about

how we can progress and improve on that.

In current ADAS systems,

you have camera and you have radar.

But you need additional modalities

to solve some of these corner cases

that we're going to walk through.

This chart kind of overviews

some of the areas in which Luminar's Lidar,

helps with different areas where cars can't see right now.

So in this video that we're gonna walk through,

you can see there's something that shows up in the road,

but it's difficult for the camera to pick it up,

because of the overhead lighting,

and because of the shading in the entire area.

When you, when you look at this scene with Luminar's Lidar,

immediately you can see that's a ball in the road,

there's actually a small child off to the side,

that's hidden in the shade.

And you can see the major advantage that's given to you

by using Lidar in this situation.

In this case, we're going to look at

Lidar's role in different weather conditions.

here in the upper left-hand corner you can see

what the camera sees in fog.

The issue with cameras and fog here

is they mostly work off of contrast.

So the contrast that you see here,

it's difficult to make out

what's happening in the road very far up,

as you look through the fog.

However in the picture on the right,

you can see the division in the road,

this, this, the sign that's there

that's identifying the division in the road.

And lidar can kind of punch through that fog,

and see all that detail,

so the car can do its path planning.

In this next situation, we've got

some specific corner cases we're going to walk through.

So first we've got an image that shows

a couple of small objects in the road, a muffler, a tire,

some lamp version targets that we'll walk through.

The issue here is a small object, in a road,

at a very far distance.

This is something difficult for cameras to pick up right now

and radar.

So, for example, take that tire.

It's a dark color,

it's dark against the road,

and if it's at night, at a far distance, 150 meters,

the camera's not going to be able to see it,

and the car is not going to be able to see it.

But if you look at the, the point density,

and the distance at which Luminar's lidar can see

based off this chart,

you can see that all the way out to 150 meters,

we can still put points on that target,

identify that targets out there,

and start to make path planning decisions

based on identifying that there's an object out there,

and that we may have to watch out for it.

These are the kind of details that are required

to get true safety in autonomy.

Let's talk about field coverage a little bit,

in relation to different sensors and their locations.

So first, there's many different sensor configurations

that you can put on a car.

In these two examples, we have Highway Pilot,

where there's configurations with lidar looking forward.

On the right-hand side we've got full self-driving vehicles,

which need, you know, modalities all around the car.

In this example,

we're going to talk about the backup camera.

It's placed behind the car,

typically down near the license plate.

And it's there very specifically

because that's where the driver can't see.

It's a blind spot.

So the camera is able to pick up

whether there's a bike, or a small child,

or some somebody, somebody directly behind it.

And that's the reason why it's placed in that location.

In this next example, let's talk about ultrasonic sensors.

So typically they're placed

all around the skirt of the vehicle.

And this is for

situations like is shown here in parallel parking.

So the vehicle needs to see all around its edges.

Now let's talk about lidar,

specifically long range lidar like Luminar creates.

If that lidar is placed in the bumper

as is shown in one of these images,

you can see in the simulation here

that a lot of the view of the lidar

is obscured by the vehicles or other objects in front of it.

So a lot of the sensing that you need to drive

isn't really available.

In this next example,

if the lidar is placed at the roof of the vehicle,

you can see the dramatic difference in this simulation.

That even when there's vehicles directly in front of it,

it, it's able to see not only the cars around it,

and the cars in front of it,

but even the road in front of those cars.

So if you really want to get to true L3 level,

hands-off driving, you're going to need this kind of

ability to see out at a distance.

Kind of equate this to, you know, in this example, you know,

an ant, you know, down in the grass, versus a giraffe,

being able to see way above it.

You know, animals have eyes at the top of their head

for a reason, it's to be able to have that perspective.

It's also probably the,

the difference between driving on the highway in a go-kart,

or, you know, in a semi-truck.

You need that, that point of view.

This example shows

Luminar's lidar integrated into a roof line,

and the view at which it actually sees out on the road.

You can see how, how far the lane markings are marked off,

how much of the, the road surface that we can actually see,

and the distance at which we can see the vehicles.

Now let's talk about the sensors need to see.

And what, what's going to be required

to ensure that the lidar can properly see.

So there's many different design considerations

that have to be taken into account for sensor availability.

These are a few examples that radar and lidar in ADAS cases

have to already contend with.

So you've got rainy roads, snowy roads, gravel roads,

you know,

all kinds of different wet and inclement conditions.

Lidar has to deal with those same conditions,

and you have to design for it.

In this case, we're talking about

both the placement considerations

for where you're going to place a lidar,

as well as, you know, where the lidar can stay clean,

you know, the most easily.

So in the, the image on the left here,

you can see the laminar flow that's happening over the car

allows for the top region of the vehicle

to stay relatively clean and free from debris.

Down here at the grill, the vehicle near the front,

you can see where contaminants, and snow,

and other things are going to pile up,

and that'll cause you to use a lot more fluid

to keep the sensors clean and freely operating.

The end goal for lidar as is with other sensors

is for a touchless,

you know, cleaning and maintenance free solution.

We don't want the end user to have to worry about

whether the sensor is going to be available all the time.

There's many different things that we have to do

to ensure that that's going to happen.

The first one is heating.

We have to be able to heat up the window

to make sure it can defog, as well as, you know,

take ice or snow off of the window.

The next one is if you get dust, or dirt,

or other things on the window, you want to have, you know,

a nozzle for spraying air and water to clear those off.

And finally, potentially coatings indoor vibration to keep

the different contaminants, water droplets,

moving and off of the sensor.

So let's talk about form and function relative to the lidar.

Design integration of, of lidar and sensors in general,

you know, really started back for autonomous vehicles

in the DARPA challenge.

Back then it was, you know, engineers getting together,

throwing sensors on vehicles, and you know,

building the robots.

That's progressed slowly over time.

And you can see in the, in the TRI example,

they've taken the Luminar lidars,

integrated them into the roof,

and created a dramatically different

really designed in or scaled in

sensor package,

that looks dramatically different than it did back in the,

the DARPA days.

In 2022, Luminar is going to release

an integrated lidar solution with Volvo.

This, this lidar solution is at the roof line,

it's integrated in, and it's becoming relatively sleek.

I think the thing we want to highlight here is that

safety, you know, placing the,

the lidar at the roof of the vehicle,

and designed, don't necessarily have to be

mutually exclusive.

As the sensor design gets smaller and smaller,

we'll be able to integrate this in better and better.

And you'll still have your point of view,

and the design will become more and more seamless.

This is one of the main factors

that's going to enable a transformative experience

for drivers to be able to take their hands and eyes

off the road and enjoy the drive.