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[Music] [Music] All right. So, thank
you very much everybody for being
here. Thank you for the great introduction.
So, I'm actually here to talk about
a few things about Lidar, I'm following
a great presentation by the way,
so we'll look a little bit about sensor
technologies and how we're going to
go from ADAS to the autonomous cars and
then the evolution of Lidar - because
that's where my background kind of comes
from - and then Lidars in the context
of the endcap requirements which are
something of actuality for the mass
market. But before beginning, a quick
introduction about LEDDARTECH. Basically,
we developed a proprietary technology
we called Lidar, it's protected by
54 patents. We have extensive expertise
in Lidar development, Lidar sensors
and the applications that go around them.
We have partnerships with industry
leading global companies, one of which
is Valeo, who most of you probably
know. And our sensors have accumulated
over the years more than 20million
hours of operation in 24/7 outdoor
environments, mostly from our background
in the transportation industry, so
similar conditions as in the automotive
but outside of the cars on the side of
the roads instead of being in the cars.
But the reality of the environment are
pretty much the same and we've been
in the automotive industry since 2011. So,
autonomous cars have been a long-time
dream, we've seen pictures starting
in the 50s talking about autonomous
cars, they were also talking about flying
cars but at least one of these two
dreams is slowly becoming a reality.
So, most of you have heard about all
of these great projects, one of which
is the Uber, more recently, that
started a pilot project in Pittsburgh.
And of course, cars aren't completely
autonomous right now, these are pilot
projects, so you still have two
engineers monitoring everything in there
but we're getting very very close to
autonomous cars. Of course, this is viable
as we heard in the previous presentation
in taxi or ride-sharing applications,
where the price of the car doesn't
matter as much and the looks of the car
doesn't matter; so, if you have big
sensors, expensive sensors, it doesn't
really matter, it's a great first
industry for the autonomous cars. And
on the consumer market, should I say,
for everyday cars, Lidar and sensors in
general in these systems will have to
become smaller and cheaper before they
go to mass market. And it's a consensus
in the industry, I think, that Lidar
will be part of this of this future.
We've been hearing in the past years a
lot about Radar and cameras but a lot
of people right now are agreeing on the
fact that that Lidars will have to
be part of the equation if we want to
have a fully autonomous system and good
autonomous cars and driver assistance
systems. So, I have here a small table
comparing the pros and cons of each of
the of the sensors. As you can see,
lidar overcomes some of the weaknesses
of each of these technologies, so
it's a great third addition to this.
Of course, Lidar on its own won't be
able to do everything, the future is
really in the fusion of multiple sources
of information but we think that Lidar
will be one of them. So, today, the
Lidar providers - we've met the Lidar
providers here and you'll have basically
three or four categories of Lidar is
in there. Number one are the scanning
Lidars, and as we heard in the past
presentation, they're basically more
expensive, quite big but they provide
a lot of information for mapping and
people are developing these algorithms
and these big sensors and they're very
useful for that but of course for mass
production, it's more of a challenge,
the sensors will have to get smaller, and
there's also some reliability issues
with the scanning movements. Then you
have the solid state Lidars; some of
them are built on some older technologies
that performed okay but had very
limited range, very limited performances.
Then you have some others in there
that has very promising technologies
that are very revolutionary but has
yet to be proven, they make a lot of
promises but we have yet to see anything
coming from them. And then there's
also LEDDAR, which is basically a new
generation of solid-state time of flight
sensors, solid-state lidars which
is ready for mass production and mass
market right now through Valeo who has
the first generation of LEDDAR and we'll
see the roadmaps going forward with
that. And before we go on, I just want
to give a small explanation on what
is LEDDAR and why it's so good. I won't
go into too much technical details
but if you want to come see me at the
booth upstairs on the exhibition, I'll
be glad to explain a little bit more
about it. But basically, LEDDAR relies
on digital signal processing of the
signals coming from the photo detectors,
so instead of relying on simple circuits
to do the distance measurements,
we fully digitize everything, filter
the waveforms and then use multiple
consecutive waveforms to crunch them
together and then do
full waveform analysis.
This gives us three main benefits;
number one, higher sensitivities, so we
found that we have a higher sensitivity
than any other optical time-of-flight
technology, which in turn gives either
an increased range and/or lower
power consumption and/or lower cost. The
ability to have a fixed diffusional
nation instead of having to focalize
the beam into in a small area and then
scan around it. Then, we have immunity
to noise and what I mean by ‘noise’
could be either direct sunlight; so,
we're actually able to have sensors that
perform well outdoors with wide field
of view without the use of any optical
filters. They simply filter out the
direct sunlight and it doesn't have any
significant impact on their sensors
performances. Noise could also be rain,
fog, any kind of harsh weather conditions,
small particles that are in the
air, these are also filtered out by our
algorithms and they don't affect the
performance or the quality of the
measurement themselves as much as other
Lidars do. And finally, there's also
no performance effect from having
multiple sensors facing at each other.
So, earlier mentioned previously, when
you're in a car in a traffic jam, there's
going to be a lot of sensors, a lot
of cars, each with their own Lidars
pointing at each other’s, looking in the
same direction and LEDDAR technology
is actually immune to that, so we're
completely immune to other sources of
both slides or other sensors, they're
discarded as white noise basically and
we're sensitive only to our own pulses.
And finally, we have a few signal
processing features that we've regrouped
in a single benefit which allows us to
get a very high dynamic range, so in
the same frame, we can detect very small
signals and very high signals with
very good accuracies. So, even if you
have a very reflective target next to
a very dark small target, we'll still
be able to detect both of these, we
handle saturation very well so there's
no problem there and we can also do
multi echo in the same pixels down to
15centimeters of separation. And now,
the evolution of LEDDAR towards autonomous
driving. We feel that Lidar systems
in general and more specifically,
LEDDAR will be able to address a lot
of different applications ranging from
the standalone ADAS, the basic functions
to the higher resolution autonomous
applications very very quickly as well
as sensor fusion with the other sensors.
So, here you have our roadmap for
the next few years, just a quick word
about our business model. Instead of
doing like the villa dines and the
corner G's of this world and providing a
single product, what we want to do is
bring a chipset to the market that the
tier ones can adopt and use any of our
reference design to build their own
implementations of these Lidars, they
will also be able to build their own
implementations from scratch if they
want, meaning that they can create very
different solutions not only from one
another but also going from the simpler
level one applications up to the level
3 level 4 applications where you need
more resolution based on the same
technology, which is a little bit like
what they're doing right now with the
Radar when you think about it; they all
use the same technology or a technology
with similar principles of operation
and they each build their
own differentiated
solutions. So, we've announced
three generation of chips; the first
one is in conjunction with Valeo
with 16 channels and then we'll have 32
and 64 solutions with higher resolution.
Here's the implementation that Valeo
is doing and that you'll see on our
current generation of products, so
it's a very simple 16-pixel device. It
has an area of 16 PI in photodiodes all
lit up at the same time. There's a
single light source that pulses the
light and 16 individual measurements or
more if we have multiple echoes in the
same channel are taken. So, this is
very good for level 1, level 2, DD,
NCAP, AB type applications will work
very well. Valeo will, for example, have
a range of about 50 meters away for
pedestrians with their solutions. Then
on to the next generations, we're
looking at 1 by 32 flash Lidar, so the
same principle as before but with more
channels, higher resolution, with a
45-degree field of view and a simple laser
diode, we will have 40 meters of range
with pedestrians and 180 meters of
range with vehicles. This is, of course,
one of the possible implementations
different laser diodes, different
parameters here could change and create
different solutions with different specs.
Then we get on to higher resolution
devices. With the same chip, we'll be
able to do a 32 by 8, so the full 3D
Lidar with the same range as the
previous solution and you have the form
factor of what it will look like in here.
Then here's a small example of the
prototype one of our partners built
with using multiple sensors, what the
visualization will look like with these
prototypes. Then on the next generation,
same principle - 1 by 64 - but much
more range on this side, so the LCA 3
will have much higher sensitivity and
therefore, a much higher range of 120
meters on 45 degrees for the pedestrians
with this solution. And just like
with the previous chip, we'll have a
3D flash Lidar solution using multiple
emitter sequencing. This one, once again,
120 meters of range with pedestrians
on 45 degrees by 30 degrees field of view.
And finally, this chip will also
support higher resolution, MEMS, micro
mirror scanning, so that we can get
a high resolution less than .25-degree
resolution on both axis on 60 by 30
or 60 by 20 degrees, it could be doubled
up to get 120 degrees on the same
location on the car. And this will address
level 3 and levels 4 applications
and then move us a step closer towards
full automation. And these are simply
a few examples of the possible
implementations of these
technologies, these
are some concepts that we came up with but
the future implementations are limitless;
a lot of people could do a lot of
different things with the same powerful
technology. But if we come back a
little bit to the reality of today, the
NCAP requirements have been updated
lately to include the AEB requirements.
There was also an announcement made
by the NHTSA very recently that says
that all vehicles will be required to
have AEB functionalities by 2022. So,
we know that the AEB functionalities
are what's going to drive the market
for the next few years, that's what
is going to be produced in very high
quantities very very soon.
As a corporation,
in 2015, only 1% of the vehicles
had this functionality standard and
25% on option, which means that the
potential for growth is extremely high.
So, a lot of different sensors can do
these functionalities. We have the stereo
cameras but they're a bit sensitive
to weather and lighting conditions.
The long-range radars are kind of too
narrow to see the pedestrians at short
range when they're coming from the
sides. The short-range radar and medium
range radar are pretty good but they
usually have a low distance resolution,
low accuracy and they're a little
bit more expensive than the LEDDAR and
of course, the high-resolution scanning
Lidar a little bit overkill for this
kind of application, they're way too
expensive. And from what we've gotten,
the ideal cost to an OEM for a sensor
like this would be around 50 USD, which
is what Valeo is promoting for their
solid-state LEDDAR-based Lidar. So,
we feel that LEDDAR will be the best
enabler for all of these AEB applications
in the very near future. So, we've
actually taken to the road one of our
sensors, we've actually taken an
equivalent of this solution that you see
right here using our current discreet
implementation. So, it's larger than
what you see there but the functionality
is equivalent. We took that on the road
and had them perform the NCAP test.
So, a first NCAP test is with the back
of a car, you want to make sure that
the car will brake when it sees either
a stopped car in front of it or a
car that's going slower or a car that's
going to be braking in front of the
vehicle. So, we have a video here, could
you click on the play button on the
PowerPoint please? All right, looks like
the video won't work today but we'll
have them at our booth if you want to
come and see them. We've performed
these tests very recently, so we see
in the video the data from the LEDDAR
and we clearly see that the LEDDAR will
detect the car before their current
vehicle has to brake, thus preventing
the impact. The other tests in the NCAP
requirements are the pedestrian, both
running and walking pedestrians, at
speeds up to 60 kilometres an hour. We've
also performed these tests, come by
our booth to see them. We basically see
the adult baited at 40 metres away,
which is plenty of time even at 60
kilometres an hour to brake. We've also
done the test with a small child coming
out of behind an obstacle and once
again, the obstacle was detected before
the car had to brake and thus, LEDDAR
would be able to prevent the collision
in that case. We've also made more road
tests on the 16-channel solution. On the
right, you'll see the final production
sensors and on the left, the prototypes
that we used, they're basically
based on our discreet implementation
until the Asics, our sensors are a
little bit larger but it doesn't prevent
us from doing any tests. So, we made
a 20-degree sensor that would see
pedestrians at about 60 meters and cars
at 150 metres and then a 90-degree
laser that would see pedestrians at 12
meters and cars at 45 meters, so this
one would be more suited to do a blind
spot monitoring, for example, or a basic
short-range cross traffic ADAS in
that case. And then the longer range
to 20 degrees would be for a front and
backwards collision. So, we had a video
there, I guess it's not going to be
working. One thing that's interesting in
here is to really note how you can see
multiple objects even if they overlap
on the same segments. This is a very
nice environment, it's pretty complex
but we can clearly see the car over
here and then these big pillars will
come over here and we see most of them
or well actually all of them that are
in the field of view. So, even though
it's somewhat lower resolution than what
a Validein does, it still gives you
a lot of information of what's going
around it. We also set the 95 degrees
on the front of the car to do the
oncoming traffic alert and the blind spot
detection, you'll be able to see these
videos in our booth. This is the
20-degree sensor running in foggy rainy
winter Quebec City, the typical Quebec
City environments, and it detected the
cars beyond 100 meters very easily,
even in these conditions. So, of course,
beyond just the sensors, the AEB
systems has to do much more than just
have a sensor and locate its data, the
sensors are only the first step or one
of the pieces of the puzzle because,
as you probably know, not all of the
objects that are detected by the sensors
will incur a risk of collision and
therefore, not all of them should trigger
a braking signal. So, some object tracking
algorithms have to be added beyond
that and trajectory prediction have to
be added on top of all of this data
to estimate if there's going to be a
collision or not with the object. This
is something that typically is not
included in the LEDDAR sensors, would
be done by our Tier one customers but
we have a lot of expertise in doing so,
so we can assist our customers in
developing these kinds of applications and
in these kinds of systems, we can even
provide algorithms because we have
experience and developed some of these
in the past. So, there are basically
four first steps in the decision-making
process when you want to make your
car brake or not. Number one is look at
the data from the sensor and segment
the data points into different groups,
either by proximity, by similarity
or by the using edge detection
algorithms but basically separating them
object by object. Then we want to do
this over consecutive frames in time and
do associations over time of these objects
in each of the consecutive frames
to see where they are located at each
point in time, which will give us an
estimation of their speed, their direction
and then we can do some trajectory
estimation that runs on our algorithms
there. Typically, we'll use predictive
filters like Kalman filters to improve
the accuracy of these algorithms,
they will also give us an estimation of
the confidence level of the trajectory
estimation, so the decision-making
can be a little bit more in-depth.
And finally, we will have to make the
decision; so, is the object going to
collide with the car or not? If yes,
do I have to brake now, can I wait a
little bit, what's the confidence factor
on that? The final decision on the
AEB system is done in this step over
here after all that has been processed.
So, that's all I have for you today.
The key takeaways, basically, I would
like you to leave today with are that
solid-state Lidar are a key element to
ADAS and autonomous driving roadmaps
for mass-market vehicles even though
we see the big Valedein rotating Lidars
on the other taxis today, that will
probably not be the case
when the autonomous
consumer cars come in the market.
That LEDDAR technology is a highly
optimized automotive grade Lidar and
that next gen low-cost Lidars are able
to meet the NCAP requirements. As of
today, we have LEDDAR core chips coming
with the SP libraries that we'll be
able to address all of the other
functionalities from simple ADAS to high
density 3D point cloud and that close
collaboration between everybody in the
industry in creating an ecosystem will
be the key not only for multiple types
of sensors, creating the sensor fusion
and having people create very reliable
tracking algorithms, but also in the
Lidar side. We will be making sure that
in conjunction with our chips, various
partners offer different parts that
are compatible with this technology, so
our customers will be able to choose
between different photo detectors,
different emitters, different emitters
scanning principles if needed, different
processors if they want to do further
processing on the external side and
basically, oversee and develop all this
ecosystem. So, thank you very much.
Moderator: Thank you very much, that was
very interesting. [Music] [Music]
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