ADAS & Autonomous Vehicle Engineering - November 2023 - 9

Cover Story
Imagry tests its automated-driving
AI in the real world including in Haifa,
Isreal using a Kia Niro EV as the test vehicle.
in San Jose, California with an office in Haifa, Israel.
The company has two business lines, one that offers
perception and motion-planning software to OEMs
and Tier 1s for passenger vehicles and a second that
provides autonomous passenger-bus technology.
This software is already being used in three projects
in Israel and Imagry has bids for similar bus routes
in the Netherlands, France, Portugal and Dubai that
should be announced in 2024.
Imagry points to three main ways its AV software
differs from its competitors. First, it's mapless, so it
doesn't need an onboard or cloud-based HD map to
assist the vehicle as it drives itself. Second, similarly,
it's location-independent because it functions even
on roads that haven't been pre-approved. Third, it's
hardware-agnostic, so OEMs and Tier 1s can use the
software no matter what chipset they're using in an AV.
The core element of Imagry's perception system
is an array of neural networks that process, in parallel
and in real-time, different types of objects while
the vehicle is in motion. The vehicle does not need
a connection to offboard data or compute power,
other than navigation information, to get from point
A to point B. Instead, multiple neural networks are
loaded onto the vehicle that look for different types
of objects. One might be looking for traffic lights,
another for traffic signs; one for pedestrians, one for
moving vehicles, one for parked vehicles and another
for traffic lanes. Each of these networks helps build, in
real-time, a 3D layer map that represents and perceives
everything within 300 meters (984 feet) of the vehicle.
Another section of Imagry's software is devoted
to motion planning, and this is where Imagry ran
into difficulties with simulated testing. This part of
the company's code uses deep convolutional neural
networks that the company has been training since
2018 using data from test vehicles driving on actual
roads in the U.S., Germany, and Israel.
" We trained those networks for many years in
order to understand all kinds of scenarios, " Ofir said.
" We trained them on hundreds of millions of images
- on 10,000 roundabouts and on a million pedestrians
and on, I don't know, 150,000 traffic lights. I
just mean many, many, many, many, many objects.
And when we tried simulation, it didn't work for us.
It wasn't good enough for us. "
The virtual driving environments couldn't match
the variety of things that actually were happening on
the road, Ofir said. To make up for that gap, Imagry
records every drive its vehicles make and then, using
semi-automatic annotation tools, it further teaches
the neural nets to identify objects using supervised
learning. Here, too, Ofir sees limitations in what
software can do on its own.
" The problem with unsupervised learning
that you hear some vendors talking about is this:
would you get into a vehicle that is driving based
on software that learned unsupervised, without
human control, you don't know what the system has
learned? " he asked. " This is why we strongly believe
that the system has to be supervised, which takes
time. You need to walk in the desert, as it were, for a
few years until you get it to the level. But once you've
got it, that's it. "
ADAS & AUTONOMOUS VEHICLE ENGINEERING
November 2023 9
ADAS & AUTONOMOUS VEHICLE ENGINEERING
November 2023 9
Imagry
ADAS & Autonomous Vehicle Engineering - November 2023

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