i3 - May/June 2020 - 21

McQueen, CEO of Bob McQueen and Associates
in Orlando, FL. The firm advises the private sector
and aids the public sector - departments of transportation in the U.S., Europe, Asia and the Middle
East - with regard to connected and automated
vehicles. According to the U.S. Department of
Transportation there are roughly 1,400 autonomous vehicle field tests nationwide, but no sharing
of the lessons learned, he says.
But McQueen says that driverless vehicle development may have reached the maturation level
where technology sharing makes sense. "Why is
there no equivalent of the FAA (Federal Aviation
Administration) for technology applied to road
transport?" he asks. "That might be an organizational answer to sharing data, moving the industry forward."

DEFINING LEADS TO UNDERSTANDING

Because cars can harm or kill, it's important to be
certain that a self-driving car won't, and testing inefficiencies are limiting the ability to have absolute
certainty, says Ziv Binyamini, CEO and co-founder
of Foretellix Ltd., based in Ramat Gan, Israel. Predominant testing processes
can deal with hundreds of situations while what's needed is the ability to deal
with tens of millions, he expounds. And a root cause of this problem is a
paucity of common definitions or language to describe potential scenarios a
self-driving car may encounter, so that "all stakeholders - from developers
to testing engineers to regulators - can talk about the same thing," Binyamini
says. Once that's achieved, the next step is setting metrics: defining the levels
of safety expected of a vehicle in given circumstances.
Foretellix has developed a "measurable scenario description language"
named MSDL for the industry to use and is contributing to the development of a new standard named OpenSCENARIO 2.0 by the Association
for Standardization of Automation and Measuring Systems (ASAM).
Binyamini says, "You need regulation to define what is safe enough"
based on quantifiable verification. Foretellix also devised a method called
"coverage driven verification" that defines a driving scenario (such as one
car cutting in front of another) in a very high level way (specified once) and
with software automatically generating many meaningful variations on it,
including unexpected results. This capability can be built into a car, he
adds, and what it finds together with real road data from driven test vehicles
can be later aggregated in a central "safety dashboard" for vastly more efficient industry-wide learning.
"The industry suffers from ambiguity in terminology," and not just regarding self-driving car technology, says Heikki Laine, vice president of product
and marketing at Cognata Ltd., based in Rehovot, Israel. The same applies to
current advanced driver assistance systems (ADAS), Laine says.
Cognata makes a simulation platform and training data for automakers
and their Tier 1 suppliers to develop autonomous driving and advanced
driver assistance systems. "At the end of the day, the big challenge is around
the diversity and the scale of exposure that the system has to highly accurate, highly realistic perception data."
C TA . t e c h / i 3

i3_0520_Feature_SelfDrivingCars.indd 21

The principle is the same for an "automotive visual intelligence platform"
developed by Cartica AI, based in Tel
Aviv, which teaches vehicles to see the
world through object "signatures," says
Karl-Thomas Neumann, a strategic advisor to the company. Just as people learn
to distinguish a wine glass from a water
glass but recognize the common elements
of each, Cartica's technology lets a car
learn as it travels by picking out, for
example, signature attributes of a traffic
sign that it has never seen before or one
that is damaged or mounted incorrectly.
It gets better with experience and,
although it's now aimed at ADAS, it can
be adapted to self-driving cars when
needed, Neumann says.
Over the next five years, automakers
will roll out in-vehicle platforms and
architectures that can be updated frequently, as opposed to occasional overthe-air software updates, notes Artur
Seidel, vice president for the Americas at
Elektrobit, a global supplier of software
for the automotive industry. "But the
motivation can't be 'I do this to get my
warranty costs down,'" Seidel says.
"That's the wrong mindset." Rather, he
says, those ongoing updates should be
for refining a vehicle's AI. Because AI
performance is a consequence of the
underlying training data, the industry
needs to come up with processes for
sharing that data within and between
companies, he says. "We're still one order
of magnitude removed from understanding the larger scenes of situations," the
intention and associations of objects.
Seidel adds, future vehicle systems
must be architected to account for changing sensor technologies. "One strength
that the vehicle can have compared to a
human being is, our sensors, as good as
they are, are limited to our eyes and our
ears - and the car can do better. While it
will not overcome the shortcomings that
may exist on the AI side," he explains,
"every car has to ship with significant
headroom" in terms of hardware, to meet
evolving software requirements over a
decade's time. "That's actually the biggest
shift now."
MAY/JUNE 2020

5/7/20 12:16 PM

http://CTA.tech/i3

i3 - May/June 2020

Table of Contents for the Digital Edition of i3 - May/June 2020