Autonomous Vehicle Engineering - September 2021 - 5

Cover Story
Embark uses " active learning "
techniques to identify the most
relevant detections and provide
the most useful insights into
critical edge cases.
to identify the most relevant detections and provide
the most useful insights into critical edge cases.
" You can think of active learning as a way for
us to understand the ways in which our machinelearning
models are failing, " Moak explained. " We
can actually sample our data using this technology to
build high-quality datasets that are lower in volume
but higher in quality to get more performance out
of our systems. "
The level of preprocessing required to make sure
the raw data is useful for machine learning is a key
challenge, said Tom Tasky, director of Intelligent
Mobility at FEV North America. The supplier has a
patented preprocessing and analytics solution that
handles up to 40 TB of data per vehicle per day in an
L3 Pilot project taking place in Europe. The amount
of time and effort involved in this part of the process
can be underestimated, he said.
" Once you start developing and looking at the
data, you see it might be poor quality and how much
additional software effort is required, " Tasky said.
" To really understand the sensors, the quality output,
any limitations in certain environmental conditions,
things like that really need to be factored in to make
sure you have time to account for it, prior to running
some expensive tests. "
Field data analytics are extremely valuable
for the development and production of optimized
components, as well as to identify design weaknesses,
status analysis and predictive maintenance. " Especially
if you look at ADAS features, these are new components
being developed and you don't necessarily have
the same reliability data in different applications being
introduced, " Tasky explained. " Discovering that information
is extremely valuable to [determine] trends
and the life cycle of these new components. "
The digital twin also is a useful tool in identifying
potential failures and determining root causing issues.
A complicated example, according to Tasky, is when
there is onboard monitoring with offboard failure
analysis and a digital twin.
" You enable this through the use of a gateway
that has the connectivity aspects to communicate to
the digital twin in the cloud, " he said. " There's a lot
involved with setting up this infrastructure, which we
help our customers with today. But the value of this is
identifying failures well in advance during development
or even field data in fleets. "
Smart diagnostics
As automated-vehicle (AV) developers began
combining more advanced sensors such as lidars,
infrared cameras and L4-specific sensors for redundancy
and higher integrity, diagnostic capabilities
lagged initially, according to Ananda Pandy, technical
specialist for ADAS and autonomy at ZF.
AUTONOMOUS VEHICLE ENGINEERING
September 2021 5
Embark
Autonomous Vehicle Engineering - September 2021

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