Momentum - October 2021 - 17

TODAY'S ENGINEERING
DATA DRIVES DRIVERLESS TRUCK LAUNCH
TRANSFORMING RAW DATA INTO HIGHQUALITY
STRUCTURED DATA is a critical path to
properly fueling machine-learning (ML) models
and deploying artificial-intelligence (AI)
applications across autonomous fleets.
Companies are working to overcome data
challenges to ensure their ML algorithms can
produce the AI required to achieve widespread
SAE Level 4 and 5 operations.
" When our trucks drive on the road, they're
collecting terabytes upon terabytes of data, and
we need to get that up into the cloud and into
the hands of our engineers, ultimately, " said
Brandon Moak, co-founder and CTO of
autonomous technology developer Embark,
whose recently launched prototype SAE Level 4
tractor is shown above. The startup uses " active
learning " techniques 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 go 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
MOMENTUM
Embark uses " active learning " techniques to identify the most relevant detections and
provide the most useful insights into critical edge cases.
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
where you have 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.
" The focus was on improving the computing ability and hardware
development for the 'virtual driver' and how to scale that development, " Pandy
explained. " The diagnostic capabilities of the vehicle actuation systems were still
at the same level as how it was developed for the core ADAS functions and were
dependent on the safety driver in the vehicle during these development
phases. "
As AV development enters the " shakeout " phase, where the integrated
vehicle platform accumulates significantly more mileage and infrastructure
setup begins, the focus shifts more to creating the reliability metrics necessary
to build confidence prior to rollout.
" Diagnostics play a major role in supporting these reliabilities, and it's not
just the number of miles driven without interventions or the number of trips
being completed, " Pandy said. " It's imperative to have the predictive diagnostics
and not have any latent failures in the system in order to make the call for the
driverless launch. " n
To read a longer version of this article by Ryan Gehm, editor-in-chief of SAE's Truck &
Off-Highway Engineering magazine, click here.
October 2021 17
Embark
https://www.sae.org/news/2021/08/data-drives-driverless-truck-launch
Momentum - October 2021

Table of Contents for the Digital Edition of Momentum - October 2021
