FINAL WORD
“
INNOVATIONS ARE THE KEY
TO KEEPING UP WITH IT COMPANIES
IN THE COMPETITIVE FIELD OF
AUTONOMOUS DRIVING.
In an industry like automotive, the
number of possible AI use cases is large
and essentially divided into four segments
which are autonomous driving, connected
vehicles, mobility as a service and
smart manufacturing.
From an infrastructure standpoint, these
distributed problems require different
strategies and may require smart algorithms
on the consumer’s device (smart phone), in
the vehicle, and in the cloud, plus long-term,
secure data management for compliance.
Naturally, there are overlaps between some
of these segments; success in one area
can yield benefits in another. For example,
autonomous driving may be a key element
of a mobility-as-a-service strategy. There are
also many requirements that all segments
have in common, including infrastructure
integration, advanced data management,
security, privacy and compliance.
There are, however, challenges to achieving
full self-driving. Each car deployed for R&D
generates a mountain of data; 1TB per
hour per car is typical. Teams can expect
to accumulate hundreds of petabytes to
exabytes of data as autonomous driving
projects progress.
This raises several critical questions such
as how to create a pipeline to move data
efficiently from vehicles to train a neural
network or how to efficiently prepare and
label data for neural network training.
Some questions that need to be addressed
are how much storage and compute power
is needed to train a neural network, to run
inference on a trained neural network and if
the training cluster should be on-premises or
in the cloud. It is also important to determine
how to correctly size the infrastructure for
data pipelines and training clusters including
storage needs, network bandwidth and
compute capacity.
Cars and other vehicles are quickly
transforming into connected devices, and
there are a number of immediate use cases
for AI in connected cars such as personal
assistants/voice-activated operations,
telematics and predictive maintenance and
infotainment/recommenders.
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INTELLIGENTCIO
are significantly different than those in
autonomous driving: How do you predict
customer demand? How do you optimise
fleet efficiency and minimise customer wait
times? How do you dynamically set prices
in response to demand? How do you ensure
passenger physical security? How do you
protect customer data, prevent fraud and
balance privacy versus convenience?
The auto industry has a lot on its plate.
Companies must look for ways to increase
operational efficiency to free up capital for
investments like those described above.
Industrial Internet of Things (IIoT) and
Industry 4.0 technologies are the key to
streamlining business, automating and
optimising manufacturing processes, and
increasing the efficiency of the supply chain.
Fadi Kanafani, Senior Director Middle
East, NetApp
Today, cars use cellular and WiFi connections
to upload and download entertainment,
navigation and operational data. In the near
future, we’ll also see cars connecting to each
other, to our homes and to infrastructure.
For example, Audi has already introduced
technology to connect cars to stoplight
infrastructure, enabling drivers in select cities
to catch a ‘green wave’, timing their drives
to avoid red lights. That’s just one of many
opportunities to use data from connected cars.
In the future, car ownership may decline
in favour of various forms of ride sharing,
particularly in dense urban areas. Car
companies will need to become mobility
service companies to address changing
consumer demand. Many car companies
such as Ford and home-grown Careem are
already branching out, acquiring scooter
and bike-sharing companies and creating
delivery services.
The Machine Learning and deep learning
problems in mobility-as-a-service models
Common manufacturing use cases include
an increased use of computer vision for
anomaly detection, process control for
improved quality/reduced waste, predictive
maintenance to maximise productivity of
manufacturing equipment.
Competition in the auto industry is also
fierce. Leaders look to train their own AI
specialists and developers and co-operate
with other companies to maintain their
standing. While these measures are intended
to close the current knowledge gap, it also
helps achieve the overarching goals of
higher product quality, better customer
experience with AI and reducing operating
costs. Innovations are the key to keeping up
with IT companies in the competitive field of
autonomous driving.
The benefits that AI brings to the
automotive industry are perceived as
excessive. At the same time, there is
an increasing pressure on business
representatives not to miss out on the next
big thing. Industry studies usually stop at
a point where they become interesting:
the impact on daily work routine. It would
be exciting to see which AI technology
the experts in the automotive industry are
working on and what challenges they face. n
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