Autonomous Vehicle Engineering - May 2021 - 23
Artificial Intelligence/Machine Learning
is an approximate model that replaces
the full-scale stochastic simulation.
" We could use deep neural networks
(DNN) where, when you have a particular platoon configuration and there are
a number of uncertain variables, instead
of having to run an ANSYS simulation
each time to calculate the drag force, for
example, you just need to evaluate these
deep neural networks to calculate the
drag and also the gradient of the drag
with respect to the design variables, "
Meidani said. " The main difference is
that deep neural networks are easy to
evaluate - they take seconds or sometimes less than a second to evaluate
these drag forces. Once this model is
trained, it can be used inside an optimization and that optimization is going
to be a lot faster. "
Researchers wanted to determine
how well a deep neural net could predict
the drag force for new scenarios that
the training had not seen yet. " We ran
1,000 realizations of a two-truck platoon
- this is a very time-consuming undertaking. We had to use supercomputers
to facilitate the simulation of these trucks
with ANSYS, a process called Design of
Experiments, " Meidani said, noting that
85% of that dataset was used to train the
neural network and the remaining 15%
was used to test the DNN's accuracy
on previously unobserved data. " The
accuracy is about 90 percent, which
is pretty good for this very-complex
nonlinear model, " he concluded.
For longer platoons, convolutional neural networks (CNN) can be
used to create a modular single-truck
surrogate when the input layer is an
image. CNN is a machine-learning
approach to analyze images and has
been widely used in image classification, segmentation, detection, etc. " So,
you could use image-based approaches
in order to predict the performance or
the response, " Meidani said. Using a
physics constraint (partial differential
equations) can better train image-based
surrogates (CNNs), he added.
AUTONOMOUS VEHICLE ENGINEERING
" If you don't include the physics,
the training will be purely data driven, "
he said, " and you wouldn't know
whether a candidate prediction makes
sense physically. " Future work includes
using CNNs and variational autoencoders to optimize the geometry of tractor-trailer bodies in the platoon (using
wind deflectors, for example), he said.
" As we increase the level of automation for truck platooning, the need
for using AI increases, " Meidani said.
" Evaluation of costs needs further studies,
but it is expected that the return of investment - as a result of fuel efficiency and
higher safety - covers the costs of AI. " ■
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Autonomous Vehicle Engineering - May 2021
Table of Contents for the Digital Edition of Autonomous Vehicle Engineering - May 2021
Autonomous Vehicle Engineering - May 2021 - CVR4
Autonomous Vehicle Engineering - May 2021 - CVR1
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Autonomous Vehicle Engineering - May 2021 - CVR4
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