Truck & Off-Highway Engineering - June 2021 - 21

AUTONOMOUS FEATURE
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 widely used
in image classification, segmentation and detection.
model to enable near real-time optimization of platoon
configurations.
" Platooning is really an optimization problem, "
Meidani said, noting that it is important to run multiple
simulations for various uncertainties. " To arrive at the
best platoon configuration, which essentially is determining
the longitudinal spacing between trucks and
their lateral position, you need to run a CFD model to
quantify the drag force, " he explained. " They're not
easy problems to solve using finite elements, so you
The purpose of the SOTIF analysis, performed using the ISO 21448 standard, is to
reduce the unknown unsafe scenarios.
typically need a full-scale high-fidelity simulation, and that may take
hours depending on how many trucks you have and how many uncertain
factors you want to consider in your problem. "
The researchers propose using a surrogate, which 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
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June 2021 21
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Truck & Off-Highway Engineering - June 2021

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