Developing a prediction model for estimating hardware requirements based on standardized embedded system characteristics in the automotive domain
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Examensarbete för masterexamen
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Modellbyggare
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Introduction: Modern day automotive vehicles often contain complex embedded
systems that handle behavior of vehicles. These systems often comprise more than
a hundred electronic control units (ECUs) that are responsible for different vehicle
functionality, each requiring various degrees of computational power, and thus hardware
requirements.
Objectives: This thesis presents the development and application of prediction
models that are able to predict baseline hardware requirements for ECUs in terms
of CPU, RAM and ROM metrics for vehicular embedded systems. These models
are based on what common high-level features that ECUs have with the features
largely being realized by standardized base software modules from the AUTOSAR
Classic standard. The created models aim to introduce another way of validating
hardware characteristics on ECUs or propose suitable hardware baselines for new
ECU projects.
Methods: From the base software modules standardized by AUTOSAR, a grouping
of said modules was presented to be able to characterize common ECU high-level
features in Volvo Cars Corporation embedded systems. This characterization was
then used as input to create regression neural networks that, given a set of features,
are able to predict an estimation on what CPU, RAM and ROM size is required for
a proposed ECU.
Results: The developed models are currently able to predict an estimation on
resource utilization for 32-bit CPUs, RAM, and ROM at runtime for an ECU. This
estimation is rounded up to a suitable baseline (total available measure on an ECU)
based on identified Volvo Cars Corporation ECU baselines. The models are able to
predict suitable hardware requirements for ECUs in 60-78% of cases.
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Ämne/nyckelord
AUTOSAR, Hardware Prediction,, Machine learning, Neural Networks, Base Software, Embedded