Pöllot M (2020)
Publication Language: English
Publication Type: Thesis
Publication year: 2020
Publisher: Dr. Hut
City/Town: München
ISBN: 978-3-8439-4647-6
URI: https://www.dr.hut-verlag.de/978-3-8439-4647-6.html
The amount of driver assistance, entertainment, and
information features in automobiles is increasing rapidly over the past few
years. Human machine interfaces are the means to communicate between car and
passengers. With the regard of automated testing of these interfaces, a typical
scenario in the development of automotive infotainment systems is revealed.
Conventional approaches for testing, based on empirical thresholds, however,
are not able to provide reliable detection rates and require a lot of maintenance.
This is where model-based testing approaches come into play. By applying
model-based approaches, the acquisition of empirical thresholds is abandoned in
favor of models learned by algorithms. Relying on an underlying model, the
algorithms exhibit higher capabilities in detecting errors that approaches
based on empirical thresholds are not taking into account.
The advent of powerful machine learning approaches plays an important role in
the data-based detection of errors in infotainment systems. Conventional
approaches require prior knowledge of errors in order to develop custom-made
algorithms for their detections. As many errors pass the development stage
without being noticed due to never being observed during this time, a novel
approach for automated testing human machine interfaces is advised.
This thesis concentrates on automated testing in the context of a model-based
end-to-end approach. After introducing the main approaches and components for
evaluating and setting up an automated testing environment, a first component
for interacting with the infotainment system is provided, which could bring
human-like input patterns forward. The main part of this thesis focuses on the
detection of irregular motion in the navigation context. By applying a model-based
approach that learns the normal behavior of the system, irregularities could be
found with relative ease in comparison to stale and fixed threshold-based
approaches. Furthermore, in the domain of screen content verification, a deep
neural network approach is dedicated to detect and classify icons in a given
screen. This approach could speed up the state-of-the-art sliding window
pattern matching approach and also be more robust to design changes and noise.
All contributions provide extensive experiments on custom data sets and
demonstrate the advantage over conventional methods.
Three model-based contributions and two prototypes for screen content testing
are investigated and adapted to provide improved results over threshold-based
approaches. The applications comprise screen input testing, screen content
analysis and screen content verification. All methods are superior to their
respective reference methods in terms of detection rates, execution time and
required maintenance, thus providing benefit in automated test environments.
APA:
Pöllot, M. (2020). Contributions to Visual Automotive Human Machine Interface Testing (Dissertation).
MLA:
Pöllot, Martin. Contributions to Visual Automotive Human Machine Interface Testing. Dissertation, München: Dr. Hut, 2020.
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