1Systems level and integration
testing are an integral part of the design and development of Automated Vehicles
(AVs). Measurement science plays a pivotal role in testing to ensure the safe
and efficient operation of AVs. This science establishes a common understanding
of the units of measurement, crucial in linking human activities. This article
describes the significance of measurement in studying interactions between key
system technologies in AVs, including AI for perception, sensing,
communications, and cybersecurity. To address the complexities of these
interactions, a novel, adaptable, and interactive framework called the System
Technology Interaction Model (STIM) is introduced. STIM considers both designed
and emergent interactions between these system technologies, allowing AV
developers to explore tailored experiments with the flexibility of filtering for
focused testing. The framework currently models system interactions statically,
not in real-time, to define potential relationships and influences during the
design phase. The novelty of this framework comes from providing a holistic
evaluation that captures testing of interactions between modules in addition to
component-level testing, while other frameworks focus on testing individual
component behaviors. It also assesses the equality of two interactions, meaning
it ensures that two interactions behave the same way for consistent results.
Moreover, the framework serves as a valuable tool for AV designers and safety
regulators to aid in establishing robust design and assessment approaches. This
work highlights the need for a common framework to thoroughly test AVs and gain
a holistic understanding of system interactions. Finally, the framework aims to
understand how to mitigate potential influences leading to AV malfunctions to
advance the development and deployment of safe and reliable Automated Vehicles.
The work focuses on level 1 and level 4 automated driving features to simplify
the work, although it can be from level 1 to level 5. Although framework
performance is inherently difficult to quantify, this framework’s performance
can be reflected through its ability to accurately capture system interactions
for improved AV design and support a broader usability among AV stakeholders. In
the future, the framework can be expanded to include additional elements, such
as infrastructure or other vehicles, to analyze information provided to AVs,
allowing experts from various domains to collaborate, create similar models,
integrate them when feasible, and model the interactions in real-time.
