AI/ML

Ontology for Scenarios for the Assessment of Automated Vehicles. (arXiv:2001.11507v1 [cs.AI])


The development of assessment methods for the performance of Automated
Vehicles (AVs) is essential to enable and speed up the deployment of automated
driving technologies, due to the complex operational domain of AVs. As
traditional methods for assessing vehicles are not applicable for AVs, other
approaches have been proposed. Among these, real-world scenario-based
assessment is widely supported by many players in the automotive field. In this
approach, test cases are derived from real-world scenarios that are obtained
from driving data.

To minimize any ambiguity regarding these test cases and scenarios, a clear
definition of the notion of scenario is required. In this paper, we propose a
more concrete definition of scenario, compared to what is known to the authors
from the literature. This is achieved by proposing an ontology in which the
quantitative building blocks of a scenario are defined. An example illustrates
that the presented ontology is applicable for scenario-based assessment of AVs.

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