6-Layer Model for a Structured Description and Categorization of Urban Traffic and Environment
Verification and validation of automated driving functions impose large challenges. Currently, scenario-based approaches are investigated in research and industry, aiming at a reduction of testing efforts by specifying safety relevant scenarios. To define those scenarios and operate in a complex rea...
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Zusammenfassung: | Verification and validation of automated driving functions impose large
challenges. Currently, scenario-based approaches are investigated in research
and industry, aiming at a reduction of testing efforts by specifying safety
relevant scenarios. To define those scenarios and operate in a complex
real-world design domain, a structured description of the environment is
needed. Within the PEGASUS research project, the 6-Layer Model (6LM) was
introduced for the description of highway scenarios. This paper refines the 6LM
and extends it to urban traffic and environment. As defined in PEGASUS, the 6LM
provides the possibility to categorize the environment and, therefore,
functions as a structured basis for subsequent scenario description. The model
enables a structured description and categorization of the general environment,
without incorporating any knowledge or anticipating any functions of actors.
Beyond that, there is a variety of other applications of the 6LM, which are
elaborated in this paper. The 6LM includes a description of the road network
and traffic guidance objects, roadside structures, temporary modifications of
the former, dynamic objects, environmental conditions and digital information.
The work at hand specifies each layer by categorizing its items. Guidelines are
formulated and explanatory examples are given to standardize the application of
the model for an objective environment description. In contrast to previous
publications, the model and its design are described in far more detail.
Finally, the holistic description of the 6LM presented includes remarks on
possible future work when expanding the concept to machine perception aspects. |
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DOI: | 10.48550/arxiv.2012.06319 |