Lippert, Moritz ; Glatzki, Felix ; Winner, Hermann (2024)
Behavior-semantic scenery description (BSSD) of road networks for automated driving.
In: IEEE Access, 12
doi: 10.1109/ACCESS.2024.3379007
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
The safety approval of Highly Automated Vehicles (HAV) still faces the automotive industry with economic and legal challenges. For verification and validation, it is essential to describe the intended behavior of an HAV in the development process in order to prove safety. The demand for this behavior comes from the traffic rules which are instantiated by the present scenery around the vehicle (e.g. traffic signs or road markings). The Operational Design Domain (ODD) specifies the scenery in which an HAV may operate, but current descriptions fail to explicitly represent the associated behavioral demand of the scenery. We propose a new approach for a Behavior-Semantic Scenery Description (BSSD) in order to describe the behavior space of a present scenery. A behavior space represents the delimitation of the legally possible behavior. The BSSD explicitly links the scenery with the behavioral demand for HAV. Based on identified goals and challenges for such an approach, we derive requirements for a generic structure of the description for complete road networks. All required elements to represent the behavior space of the scenery are identified. Within real world examples, we present an instance of the BSSD integrated into the HD-map framework Lanelet2 to prove the applicability of the description. The presented approach supports development, test and operation of HAV by closing the knowledge gap of where a vehicle has to behave in which limits within an ODD.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Lippert, Moritz ; Glatzki, Felix ; Winner, Hermann |
| Art des Eintrags: | Bibliographie |
| Titel: | Behavior-semantic scenery description (BSSD) of road networks for automated driving |
| Sprache: | Englisch |
| Publikationsjahr: | 18 März 2024 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | IEEE Access |
| Jahrgang/Volume einer Zeitschrift: | 12 |
| DOI: | 10.1109/ACCESS.2024.3379007 |
| Kurzbeschreibung (Abstract): | The safety approval of Highly Automated Vehicles (HAV) still faces the automotive industry with economic and legal challenges. For verification and validation, it is essential to describe the intended behavior of an HAV in the development process in order to prove safety. The demand for this behavior comes from the traffic rules which are instantiated by the present scenery around the vehicle (e.g. traffic signs or road markings). The Operational Design Domain (ODD) specifies the scenery in which an HAV may operate, but current descriptions fail to explicitly represent the associated behavioral demand of the scenery. We propose a new approach for a Behavior-Semantic Scenery Description (BSSD) in order to describe the behavior space of a present scenery. A behavior space represents the delimitation of the legally possible behavior. The BSSD explicitly links the scenery with the behavioral demand for HAV. Based on identified goals and challenges for such an approach, we derive requirements for a generic structure of the description for complete road networks. All required elements to represent the behavior space of the scenery are identified. Within real world examples, we present an instance of the BSSD integrated into the HD-map framework Lanelet2 to prove the applicability of the description. The presented approach supports development, test and operation of HAV by closing the knowledge gap of where a vehicle has to behave in which limits within an ODD. |
| Freie Schlagworte: | automated vehicles, behavioral requirements, operational design domain, scenery description, vehicle safety |
| Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Fachgebiet Fahrzeugtechnik (FZD) |
| Hinterlegungsdatum: | 16 Okt 2024 05:35 |
| Letzte Änderung: | 16 Okt 2024 08:18 |
| PPN: | 522247288 |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
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