Szczegóły publikacji
Opis bibliograficzny
Method for road occlusions handling in generic sensor models / Michał JASIŃSKI // W: MMAR 2021 [Dokument elektroniczny] : 2021 25th international conference on Methods and Models in Automation & Robotics : August 23–26, 2021, Międzyzdroje, Poland. — Wersja do Windows. — Dane tekstowe. — [Piscataway] : IEEE, cop. 2021. — e-ISBN: 978-1-7281-7380-1. — S. 179–184. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 184, Abstr. — Toż. na Dysku Flash. — e-ISBN: 978-1-7281-7379-5. — M. Jasiński - dod. afiliacja: APTIV Services Poland S. A., Kraków
Autor
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 135671 |
|---|---|
| Data dodania do BaDAP | 2021-09-13 |
| Tekst źródłowy | URL |
| DOI | 10.1109/MMAR49549.2021.9528443 |
| Rok publikacji | 2021 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
Abstract
Due to the increasing complexity of Advanced Safety systems, a strong endeavor is required to acquire realistic and real-time capable simulations, in order to enable robust and easily reproducible system verification in virtual environments. To make simulations reliable, high-fidelity sensor models are required. One of the approaches is to implement a generic sensor model that explicitly emulates the output of an object detection algorithm, based on high-level simulation data. However, such a model has to accurately handle object-based occlusions, to assure that shadowed objects are not detected. Various generic sensor models available in the literature already solve the problem of how to estimate occlusions, given a set of objects. Nevertheless, none of the models takes into account a road profile, i.e. hills. The method proposed in this paper provides an accurate and easy to implement road profile estimation using a set of bounding boxes. Thanks to the generated structures, a road-based shadowing can be enabled in any of the object-based generic sensor models. The obtained results clearly show the robustness and usefulness of the proposed methodology.
