An extension proposal for the collective perception service to avoid transformation errors and Include object predictions

peer-reviewed
Erstveröffentlichung
2021-12-17Autoren
Strohbeck, Jan
Herrmann, Martin
Müller, Johannes
Buchholz, Michael
Beitrag zu einer Konferenz
Erschienen in
2021 IEEE Vehicular Networking Conference (VNC) ; 2021 (2021). - S. 40-43. - ISBN 978-1-6654-4450-7. - eISSN 2157-9865
Link zur Originalveröffentlichung
https://dx.doi.org/10.1109/VNC52810.2021.9644655Fakultäten
Fakultät für Ingenieurwissenschaften, Informatik und PsychologieInstitutionen
Institut für Mess-, Regel- und MikrotechnikDokumentversion
Akzeptierte VersionKonferenz
2021 IEEE Vehicular Networking Conference (VNC), 2021-11-10 - 2021-11-12, Ulm
Zusammenfassung
The collective perception service, which is in progress of standardization by the European Telecommunication Standards Institute, allows to share perception information among connected vehicles and road side units and thus can increase both safety and traffic efficiency. However, based on our practical experience from our research on infrastructure support of automated vehicles on a pilot installation in real traffic, in this work, we outline some drawbacks of the existing draft when applied to real-world environments. We observe that the strict cartesian representation does not fit well with typical models used to predict the motion of vehicles in automated driving applications. In theses cases, transformations and approximations are required, which increases the uncertainty about the perceived objects. In this work, we demonstrate the effect of such transformation errors using examples and propose an extension of the standard to prevent unnecessary transformations and approximations. Additionally, we show that the collective perception service can further be enhanced by allowing the optional transmission of motion predictions of perceived objects. That is, receivers benefit from saving computation time for object predictions and from the reception of high-quality motion predictions from road side units that are more accurate due to their knowledge of local peculiarities.
EU-Projekt uulm
ICT4CART / ICT Infrastructure for Connected and Automated Road Transport / EC / H2020 / 768953
Projekt uulm
LUKAS / Verbundprojekt: LUKAS - Lokales Umfeldmodell für das kooperative, automatisierte Fahren in komplexen Verkehrssituationen; Teilvorhaben: Infrastrukturseite Datenverarbeitung und kooperative Handlungsplanung / BMWi / 19A20004F
Schlagwörter
[GND]: Autonomes Fahrzeug[LCSH]: Automated vehicles
[Freie Schlagwörter]: Collective Perception | Autonomes Fahren | Multiple Trajectory Prediction
[DDC Sachgruppe]: DDC 620 / Engineering & allied operations
Metadata
Zur LanganzeigeDOI & Zitiervorlage
Nutzen Sie bitte diesen Identifier für Zitate & Links: http://dx.doi.org/10.18725/OPARU-41868
Strohbeck, Jan et al. (2022): An extension proposal for the collective perception service to avoid transformation errors and Include object predictions. Open Access Repositorium der Universität Ulm und Technischen Hochschule Ulm. http://dx.doi.org/10.18725/OPARU-41868
Verschiedene Zitierstile >