Cooperative Maneuver Planning for Mixed Traffic at Unsignalized Intersections Using Probabilistic Predictions

CoopPlanning.mp4 (6.392Mb)
Accompanying video for the publication "Cooperative Maneuver Planning for Mixed Traffic at Unsignalized Intersections Using Probabilistic Predictions". It shows three example sequences of our cooperative planning at the two considered intersections simulated in SUMO, given different cooperative vehicle penetration rates. It gives a qualitative overview of the advantages of our approach and summarizes the quantitative gains in decreased time loss and increased traffic throughput compared to a no cooperation baseline.
Accompanying video for the publication "Cooperative Maneuver Planning for Mixed Traffic at Unsignalized Intersections Using Probabilistic Predictions". It shows three example sequences of our cooperative planning at the two considered intersections simulated in SUMO, given different cooperative vehicle penetration rates. It gives a qualitative overview of the advantages of our approach and summarizes the quantitative gains in decreased time loss and increased traffic throughput compared to a no cooperation baseline.
peer-reviewed
Erstveröffentlichung
2022-07-19Autoren
Mertens, Max Bastian
Müller, Johannes
Buchholz, Michael
Beitrag zu einer Konferenz
Erschienen in
2022 IEEE Intelligent Vehicles Symposium (IV) ; 4 (2022). - S. 1174-1180. - ISBN 978-1-6654-8821-1, ISBN 978-1-6654-8822-8
Link zur Originalveröffentlichung
https://dx.doi.org/10.1109/IV51971.2022.9827300Fakultäten
Fakultät für Ingenieurwissenschaften, Informatik und PsychologieInstitutionen
Institut für Mess-, Regel- und MikrotechnikDokumentversion
Akzeptierte VersionKonferenz
IEEE Intelligent Vehicles Symposium (IV), 2022-06-05 - 2022-06-09, Aachen, Germany
Zusammenfassung
Intersections are among the scenarios that are most crucial for efficiency and traffic flow on roads. Several approaches to traffic control at intersections exist, each with its own advantages and drawbacks. These days, wireless connections between road users, automated vehicles, and intelligent infrastructure enable new ways of coordinating traffic. However, the gradual deployment of those advanced technologies leads to a heterogeneous mixture of partially automated, connected, and legacy vehicles. Planning and coordinating maneuvers for this mixed traffic is a challenge and subject to current research, as it can achieve significant efficiency improvements in those scenarios. In this paper, we propose a new maneuver planning system for cooperative connected vehicles in mixed traffic at unsignalized intersections, which often occur in urban areas. Our system consists of a probabilistic multi-modal prediction based on a driver model and an efficient optimization algorithm to find the best maneuvers. We present the functionality of our approach and evaluate the impact on traffic efficiency using simulations of two different intersection layouts at various rates of cooperative vehicle penetration.
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]: Car-to-Car-Kommunikation[LCSH]: Automated vehicles
[Freie Schlagwörter]: Kooperatives Fahren | Vernetzte Fahrzeuge | Vehicle-to-infrastructure
[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-45416
Mertens, Max Bastian; Müller, Johannes; Buchholz, Michael (2022): Cooperative Maneuver Planning for Mixed Traffic at Unsignalized Intersections Using Probabilistic Predictions. Open Access Repositorium der Universität Ulm und Technischen Hochschule Ulm. http://dx.doi.org/10.18725/OPARU-45416
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