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Cooperative Driving: Research on Generic Decentralized Maneuver Coordination for Connected and Automated Vehicles

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Smart Cities, Green Technologies, and Intelligent Transport Systems (VEHITS 2021, SMARTGREENS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1612))

Abstract

Maneuver coordination for Connected and Automated Vehicles (CAVs) can be enhanced by vehicle-to-everything (V2X) communication. In order to disseminate planned maneuver intentions or requests, Maneuver Coordination Messages (MCMs) are exchanged between the CAVs that enable them to negotiate and perform cooperative maneuvers. In this way, V2X communication can extend the perception range of the sensors, enhance the decision making and maneuver planning of the CAVs, as well as allow complex interactions between the vehicles. Various maneuver coordination schemes exist for specific traffic use cases. Recently, several maneuver coordination approaches have been proposed that target at generic decentralized solutions which can be applied for a wide range of use cases relying on direct vehicle-to-vehicle (V2V) communication. This paper presents such use cases and existing generic approaches for decentralized maneuver coordination. The approaches are systematically described, compared and classified considering explicit and implicit trajectory broadcast, space-time reservation, cost values, priority maneuvers and complex interactions among vehicles. Furthermore, this paper outlines open research gaps in the field and discusses future research directions.

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Notes

  1. 1.

    Published ETSI standards are available at https://www.etsi.org/standards.

  2. 2.

    This paper is an extended version of [20] that describes and compares four approaches for generic decentralized maneuver coordination with CAVs. The present paper covers two additional approaches, PriMa and CVIP that were not available at the publication of [20], as well as two updated versions of the previously published approaches ETB and STR. We have used the same type of figures as in [20] that demonstrate the use cases and extended them for the new approaches. In addition, the comparison and research gaps as well as the references are extended and updated accordingly.

  3. 3.

    https://github.com/riebl/artery.

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Acknowledgements

This work was gratefully supported by the German Science Foundation (DFG) by project KOALA 2 under number 273374642 within the priority program Cooperatively Interacting Automobiles (CoIn-Car, SPP 1835). The scenario figures in the paper were created with the illustration toolkit from the C2C-CC (URL: https://www.car-2-car.org).

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Authors and Affiliations

  1. Technische Hochschule Ingolstadt, CARISSMA Institute for Electric, COnnected, and Secure Mobility (C-ECOS), Esplande 10, Ingolstadt, Germany

    Daniel Maksimovski, Christian Facchi & Andreas Festag

  2. Fraunhofer Institute for Transportation and Infrastructure Systems IVI, Ingolstadt, Germany

    Andreas Festag

Authors
  1. Daniel Maksimovski
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  2. Christian Facchi
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  3. Andreas Festag
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Correspondence to Daniel Maksimovski .

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Editors and Affiliations

  1. Siemens, Munich, Germany

    Cornel Klein

  2. RWTH Aachen, Aachen, Germany

    Matthias Jarke

  3. Maynooth University, Maynooth, Kildare, Ireland

    Markus Helfert

  4. Universität Kaiserslautern, Kaiserslautern, Germany

    Karsten Berns

  5. Ford Research and Advanced Engineering, Dearborn, MI, USA

    Oleg Gusikhin

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Maksimovski, D., Facchi, C., Festag, A. (2022). Cooperative Driving: Research on Generic Decentralized Maneuver Coordination for Connected and Automated Vehicles. In: Klein, C., Jarke, M., Helfert, M., Berns, K., Gusikhin, O. (eds) Smart Cities, Green Technologies, and Intelligent Transport Systems. VEHITS SMARTGREENS 2021 2021. Communications in Computer and Information Science, vol 1612. Springer, Cham. https://doi.org/10.1007/978-3-031-17098-0_18

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  • DOI: https://doi.org/10.1007/978-3-031-17098-0_18

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