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Forward-looking automated cooperative longitudinal control: Extending cooperative adaptive cruise control (CACC) with column-wide reach and automated network quality assessment

: Schäufele, Bernd; Sawade, Oliver; Pfahl, Dennis; Massow, Kay; Bunk, Sebastian; Henke, Birgit; Radusch, Ilja

Postprint urn:nbn:de:0011-n-4876462 (936 KByte PDF)
MD5 Fingerprint: c6f42f292f76a28921f75621d2bb3ea9
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Erstellt am: 27.3.2018

Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
IEEE 20th International Conference on Intelligent Transportation Systems, ITSC 2017 : 16-19 October 2017, Yokohama, Japan
Piscataway, NJ: IEEE, 2017
ISBN: 978-1-5386-1526-3
ISBN: 978-1-5386-1525-6
ISBN: 978-1-5386-1527-0
6 S.
International Conference on Intelligent Transportation Systems (ITSC) <20, 2017, Yokohama>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer FOKUS ()

Cooperative automated Driver Assistance Systems (CoDAS) are a novel category of advanced driver assistance systems (ADAS), taking into account information received over wireless transmission from other vehicles and roadside infrastructure to enable or improve automated vehicle guidance and control. Research in cooperative longitudinal control so far focusses on improved distance control to a leading cooperative vehicle, often referred to as cooperative adaptive cruise control (CACC). In this work we expand on the concept of cooperative longitudinal control by introducing multi-object control planning, taking into account not only the leading vehicle, but also shared situational knowledge between vehicles and infrastructure. Thus, our system is able to track vehicles directly ahead either from direct cooperation or from transmitted knowledge and can adapt longitudinal control appropriately. We utilize the Collaborative Maneuver Protocol (CMP) to extend vehicle knowledge and estimate network quality. We have evaluated the function in various scenarios in the PHABMACS simulator and discuss effects of transmission quality on control parameters.