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  4. Cooperative Automated Driving for Bottleneck Scenarios in Mixed Traffic
 
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June 2023
Conference Paper
Title

Cooperative Automated Driving for Bottleneck Scenarios in Mixed Traffic

Abstract
Connected automated vehicles (CAV), which incorporate vehicle-to-vehicle (V2V) communication into their motion planning, are expected to provide a wide range of benefits for individual and overall traffic flow. A frequent constraint or required precondition is that compatible CAVs must already be available in traffic at high penetration rates. Achieving such penetration rates incrementally before providing ample benefits for users presents a chicken-and-egg problem that is common in connected driving development. Based on the example of a cooperative driving function for bottleneck traffic flows (e.g. at a roadblock), we illustrate how such an evolutionary, incremental introduction can be achieved under transparent assumptions and objectives. To this end, we analyze the challenge from the perspectives of automation technology, traffic flow, human factors and market, and present a principle that 1) accounts for individual requirements from each domain; 2) provides benefits for any penetration rate of compatible CAVs between 0 % and 100 % as well as upward-compatibility for expected future developments in traffic; 3) can strictly limit the negative effects of cooperation for any participant and 4) can be implemented with close-to-market technology. We discuss the technical implementation as well as the effect on traffic flow over a wide parameter spectrum for human and technical aspects.
Author(s)
Baumann, M.V.
Beyerer, Jürgen  
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
Buck, H.S.
Karlsruhe Institute of Technology -KIT-  
Deml, Barbara
Karlsruhe Institute of Technology -KIT-  
Ehrhardt, Sofie
Karlsruhe Institute of Technology -KIT-  
Frese, Christian  
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
Kleiser, Dominik  
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
Lauer, Martin
Karlsruhe Institute of Technology -KIT-  
Roschani, Masoud  
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
Ruf, Miriam  
Fraunhofer-Institut für Chemische Technologie ICT  
Stiller, Christoph
Karlsruhe Institute of Technology -KIT-  
Vortisch, Peter
Karlsruhe Institute of Technology -KIT-  
Ziehn, Jens  
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
Mainwork
34th IEEE Intelligent Vehicles Symposium, IV 2023. Proceedings  
Conference
Intelligent Vehicles Symposium 2023  
Open Access
DOI
10.1109/iv55152.2023.10186638
Additional full text version
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Language
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Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
Fraunhofer-Institut für Chemische Technologie ICT  
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