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Article . 2019
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Transportation Research Part C Emerging Technologies
Article . 2019 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
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Mixing V2V- and non-V2V-equipped vehicles in car following

Authors: Francisco Navas; Vicente Milanés;

Mixing V2V- and non-V2V-equipped vehicles in car following

Abstract

Cooperative Adaptive Cruise Control (CACC) provides significant traffic flow improvements when a vehicle-to-vehicle (V2V) communication link exists with the preceding vehicle , but it degrades to an Adaptive Cruise Control (ACC) when this communication link is no longer available. This degradation occurs even if the information from another V2V-equipped vehicle ahead (different to the preceding one) is still available. This paper presents a novel car-following control system-Advanced Cooperative Adaptive Cruise Control (ACACC)-that benefits from the existing communication with this vehicle ahead in the string, reducing inter-vehicle gap whereas keeping string stability. The proposed control structure provides a hybrid behaviour between two CACC controllers with different time gaps according to the string position of the vehicle with the V2V communication link available. An stable hybrid behavior between both controllers is ensured through the Youla-Kucera parameterization. Simulation and real experiments show the proper behaviour of the designed control algorithm and a good performance compared to existing ACC/CACC controllers.

Country
France
Keywords

mixed traffic sit- uations, intelligent transportation systems, non-V2V-equipped vehicles, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Cooperative adaptive cruise control, V2V- equipped vehicles

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    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    60
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 1%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
60
Top 1%
Top 10%
Top 10%
Green
bronze