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ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2022
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
https://doi.org/10.5281/zenodo...
Dataset . 2022
License: CC BY
Data sources: Sygma
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NL, Advanced Driving, Cooperative Collision Avoidance

Authors: 5G-MOBIX;

NL, Advanced Driving, Cooperative Collision Avoidance

Abstract

Use Case Category: Advanced Driving User Story: Cooperative Collision Avoidance Location: Dutch (NL) trial site According to 3GPP TS 22.186 R16, Advanced Driving “enables semi-automated or fully-automated driving. Longer inter-vehicle distance is assumed. Each vehicle and/or Road Side Unit (RSU) shares data obtained from its local sensors with vehicles in proximity, thus allowing vehicles to coordinate their trajectories or manoeuvres. In addition, each vehicle shares its driving intention with vehicles in proximity. The benefits of this use case group are safer traveling, collision avoidance, and improved traffic efficiency”. User Story: Cooperative Collision Avoidance This 5G-MOBIX Service will take place at an Intelligent Intersection of the A270 Motorway - N270 Highway between Eindhoven and Helmond (NL). This site has been used for several tests and trials on Automated Driving and/or C-ITC. In 5G-Mobix the current facilities to support CCAM will be upgraded to work on a 5G mobile connectivity platform. Cooperative Collision Avoidance (CoCA) targets to solve a challenging traffic situation on the motorway/highway environment. The Vehicle A (‘ego vehicle’, a foreign registered vehicle driving on the Dutch motorway/highway network) will make itself ‘visible’ and known to other traffic participants and to the infrastructure for Edge Computing through C-ITS messaging via 5G networks, and Cellular V2X (C-V2X) communication i.e. either LTE-based or 5G NR-based V2X. The Vehicle B (‘alter vehicle’, a Dutch registered vehicle) will submit similar information of its presence, speed and direction of movement to ‘ego vehicle’ and infrastructure as described above. Hence the ‘Edge Cloud’ infrastructure facilities can perform calculations and offload from the vehicles to the infrastructure and then return the suggested manoeuvring messages. At the intersection of A270-N270 roads, the most critical path for cooperative automated driving is the left turn over the direct traffic flow that has a right-of-way for driving in highly automated mode through the intersection. The ego vehicle that is approaching the intersection must be able to clear itself safely across the motorway/highway lanes to join the main traffic flow. When deemed necessary, the ego vehicle need to safely stop autonomously before crossing the motorway/highway and start driving only when its calculated trajectory path is safe and clear for autonomous manoeuvring. For this purpose, the ego vehicle must be able to utilise both its own trajectory and timing data and information from the road side sensors as well as the alter vehicle’s precise location, intended direction and speed to avoid collision. The target in 5G-Mobix is time-critical message exchange between CAVs using 5G technologies. These technologies include: · C-V2X based on LTE or 5G NR for direct communication between vehicles and 5G networks · 5G NR enabled base stations (gNB) · 5G core technologies like Edge Computing (‘Edge Cloud ‘) and Slicing · Ultra-Reliable Low Latency Communication (uRLLC) to support time-critical communication. Currently the CAVs are capable to support direct communication between vehicles via ITS-G5 based solutions and/or 4G network-based communication for C-ITS message exchange. The ultimate solution is to use hybrid in-vehicle communication units that can communicate both directly between vehicles and over the network using 5G NR-V2X.

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selected citations
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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).
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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!
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