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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: 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/
https://doi.org/10.5281/zenodo...
Dataset . 2022
License: CC BY
Data sources: Sygma
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FR, Advanced driving, Infrastructure-assisted

Authors: 5G-MOBIX;

FR, Advanced driving, Infrastructure-assisted

Abstract

Use Case Category: Advanced Driving User Story: Infrastructure-assisted advanced driving Location: French (FR) 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: Infrastructure-assisted advanced driving This user story deals with safe lane change manoeuvre dictated from road operator in presence on a multi-lane highway in presence of separation signs between the different lanes. The most critical factors of lane change manoeuvres comprise the following: a safe distance to the oncoming separation signs when initiating the lane change operation and a safe gap to the connected vehicle behind coming in the same direction, but on a different lane. Consequently, such a system requires the capability to manage the speed and steering in a coordinated manner, thereby minimizing collision risk with neighbouring vehicles in a hybrid environment (connected, automated, basic vehicles). In this user story, the MEC will receive two types of data: the first one is provided by the road operator’s Traffic Management Centre (TMC) in order to share up to date information about the different types and rules of the toll gates (heavy trucks, CAV, payment methods), while the second data flow will be provided by roadside sensors (such as cameras, lidar) in order to be aware of the presence of separation signs between the different lanes on the roads. The received raw data will be fused and treated in vicinity of the road entities, i.e. in the MEC, to be quickly processed. After analysing all the input data together with the vehicle related information (location, speed), the MEC will take the lane change decision, calculate the trajectory and will guide the vehicle in order to safely change the lane. MEC information is given to the automated vehicle in time, so it can start its lane change manoeuvre. The automated vehicle will also inform the connected vehicle coming behind in the same direction about its trajectory change intention to ensure a safe and efficient lane change operation. Involved vehicles will exchange their intended trajectories to coordinate their lateral (steering) and longitudinal controls (acceleration/deceleration) to ensure a smooth manoeuvre. Such a communication should be rapid and reliable enough to guarantee the safety of both vehicles (URLLC service of 5G networks).

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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!
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