cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7348959
title: ES-PT, Remote Driving, Automated shuttle remote driving across borders

description: Use Case Category: <strong>Remote Driving</strong><br> User Story: <strong>Automated shuttle remote driving across borders</strong><br> Location: Spain-Portugal (ES-PT) cross-border corridor According to 3GPP TS 22.186 R16, Remote Driving “enables a remote driver or a V2X application to operate a remote vehicle for those passengers who cannot drive themselves or a remote vehicle located in dangerous environments. For a case where variation is limited, and routes are predictable, such as public transportation, driving based on cloud computing can be used. In addition, access to cloud-based back-end service platform can be considered for this use case group”. User Story: <strong>Automated shuttle remote driving across borders</strong> This use case is focussed in the deployment of last mile EV Automated shuttle vehicles in different environments: · Cross Border environment: The shuttle will cover a route between Spain and Portugal connecting the cities of Tui and Valença through the old international bridge. · Urban environment: The shuttle will cover a route in the city of Vigo. For both environments, we will consider these two scenarios: · Scenario 1: Cooperative automated operation: In this scenario, the EV Autonomous shuttle is able to receive information coming from other actors (like a Vulnerable Road User) and adapt its behaviour according to specific needs. · Scenario 2: Remote Control: In this scenario the EV autonomous vehicle is driving following a predefined route, and suddenly an obstacle appears in its path blocking the original route. In this situation, an operator is alarmed, and he/she is able to remotely take the control of the EV autonomous vehicle or issue a set of new navigation commands in order to handle a new route.
DOI: 10.5281/zenodo.7348881
title: CN, Vehicles Platooning, Cloud-assisted platooning

description: Use Case Category: <strong>Vehicles Platooning</strong><br> User Story: <strong>Cloud-assisted platooning</strong><br> Location: Chinese (CN) trial site According to 3GPP TS 22.186 R16, Vehicles Platooning “enables the vehicles to dynamically form a group travelling together. All the vehicles in the platoon receive periodic data from the leading vehicle, in order to carry on platoon operations. This information allows the distance between vehicles to become extremely small, i.e., the gap distance translated to time can be very low (sub second). Platooning applications may allow the vehicles following to be autonomously driven”. User Story: <strong>Cloud-assisted platooning</strong> The autonomous driving vehicle fleet communicates with each other through LTE-V at the start. Among them, the leading vehicle includes the platoon control unit (PCU), which coordinates the vehicles in the fleet to ensure a certain safe distance and to drive in a platoon. The leading vehicle communicates with the control centre deployed in a cloud server through V2N to obtain the test scheme and the global path planning. Then it provides the basic planning for the rear vehicle through V2V communication (including chasing, continuous running, acceleration, deceleration, obstacle avoidance, overall acceleration, and deceleration, etc.). The following vehicle also has a certain perception and planning decision-making ability. Besides, LTE-V communication can be replaced by DSRC technology, and comparison between these two methods will be implemented.
DOI: 10.5281/zenodo.7348882
title: CN, Vehicles Platooning, Cloud-assisted platooning

description: Use Case Category: <strong>Vehicles Platooning</strong><br> User Story: <strong>Cloud-assisted platooning</strong><br> Location: Chinese (CN) trial site According to 3GPP TS 22.186 R16, Vehicles Platooning “enables the vehicles to dynamically form a group travelling together. All the vehicles in the platoon receive periodic data from the leading vehicle, in order to carry on platoon operations. This information allows the distance between vehicles to become extremely small, i.e., the gap distance translated to time can be very low (sub second). Platooning applications may allow the vehicles following to be autonomously driven”. User Story: <strong>Cloud-assisted platooning</strong> The autonomous driving vehicle fleet communicates with each other through LTE-V at the start. Among them, the leading vehicle includes the platoon control unit (PCU), which coordinates the vehicles in the fleet to ensure a certain safe distance and to drive in a platoon. The leading vehicle communicates with the control centre deployed in a cloud server through V2N to obtain the test scheme and the global path planning. Then it provides the basic planning for the rear vehicle through V2V communication (including chasing, continuous running, acceleration, deceleration, obstacle avoidance, overall acceleration, and deceleration, etc.). The following vehicle also has a certain perception and planning decision-making ability. Besides, LTE-V communication can be replaced by DSRC technology, and comparison between these two methods will be implemented.
DOI: 10.5281/zenodo.7348877
title: DE, Vehicles Platooning, eRSU-assisted platooning

description: Use Case Category: <strong>Vehicles Platooning</strong><br> User Story: <strong>eRSU-assisted platooning</strong><br> Location: German (DE) trial site According to 3GPP TS 22.186 R16, Vehicles Platooning “enables the vehicles to dynamically form a group travelling together. All the vehicles in the platoon receive periodic data from the leading vehicle, in order to carry on platoon operations. This information allows the distance between vehicles to become extremely small, i.e., the gap distance translated to time can be very low (sub second). Platooning applications may allow the vehicles following to be autonomously driven”. User Story: eRSU-assisted platooning In this use case, a platoon of 3 AVs is operating along a road. The platoon approaches a truck. At 500m to a junction, the truck signals its intention to take the right turn and slows down. A local EDM instance deployed on an eRSU located at the junction, detects the truck’s changing velocity and trajectory through learned pattern recognition from local sensor data. It updates the global map and notifies the centralized traffic management & planning applications through eRSU’s upstream interfaces. The platoon leader detects a potential collision as it approaches the truck and request the EDM from the eRSU. The route planning service in the eRSU detects a possible overtaking manoeuvre due to low traffic ahead and possibly adapted green light phase for extended free flow based on information from traffic management system. Based on the information provided by the eRSU and its services, the platoon leader initiates an overtaking manoeuvre by communicating context data (location, speed, LDM) to the eRSU. Using the EDM, the route planning service in the eRSU formulates the overtaking manoeuvre and communicates the overtaking plan to the platoon leader. During the overtaking manoeuvre, the platoon enters the coverage of an eRSU of another provider. This results in eRSU handover with upstream gateway relocation from current MNO’s eNB/gNB to new MNO’s. In effect, the ITS instances and context on source eRSU must be handed over to the new eRSU. At mobile network level, this handover is carried out with handover protocol between the MNOs’ core networks. At application level, resource and service allocation are orchestrated by the centralized ITS service entities, which are made aware of mobile network and MEC provisioning.
DOI: 10.5281/zenodo.7348876
title: DE, Vehicles Platooning, eRSU-assisted platooning

description: Use Case Category: <strong>Vehicles Platooning</strong><br> User Story: <strong>eRSU-assisted platooning</strong><br> Location: German (DE) trial site According to 3GPP TS 22.186 R16, Vehicles Platooning “enables the vehicles to dynamically form a group travelling together. All the vehicles in the platoon receive periodic data from the leading vehicle, in order to carry on platoon operations. This information allows the distance between vehicles to become extremely small, i.e., the gap distance translated to time can be very low (sub second). Platooning applications may allow the vehicles following to be autonomously driven”. User Story: eRSU-assisted platooning In this use case, a platoon of 3 AVs is operating along a road. The platoon approaches a truck. At 500m to a junction, the truck signals its intention to take the right turn and slows down. A local EDM instance deployed on an eRSU located at the junction, detects the truck’s changing velocity and trajectory through learned pattern recognition from local sensor data. It updates the global map and notifies the centralized traffic management & planning applications through eRSU’s upstream interfaces. The platoon leader detects a potential collision as it approaches the truck and request the EDM from the eRSU. The route planning service in the eRSU detects a possible overtaking manoeuvre due to low traffic ahead and possibly adapted green light phase for extended free flow based on information from traffic management system. Based on the information provided by the eRSU and its services, the platoon leader initiates an overtaking manoeuvre by communicating context data (location, speed, LDM) to the eRSU. Using the EDM, the route planning service in the eRSU formulates the overtaking manoeuvre and communicates the overtaking plan to the platoon leader. During the overtaking manoeuvre, the platoon enters the coverage of an eRSU of another provider. This results in eRSU handover with upstream gateway relocation from current MNO’s eNB/gNB to new MNO’s. In effect, the ITS instances and context on source eRSU must be handed over to the new eRSU. At mobile network level, this handover is carried out with handover protocol between the MNOs’ core networks. At application level, resource and service allocation are orchestrated by the centralized ITS service entities, which are made aware of mobile network and MEC provisioning.
DOI: 10.5281/zenodo.7348375
title: CN, Advanced Driving, Cloud-assisted advanced driving

description: Use Case Category: <strong>Advanced Driving</strong><br> User Story: <strong>Cloud-assisted advanced driving</strong><br> Location: Chinese (CN) 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: Cloud-assisted advanced driving In the China site, the roadside unit, remote control centre and cloud server will monitor and control the autonomous vehicles in real time, to realize the various testes of Internet-connected applications of vehicles safely and efficiently. In the high-level abstraction of the user story, Vehicle A and Vehicle B are both running in a straight line along the right lane. Vehicle B is traveling at a constant speed in front of Vehicle A, and both vehicles keep a small distance. In the beginning, the remote-control centre (MEC) issues the overtaking order to vehicle A through the RSU. After receiving the order, Vehicle A sends the overtaking information to Vehicle B through V2V communication and receive the real-time information from Vehicle B via V2V, which includes the position, speed and heading angle of the vehicle. Thus, Vehicle A makes an automatic decision according to the information of Vehicle B. The decision-making of Vehicle A is detailed as follows: First, it changes to the left lane; then it accelerates to overtake according to the speed of Vehicle B. Finally, it changes lane back to the right lane, and sends its information to Vehicle B. In this way, these two cars complete overtaking operation. In this process, Vehicle A exchanges messages related to CoCA (Coordinated collision avoidance) through the 3GPP V2X communication service, while other vehicles transmit messages through the 3GPP communication service as well, finally they will achieve coordinated manoeuvring.
DOI: 10.5281/zenodo.7348376
title: CN, Advanced Driving, Cloud-assisted advanced driving

description: Use Case Category: <strong>Advanced Driving</strong><br> User Story: <strong>Cloud-assisted advanced driving</strong><br> Location: Chinese (CN) 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: Cloud-assisted advanced driving In the China site, the roadside unit, remote control centre and cloud server will monitor and control the autonomous vehicles in real time, to realize the various testes of Internet-connected applications of vehicles safely and efficiently. In the high-level abstraction of the user story, Vehicle A and Vehicle B are both running in a straight line along the right lane. Vehicle B is traveling at a constant speed in front of Vehicle A, and both vehicles keep a small distance. In the beginning, the remote-control centre (MEC) issues the overtaking order to vehicle A through the RSU. After receiving the order, Vehicle A sends the overtaking information to Vehicle B through V2V communication and receive the real-time information from Vehicle B via V2V, which includes the position, speed and heading angle of the vehicle. Thus, Vehicle A makes an automatic decision according to the information of Vehicle B. The decision-making of Vehicle A is detailed as follows: First, it changes to the left lane; then it accelerates to overtake according to the speed of Vehicle B. Finally, it changes lane back to the right lane, and sends its information to Vehicle B. In this way, these two cars complete overtaking operation. In this process, Vehicle A exchanges messages related to CoCA (Coordinated collision avoidance) through the 3GPP V2X communication service, while other vehicles transmit messages through the 3GPP communication service as well, finally they will achieve coordinated manoeuvring.
DOI: 10.5281/zenodo.7348335
title: FR, Advanced driving, Infrastructure-assisted

description: Use Case Category: <strong>Advanced Driving</strong><br> User Story: <strong>Infrastructure-assisted advanced driving</strong><br> 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).
DOI: 10.5281/zenodo.7348336
title: FR, Advanced driving, Infrastructure-assisted

description: Use Case Category: <strong>Advanced Driving</strong><br> User Story: <strong>Infrastructure-assisted advanced driving</strong><br> 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).
DOI: 10.5281/zenodo.7348262
title: ES-PT, Advanced driving, Complex manoeuvres: Automated Overtaking

description: Use Case Category: <strong>Advanced Driving</strong><br> User Story: <strong>Complex manoeuvres in cross-border settings</strong><br> Location: Spain-Portugal (ES-PT) cross-border corridor 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”. Scenario 2: <strong>Automated Overtaking</strong>: When an automated vehicle needs to overtake a vehicle that precedes it, additional information provided by communication technologies will drastically improve and complement the information provided by its sensor constellation. There are many situations where the dimensions of other vehicles can cover the field of view of the autonomous vehicle sensors (for example, a truck can reduce the vision of a camera, a laser or a radar sensor). Moreover, according to the route followed by the vehicle, it can happen that a highway exit or a toll is near to the point where the overtaking takes place, and a queue of vehicles can complicate the scenario reducing free space and therefore producing a less appropriate manoeuvre. Other complex scenario can appear when there are vehicles behind the automated vehicles that occludes the vision of rear sensors. Considering that we are driving on a two-lane road with a right-hand traffic regulation, this occlusion can produce that the automated vehicle is not able to perceive a vehicle driving fast in the left lane. The purpose of this use case is to extend the 360º perception layer of the automated vehicle by integrating communication capabilities in the different vehicles of the scenario and additional road sensors (e.g. traffic radars) in the infrastructure. In this way, vehicles will be able to share their positions, speeds, sizes, etc., as well as the road-side infrastructure, helping automated vehicle to understand current situation and thus take the best decision of how to proceed with the automated overtaking.