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44 results for “manoeuvres”
Test Data of Passenger kinematics in Lane change and Lane change with Braking Manoeuvres from Ghaffari et al., 2018
<p>This data was extracted by the "authors" of this dataset from the publication</p> <p><strong>Ghaffari, G., Brolin, K., Bråse, D., Pipkorn, B., Svanberg, B., Jakobsson, L., & Davidsson, J. (2018). Passenger kinematics in Lane change and Lane change with Braking Manoeuvres using two belt configurations: standard and reversible pre-pretensioner. <em>2018 IRCOBI Conference Proceedings, </em>pp. 12-14, <a href="http://www.ircobi.org/wordpress/downloads/irc18/pdf-files/80.pdf">http://www.ircobi.org/wordpress/downloads/irc18/pdf-files/80.pdf</a>.</strong></p> <p>within the OSCCAR project and made publicly available for future validations of active Human Body Models.</p> <p><strong>If you use this data, please cite the original paper.</strong></p> <p>To use the data, a generic model of the vehicle environment is also openly available: <a href="https://openvt.eu/osccar/precrash_seat_models/safer-ahbm_2-3">https://openvt.eu/osccar/precrash_seat_models/safer-ahbm_2-3 </a></p>
Version 2 of Dataset for paper "Drivers' Manoeuvre Prediction for Safe HRI"
<p>Second version of the dataset published for "Drivers’ Manoeuvre Prediction for Safe HRI".</p> <p>Details in the README file.</p>
User Driving Recordings in Simulated Environment for Manoeuvre Anticipation
<p>This is a dataset acquired with the Euro Track Simulator 2 for the evaluation of driver intention based on face tracking and vehicular data. For each subject we collected the environment video and the face looking video plus the telemetry expressed in JSON.</p> <p>For each manoeuvre we provide the 5 seconds before. </p> <p>This dataset has been used for evaluating a technique of Domain Adversarial Recurrent Neural Network in the paper "Adaptive Driving Manoeuvre Anticipation through Domain-Adversarial Recurrent Neural Networks"</p>
Simulation Dataset: Collective anti-predator escape manoeuvres through optimal attack and avoidance strategies
<p>This is a supplementary simulation dataset to reproduce Fig. 3C,D of the manuscript "Collective anti-predator escape manoeuvres through optimal attack and avoidance strategies" by Bartashevich et al.</p> <p>The zip folder contains the following 3 files in h5 format: front attack (out_Npred1_pred_angle0.0.h5), side attack (<span>out_Npred1_pred_angle1.5707963267948966.h5), </span><span>back attack (out_Npred1_pred_angle3.141592653589793.h5).</span></p> <p>Each file has the following "keys": KeysViewHDF5 ['circ_seg', 'end', 'endD', 'end_PosVel', 'fount', 'part', 'partD', 'pavas', 'pred', 'predD', 'start', 'start_fountain', 'start_pred', 'swarm', 'swarm_pred0', 'swarm_predD'].</p> <p>The key necessary to reproduce Fig. 3C,D of the aforementioned paper is "fount" (<HDF5 dataset "fount": shape (40, 1200, 100, 8), type "<f8">). Namely, "fount" data array consists of 40 simulation runs, 1200 time points, 100 agents, and 8 metrics. The metric with index "0" depicts the value of the Euclidean distance from the agent <em>i</em> to the simulated predator. The metric with index "1" depicts the value of the position angle (theta 1 in rad) of the agent <em>i</em> relative to the simulated predator. The metric with index "2" depicts the value of the flee angle (theta 2 in rad) of the agent <em>i</em> relative to the simulated predator.</p> <p>To estimate the start and the end of the fountain evasion, one can use the following script in Python:</p> <p>import numpy as np</p> <p>m = h5py.File(filename, "r")<br><br>for key in m.keys():<br> print(key)</p> <p>fount_runs = m[key]["fount"]</p> <p>for j in range(40):<br> fnt_start[j] = np.where(fount_runs[j, 0:1200, 0:100,5]==1)[0][0] <br> fnt_end[j] = np.where(fount_runs[j, 0:1200, 0:100,5]==1)[0][-1]</p>
Empirical Dataset: Collective anti-predator escape manoeuvres through optimal attack and avoidance strategies
<h3>Description of the data and file structure</h3> <p>The files contain the source data for Fig. 1 and Fig. 3A,B of the manuscript "Collective anti-predator escape manoeuvres through optimal attack and avoidance strategies" by Bartashevich et al.</p> <h4>Files and variables</h4> <h5>File: Fountain_Fish_coordinates.zip</h5> <p><strong>Description:</strong> </p> <p>The zip file contains 30 folders, each containing information on one predator attack and respective prey evasion. Each folder is named according to the drone ID used for the filming (e.g., DJI _1, DJI _2, DJI _3) and the respective frame number (e.g., f930) from the video recording. </p> <h5>File naming</h5> <p>Each folder contains JPG and CSV files. </p> <p>The JPG files show the image from the footage at the corresponding frame indicated in the files' name (e.g., frame_001_im).</p> <p>There are 2 types of CSV files. Files with the name 'polygon.csv' contain coordinates (in pixels) of points (x, y) defining the polygon outlining the prey school at the particular frame as indicated in the files' name (e.g., frame001) and corresponding to the image in the JPG file with the same frame number. Files with the name 'sardines_and_marlin.csv' contain coordinates (in pixels) of points (x, y), defining the head (columns 1 and 2) and the dorsal fin (columns 3 and 4) of single sardine individuals (by rows), and of the respective attacking marlin: marlin's head (columns 5 and 6), marlin's dorsal fin (columns 7 and 8), and marlin's tip of the bill (columns 9 and 10). These coordinates correspond to the respective image with the same frame number.</p>
ES-PT, Extended Sensors, Complex manoeuvres: HD maps
<p>Use Case Category: <strong>Extended Sensors</strong><br> User Story: <strong>Complex manoeuvres in cross-border settings: HD Maps</strong><br> Location: Spain-Portugal (ES-PT) cross-border corridor</p> <p>According to 3GPP TS 22.186 R16, Extended Sensors “enable the exchange of raw or processed data gathered through local sensors or live video data among vehicles, RSUs, devices of pedestrians and V2X application servers. The vehicles can enhance the perception of their environment beyond what their own sensors can detect and have a more holistic view of the local situation”.</p> <p>Scenario 3: <strong>HD maps</strong>:</p> <p>This use case focusses in the capability of automated vehicles and road-side infrastructure to detect changes in the road and the HD-Map used for driving, and in sending these changes to the ITS-Centre in order to centralise and broadcast this information to the other approaching vehicles.</p> <p>Lasers, cameras and traffic radars information can be fused with D-GPS and HD-Maps data, in order to determine changes in the stored information. This information can be measured in terms of length of the event, changes in road description (number of lanes, width of the lanes), dangerousness of the situation, etc.</p> <p>Finally, obtained data is shared with the ITS-Centre in order to be stored and shared with other vehicles, ensuring the information reaches all the relevant vehicles.</p>
ES-PT, Advanced driving, Complex manoeuvres: Automated Overtaking
<p>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</p> <p>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”.</p> <p>Scenario 2: <strong>Automated Overtaking</strong>:</p> <p>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.</p> <p>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.</p> <p>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.</p> <p>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.</p>
ES-PT, Advanced driving, Complex manoeuvres: lane merge
<p>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</p> <p>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”.</p> <p>Scenario 1: <strong>Lane merge for automated vehicles</strong>:</p> <p>This scenario manages the situation where automated vehicles are in a lane merge scenario, analysing the traffic flow of the target lane. In this way, the system can detect existing vehicles including their lane position, acceleration, speed, size, etc. providing an extended perception layer, which is taken into account by the automated vehicle to determine the best merge manoeuvre according to the current situation.</p> <p>Vehicles in the lane to be merged are connected vehicles that share their vehicle data with the use of a Communication Unit with 5G capabilities and through a MEC Node. Road sensing technologies, such as traffic radars, are also used to detect the presence of vehicles in the target lane and to transmit their position and speed to the automated vehicle.</p> <p>Automated vehicle uses the Communication Unit as well in order to receive the information sent by surrounded vehicles and the road-side infrastructure, and therefore determine the status of the lane merge and the best way to operate to success in a safe and comfortable lane merge.</p>
Data from: Kinematic and hydrodynamic analyses of turning manoeuvres in penguins: Body banking and wing upstroke generate the centripetal force
<p>Penguins perform lift-based swimming by flapping their wings. Previous kinematic and hydrodynamic studies have revealed the basics of wing motion and force generation in penguins. Although these studies have focused on steady forward swimming, the mechanism of turning manoeuvres is not well understood. In this study, we examined the horizontal turning of penguins via 3D motion analysis and quasi-steady hydrodynamic analysis. Free swimming of gentoo penguins (<em>Pygoscelis papua</em>) at an aquarium was recorded, and body and wing kinematics were analysed. In addition, quasi-steady calculations of the forces generated by the wings were performed. Among the selected horizontal swimming manoeuvres, turning was distinguished from straight swimming by the body trajectory for each wingbeat. During the turns, the penguins maintained outward banking through a wingbeat cycle and utilized a ventral force during the upstroke as a centripetal force to turn. Within a single wingbeat during the turns, changes in the body heading and bearing also mainly occurred during the upstroke, while the subsequent downstroke accelerated the body forward. We also found contralateral differences in the wing motion; i.e., the inside wing of the turn became more elevated and pronated. Quasi-steady calculations of the wing force confirmed that the asymmetry of the wing motion contributes to the generation of the centripetal force during the upstroke and the forward force during the downstroke. The results of this study demonstrate that the hydrodynamic force of flapping wings, in conjunction with body banking, is actively involved in the mechanism of turning manoeuvres in penguins.</p>
Testing of a Valsalva Assist Device (VAD) in Healthy Volunteers Performing a Valsalva Manoeuvre
ClinicalTrials.gov study NCT03298880. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Kinematic and hydrodynamic analyses of turning manoeuvres in penguins: Body banking and wing upstroke generate the centripetal force
Open the record for dataset details and reuse information.
Dataset for Drivers' Manoeuvre Classification for Safe HRI
<p>Dataset for the article " Drivers' Manoeuvre Classification for Safe HRI "</p>
MBES M3 Kongsberg HF model dataset in the manoeuvring basin of Leixões harbour for the NETTAG+ project.
<div> <p>Dataset created in a real scenario enviroment for data characterization to infer if marine debris could be detected and classified in the water column. Multiple acoustic frequencies were acquired for the multiple objects. <span>We present a </span><span>novel<span> dataset with 7384 acoustic images acquired </span>at<span> different acoustic frequencies using the Kongsberg M3 High Frequency Multibeam Echosounder model. This dataset provides acoustic images </span>for detecting and classifying <span>10 </span>different types<span> of marine litter. Data collection was </span>performed <span>in the manoeuvring basin of Leixões harbour.</span></span></p> <p>The images were acquired with known navigation data, with the appropriate and well defined coordinate frames. </p> </div>
Aircraft Aerobatic Manoeuvres
<p>For the context of my master's dissertation, I collected a lot of aerobatic manoeuvres examples from volunteers and decided to publish them. Therefore, the dataset is available for the community if anyone is interested in using the data to train airplane controllers. </p> <p>Note: Not the best version for dataset download. Better version will be available in <a href="https://zenodo.org/record/6804539">here</a>.</p> <p> </p> <p>Currently unavailable.</p>
Fluid Leakage Past Tracheal Tube Cuff : Effect of Suctioning Manoeuvre
ClinicalTrials.gov study NCT01170156. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Resistive Inspiratory Manoeuvre as Airway Clearance Technique in Cystic Fibrosis
ClinicalTrials.gov study NCT02261987. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Effect of Alveolar Recruitment Manoeuvre on Optic Nerve Sheath Diameter in Laparoscopic Hysterectomies
ClinicalTrials.gov study NCT06168578. IPD Sharing: YES. Countries: 1. Publications: 3.
Effectiveness of the Epley Manoeuvre Performed in Primary Care to Treat Benign Paroxysmal Positional Vertigo
ClinicalTrials.gov study NCT01969513. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Passive Leg Raising - an Important Diagnostic Manoeuvre
ClinicalTrials.gov study NCT03454802. IPD Sharing: NO. Countries: 1. Publications: 1.
Effect of I/R Injuries by Pringle Manoeuvre on the Prognosis of HCC Patients After Curative Hepatectomy
ClinicalTrials.gov study NCT00725335. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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