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9 results for “vehicle trajectories”
An Agent-Based Model to Predict Pedestrians Trajectories with an Autonomous Vehicle in Shared Spaces: Video Results
<p>A video illustrating the results presented in the paper: <em>"Prédhumeau M., Mancheva L., Dugdale J., and Spalanzani A. 2021. An Agent-Based Model to Predict Pedestrians Trajectories with an Autonomous Vehicle in Shared Spaces. In the Proc. of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021). IFAAMAS, Online."</em></p> <p> </p>
DATA7: A dataset that uses synthetic trajectories of vehicles and real cellular tower locations to simulate the workload of Edge nodes in the city of Pisa
<p><strong>Description</strong></p> <p>The dataset contains observations of vehicles in the range of edge nodes (cellular towers). The trajectories of vehicles are synthetically generated with <a href="https://www.eclipse.org/sumo/">SUMO</a>. The cellular tower positions have been taken from <a href="https://opencellid.org/">OpenCelliD</a>. The dataset is in the comma-separated values (CSV) format, and is around 220MB decompressed.</p> <p><br> The CSV contains the following fields:<br> * edge_id: unique identifier of the edge devices<br> * edge_lat: latitude coordinate of the edge device<br> * edge_lon: longitude coordinate of the edge device<br> * time: simulation step of the observation<br> * vehicle_id: unique identifier of the vehicle<br> * vehicle_lat: latitude coordinate of the vehicle<br> * vehicle_lon: longitude coordinate of the vehicle<br> * distance: geodesic distance in meters from the vehicle and the edge device</p>
Paper data for DeepManeuver: Adversarial Test Generation for Trajectory Manipulation of Autonomous Vehicles
<p>This repo contains the study and appendix data for "DeepManeuver: Adversarial Test Generation for Trajectory Manipulation of Autonomous Vehicles". DOI 10.1109/TSE.2023.3301443.</p>
Vehicle trajectories collected from a swarm of drones in a Swiss city
<p>This is an open dataset of naturalistic vehicle trajectories that have been collected by two drones flying over the city of Pully, Switzerland. This detailed dataset includes 30 datapoints per second for each vehicle, covering three types: Car, Bus, and Truck.</p> <p><strong>How are the files organized?</strong></p> <p>The name of the file follows the format: DX_TIMEDAYZ_LOCATION</p> <ul> <li>DX - indicates the drone number, either Drone 1 or Drone 2</li> <li>TIMEDAYZ - indicates whether the dataset is collected during morning (AM), noon (NOON) or afternoon (PM) and then Z for the number of session.</li> <li>LOCATION - indicates the location that each drone was flying. As Drone 1 was only in 1 location, this value changes only for Drone 2.</li> </ul> <p><strong>How are the .csv files organized?</strong></p> <div>For each .csv file the following apply:</div> <div> <ul> <li>each row represents one datapoint</li> <li>the first column includes the unique track_id (per file)</li> <li>the second column includes the type of the specific vehicle</li> <li>the third and fourth column includes the longitude and latitude</li> <li>the fifth column includes the local time in ISO 8601 format</li> </ul> </div>
Vehicle trajectory data in simulation network
<p>The project will use vehicle trajectory data generated from simulation platform. The simulation network was built in VISSIM containing a four-leg intersection with left-turn, through, and right-turn movements. The trajectory data were generated based on various traffic demand levels. The data set contains second-by-second vehicle speed and location. Details of the data set are explained as:</p> <p>Column 1 (NO): Number (Number/Index of the vehicle)<br> Column 2 (SimSec): Simulation second (Simulation time [s]) [s]<br> Column 3 (Lane\Link\No): Lane\Link\Number (Unique number of the link or connector)<br> Column 4 (Lane\Index): Lane\Index (Unique number of the lane)<br> Column 5 (Speed): Speed (Speed at the end of the time step) [km/h]<br> Column 6 (Pos): Position (Distance on the link from the beginning of the link or connector) [m]</p>
Synthetic vehicle trajectory dataset for the metropolitan city of Los Angeles using DDTG
<p>The analysis of trajectory datasets has numerous applications ranging from urban planning to human mobility understanding, but to protect the privacy of individuals trajectory datasets are rarely released to researchers. And even when they are, they are limited in size and spatio-temporal coverage. To address these issues a number of methods for generating synthetic yet realistic trajectory datasets have been proposed. These existing methods either require a lot of complex parameters to be calibrated (simulators) or rely on existing trajectory datasets (generative models). We use our proposed, and recently published at IEEE BigData 2022 conference, Data-Driven Trajectory Generator, dubbed DDTG, to generate a synthetic vehicle trajectory dataset in the metropolitan city of Los Angeles. The dataset consists of 1.5 million trajectories spanning the first two weeks of December 2019.</p>
Vehicle trajectory and pavement behavior data
<p>The dataset includes three documents.</p> <p><strong>HDV_data_NGSIM_I_80.xlsx</strong></p> <p>The vehicle trajectory data from Next Generation SIMulation (NGSIM) dataset was collected on eastbound I-80 in the San Francisco Bay area, in Emeryville, CA, on April 13, 2005, from 4:03:56 pm to 4:08:56 pm. Including vehicle id, frame id, the total count of frames of each vehicle, global time, local position, global position, vehicle length, vehicle width, vehicle class, speed, acceleration, lane id, preceding vehicle id, following vehicle id, space headway, time headway, and time.</p> <p><strong>CAV_data_CARLA_SUMO.xml</strong></p> <p>The simulated CAV trajectory data with CARLA and SUMO, including vehicle id, position, angle, type, speed, lane id, and slope of each frame.</p> <p><strong>LTPP_data.csv</strong></p> <p>The table including 21 columns is calculated from the Long-Term Pavement Performance (LTPP) database.</p> <ul> <li>IRI The IRI value measured when age was 0. (m/km)</li> <li>Cr_Gator Area of alligator cracking in square meters. (m^2)</li> <li>Cr_Lwp Length of longitudinal cracks within the defined wheel paths in meters. (m)</li> <li>Cr_Lnwp Length of longitudinal cracks not in the defined wheel paths in meters. (m)</li> <li>Pt_A Area of patches in square meters. (m^2)</li> <li>Pt_N Number of patches in square meters. (m^2)</li> <li>Cr_Wp Length of wheelpath cracks in meters. (m)</li> <li>Cr_Gt183 Total length of transverse cracks greater than 1.83. (m)</li> <li>Rt The depth of rutting in millimeters. (mm)</li> <li>Fr Friction number between the vehicle wheel tire and the pavement</li> <li>IRI_0 The IRI value measured when age was 0. (m/km)</li> <li>Tk_Sb Layer thickness measurement for surface coarse and binder course. (in)</li> <li>Md_s Average backcalculated elastic modulus of the surface layer.(psi)</li> <li>Hydr Average measured hydraulic conductivity of the specimen. (cm/sec)</li> <li>Prcp Average monthly precipitation in millimeters. (mm)</li> <li>Fz Average freeze index. (℃/day)</li> <li>Esal Annual average ESAL (kESAL)</li> <li>Esal_q quadratic form of Kesal (kESAL^2)</li> <li>Age Time duration between new construction to roughness survey date. (year)</li> <li>Gr Mean specific gravity of asphalt cement</li> <li>Pt_Ca Coarse aggregate amount percent by total weight of aggregate in percentage. (%) </li> </ul>
Synthetic vehicle trajectory dataset for the metropolitan city of Los Angeles using DDTG
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Vehicle trajectory data in Eco-friendly Cooperative Traffic Optimization (EcoTOp) system at signalized intersections
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