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1,308 results for “Vehicle”
Source data for road transportation applications (road surface assessment, authentication of automotive vehicles)
<p>This data set records the driving using an Inertial Measurement Units of 12 different vehicles on the road infrastructure of the European Commission Joint Research Centre.</p> <p>The data set is described more in detail in the paper:</p> <p>Baldini, G.; Geib, F.; Giuliani, R. Continuous Authentication of Automotive Vehicles Using Inertial Measurement Units. <em>Sensors</em> <strong>2019</strong>, <em>19</em>, 5283.</p> <p><a href="https://doi.org/10.3390/s19235283">https://doi.org/10.3390/s19235283</a></p> <p>Please, cite this paper if you use this data set.</p>
Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, at July 11, 2017, Rio Grande do Sul, Brazil
<p>Title:</p> <p>Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, at July 11, 2017, Rio Grande do Sul, Brazil</p> <p> </p> <p>Data description:</p> <p> </p> <p>The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also <em>Drone</em>) covering an forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (<a href="https://www.sciencedirect.com/science/article/pii/S0303243419309481#bib0015">Alvares et al., 2013</a>). The rainfall is well distributed throughout the year.</p> <p> </p> <p>Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files.</p> <p> </p> <p>UAV and camera settings for the acquisition (Specifications Table):</p> <p> </p> <p><strong>Parameters</strong></p> <p><strong>Specification/value</strong></p> <p>Date (YYYYMMDD):</p> <p>20170711</p> <p>Time of day (BRT = -3)</p> <p>10h a.m.</p> <p>UAV – Drone - Camera</p> <p>Phantom 4.</p> <p>Fly high (meters above ground)</p> <p>250 m</p> <p>View angle</p> <p>90° automatic mode.</p> <p>Sky conditions</p> <p>( x ) Clear sky</p> <p>( ) Low cloud coverage (some clouds)</p> <p>( ) Completely cloudy</p> <p>Wind condition</p> <p>( x ) no wind</p> <p>( ) Low speed</p> <p>( ) High speed wind</p> <p>Approximate data acquisition duration</p> <p>16 minutes</p> <p>Total of photographs acquired</p> <p>143</p> <p>Across track coverage</p> <p>80%</p> <p>Cross-track coverage</p> <p>80%</p> <p>Fly planning software</p> <p>Pix4D Capture</p> <p> </p> <p>For more information contact: Fábio Marcelo Breunig, <a href="mailto:breunig@ufsm.br">breunig@ufsm.br</a></p> <p>An example of the mosaic is showed (Figure 2), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted.</p> <p> </p> <p>Figure 2. The capture of an orthomosaic and processing workflow</p> <p> </p> <p> </p> <p>References to the main project/publications:</p> <p> </p> <p>Breunig, Fabio Marcelo. CONESAT – Monitoring the CONESUL using remote sensing data. Project. Federal University of Santa Maria, Campus of Frederico Westphalen. Brazil. Available at: <https://www.researchgate.net/project/CONESAT-Monitoring-the-CONESUL-using-remote-sensing-data>.</p> <p>Breunig, Fabio Marcelo. Integration of multiscale remote sensing data in the precision agriculture and silviculture (in Portuguese: Integração de dados multiescala de sensoriamento remoto na agricultura e silvicultura de precisão). Project. National Council for Scientific and Technological Development (CNPq). Grant 113769/2018-0</p> <p>Breunig, Fabio Marcelo. Combination of UAV, PlanetScope, Landsat and Sentinel-2 images to precision silviculture and agriculture in a subtropical region (in Portuguese: Combinação de imagens de VANT, PlanetScope, Landsat e Sentinal-2 para a silvicultura e agricultura de precisão em uma região subtropical). Project of the National Council for Scientific and Technological Development (CNPq). Grant 305084/2020-8</p> <p> </p> <p>Acknowledgments:</p> <p>This work was supported by the National Council for Scientific and Technological Development (CNPq) (Grants 113769/2018-0, 312081/2013-8, 478085/2013-3 and, 305084/2020-8) and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (Grant 23830.388.22048.19092016).</p> <p> </p> <p>Other considerations</p> <p> </p> <p>PS. A pdf file is also attached with this description</p> <p> </p> <p>Declaration of Competing Interest</p> <p>The author declares that he has no competing interests or personal relationships that have or could be perceived to have influenced the work reported in this report.</p> <p> </p> <p>References associated:</p> <p>Breunig, Fábio Marcelo (2017, July 7). Unmanned Aerial Vehicle (UAV) data acquired over an subtropical forest area of the UFSM campus Frederico Westphalen, at July 7, 2017, Rio Grande do Sul, Brazil. Zenodo. http://doi.org/10.5281/zenodo.4327943</p> <p>Alvares, Clayton Alcarde, José Luiz Stape, Paulo Cesar Sentelhas, José Leonardo De Moraes Gonçalves, and Gerd Sparovek, ‘Köppen’s Climate Classification Map for Brazil’, <em>Meteorologische Zeitschrift</em>, 22 (2013), 711–28 <https://doi.org/10.1127/0941-2948/2013/0507></p> <p>Breunig, Fábio Marcelo (2019): UAV images acquired over the UFSM campus in Frederico Westphalen, RS, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897548</p> <p>Breunig, Fábio Marcelo (2019): UAV derived orthomosaic over the “prainha” in the municipality of Iraí, Rio Grande do Sul, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897909</p> <p>Sestari, Geovane (2019): RPAS orthomosaic over the remnant of rainforest on UFSM/IFFar campus in the municipality of Frederico Westphalen, Rio Grande do Sul, Brazil.<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.910114</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Unmanned Aerial Vehicle (UAV) data acquired over an subtropical forest area of the UFSM campus Frederico Westphalen, at July 7, 2017, Rio Grande do Sul, Brazil
<p>Title:Unmanned Aerial Vehicle (UAV) data acquired over an subtropical forest area of the UFSM campus Frederico Westphalen, at July 7, 2017, Rio Grande do Sul, Brazil</p> <p> </p> <p>Data description:</p> <p> </p> <p>The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also <em>Drone</em>) covering an forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (<a href="https://www.sciencedirect.com/science/article/pii/S0303243419309481#bib0015">Alvares et al., 2013</a>). The rainfall is well distributed throughout the year.</p> <p> </p> <p>Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files.</p> <p> </p> <p>UAV and camera settings for the acquisition (Specifications Table):</p> <p> </p> <p><strong>Parameters</strong></p> <p><strong>Specification/value</strong></p> <p>Date (YYYYMMDD):</p> <p>20170707</p> <p>Time of day (BRT = -3)</p> <p>14h a.m.</p> <p>UAV – Drone - Camera</p> <p>Phantom 4.</p> <p>Fly high (meters above ground)</p> <p>250 m</p> <p>View angle</p> <p>90° automatic mode.</p> <p>Sky conditions</p> <p>( x ) Clear sky</p> <p>( ) Low cloud coverage (some clouds)</p> <p>( ) Completely cloudy</p> <p>Wind condition</p> <p>( x ) no wind</p> <p>( ) Low speed</p> <p>( ) High-speed wind</p> <p>Approximate data acquisition duration</p> <p>16 minutes</p> <p>Total of photographs acquired</p> <p>143</p> <p>Across track coverage</p> <p>80%</p> <p>Cross-track coverage</p> <p>80%</p> <p>Fly planning software</p> <p>Pix4D Capture</p> <p> </p> <p>For more information contact: Fábio Marcelo Breunig, <a href="mailto:breunig@ufsm.br">breunig@ufsm.br</a></p> <p>An example of the mosaic is showed (Figure 2), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted.</p> <p> </p> <p>Figure 2. The capture of an orthomosaic and processing workflow</p> <p> </p> <p> </p> <p>References to the main project/publications:</p> <p> </p> <p>Breunig, Fabio Marcelo. CONESAT – Monitoring the CONESUL using remote sensing data. Project. Federal University of Santa Maria, Campus of Frederico Westphalen. Brazil. Available at: <https://www.researchgate.net/project/CONESAT-Monitoring-the-CONESUL-using-remote-sensing-data>.</p> <p>Breunig, Fabio Marcelo. Integration of multiscale remote sensing data in the precision agriculture and silviculture (in Portuguese: Integração de dados multiescala de sensoriamento remoto na agricultura e silvicultura de precisão). Project. National Council for Scientific and Technological Development (CNPq). Grant 113769/2018-0</p> <p>Breunig, Fabio Marcelo. Combination of UAV, PlanetScope, Landsat and Sentinel-2 images to precision silviculture and agriculture in a subtropical region (in Portuguese: Combinação de imagens de VANT, PlanetScope, Landsat e Sentinal-2 para a silvicultura e agricultura de precisão em uma região subtropical). Project of the National Council for Scientific and Technological Development (CNPq). Grant 305084/2020-8</p> <p> </p> <p>Acknowledgments:</p> <p>This work was supported by the National Council for Scientific and Technological Development (CNPq) (Grants 113769/2018-0, 312081/2013-8, 478085/2013-3 and, 305084/2020-8) and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (Grant 23830.388.22048.19092016).</p> <p> </p> <p>Other considerations</p> <p> </p> <p>PS. A pdf file is also attached with this description</p> <p> </p> <p>Declaration of Competing Interest</p> <p>The author declares that he has no competing interests or personal relationships that have or could be perceived to have influenced the work reported in this report.</p> <p> </p> <p>References associated:</p> <p> </p> <p>Alvares, Clayton Alcarde, José Luiz Stape, Paulo Cesar Sentelhas, José Leonardo De Moraes Gonçalves, and Gerd Sparovek, ‘Köppen’s Climate Classification Map for Brazil’, <em>Meteorologische Zeitschrift</em>, 22 (2013), 711–28 <https://doi.org/10.1127/0941-2948/2013/0507></p> <p>Breunig, Fábio Marcelo (2019): UAV images acquired over the UFSM campus in Frederico Westphalen, RS, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897548</p> <p>Breunig, Fábio Marcelo (2019): UAV derived orthomosaic over the “prainha” in the municipality of Iraí, Rio Grande do Sul, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897909</p> <p>Sestari, Geovane (2019): RPAS orthomosaic over the remnant of rainforest on UFSM/IFFar campus in the municipality of Frederico Westphalen, Rio Grande do Sul, Brazil.<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.910114</p> <p>Title:</p> <p> </p> <p>Unmanned Aerial Vehicle (UAV) data acquired over an subtropical forest area of the UFSM campus Frederico Westphalen, at July 7, 2017, Rio Grande do Sul, Brazil</p> <p> </p> <p>Data description:</p> <p> </p> <p>The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also <em>Drone</em>) covering an forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (<a href="https://www.sciencedirect.com/science/article/pii/S0303243419309481#bib0015">Alvares et al., 2013</a>). The rainfall is well distributed throughout the year.</p> <p> </p> <p>Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files.</p> <p> </p> <p>UAV and camera settings for the acquisition (Specifications Table):</p> <p> </p> <p><strong>Parameters</strong></p> <p><strong>Specification/value</strong></p> <p>Date (YYYYMMDD):</p> <p>20170707</p> <p>Time of day (BRT = -3)</p> <p>14h a.m.</p> <p>UAV – Drone - Camera</p> <p>Phantom 4.</p> <p>Fly high (meters above ground)</p> <p>250 m</p> <p>View angle</p> <p>90° automatic mode.</p> <p>Sky conditions</p> <p>( x ) Clear sky</p> <p>( ) Low cloud coverage (some clouds)</p> <p>( ) Completely cloudy</p> <p>Wind condition</p> <p>( x ) no wind</p> <p>( ) Low speed</p> <p>( ) High speed wind</p> <p>Approximate data acquisition duration</p> <p>16 minutes</p> <p>Total of photographs acquired</p> <p>143</p> <p>Across track coverage</p> <p>80%</p> <p>Cross-track coverage</p> <p>80%</p> <p>Fly planning software</p> <p>Pix4D Capture</p> <p> </p> <p>For more information contact: Fábio Marcelo Breunig, <a href="mailto:breunig@ufsm.br">breunig@ufsm.br</a></p> <p>An example of the mosaic is showed (Figure 2), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted.</p> <p> </p> <p>Figure 2. The capture of an orthomosaic and processing workflow</p> <p> </p> <p> </p> <p>References to the main project/publications:</p> <p> </p> <p>Breunig, Fabio Marcelo. CONESAT – Monitoring the CONESUL using remote sensing data. Project. Federal University of Santa Maria, Campus of Frederico Westphalen. Brazil. Available at: <https://www.researchgate.net/project/CONESAT-Monitoring-the-CONESUL-using-remote-sensing-data>.</p> <p>Breunig, Fabio Marcelo. Integration of multiscale remote sensing data in the precision agriculture and silviculture (in Portuguese: Integração de dados multiescala de sensoriamento remoto na agricultura e silvicultura de precisão). Project. National Council for Scientific and Technological Development (CNPq). Grant 113769/2018-0</p> <p>Breunig, Fabio Marcelo. Combination of UAV, PlanetScope, Landsat and Sentinel-2 images to precision silviculture and agriculture in a subtropical region (in Portuguese: Combinação de imagens de VANT, PlanetScope, Landsat e Sentinal-2 para a silvicultura e agricultura de precisão em uma região subtropical). Project of the National Council for Scientific and Technological Development (CNPq). Grant 305084/2020-8</p> <p> </p> <p>Acknowledgments:</p> <p>This work was supported by the National Council for Scientific and Technological Development (CNPq) (Grants 113769/2018-0, 312081/2013-8, 478085/2013-3 and, 305084/2020-8) and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (Grant 23830.388.22048.19092016).</p> <p> </p> <p>Other considerations</p> <p> </p> <p>PS. A pdf file is also attached with this description</p> <p> </p> <p>Declaration of Competing Interest</p> <p>The author declares that he has no competing interests or personal relationships that have or could be perceived to have influenced the work reported in this report.</p> <p> </p> <p>References associated:</p> <p> </p> <p>Alvares, Clayton Alcarde, José Luiz Stape, Paulo Cesar Sentelhas, José Leonardo De Moraes Gonçalves, and Gerd Sparovek, ‘Köppen’s Climate Classification Map for Brazil’, <em>Meteorologische Zeitschrift</em>, 22 (2013), 711–28 <https://doi.org/10.1127/0941-2948/2013/0507></p> <p>Breunig, Fábio Marcelo (2019): UAV images acquired over the UFSM campus in Frederico Westphalen, RS, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897548</p> <p>Breunig, Fábio Marcelo (2019): UAV derived orthomosaic over the “prainha” in the municipality of Iraí, Rio Grande do Sul, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897909</p> <p>Sestari, Geovane (2019): RPAS orthomosaic over the remnant of rainforest on UFSM/IFFar campus in the municipality of Frederico Westphalen, Rio Grande do Sul, Brazil.<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.910114</p>
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>
Universal safety distance alert device for road vehicles - Testing videos
<p>Testing the Universal safety distance alert device for road traffic in simulated and in real traffic.</p>
In-vehicle Sensing Datasets (e.g., GPS, IMU, and OBD data) In Florida
<p>This data collection and distribution is supported by NSF OAC-1948066. These datasets include a total of 497 trajectory datasets over 2404 km. Each dataset includes 6DOF IMU data (e.g., triaxial acceleration and gyroscope data), GPS data (e.g., latitude, longitude, altitude, speed over ground, the number of connected satellites, Course Over Ground), and OBD data (e.g., rpm, throttle positions, accelerator positions, RPM, air temperature, etc.). The data collection mechanism adopts the asynchronous sampling technologies that make capturing sensor data independent of the recorded signal. Therefore, datasets collected from each sensor are logged in separate files (e.g., time_obd.jsonl, time_gps.jsonl, time_obd.jsonl). By matching the time when each sensor module initiated to log data, one can aggregate/fuse multi-type in-vehicle sensing data.</p><p> </p>
Next-generation 3D object detection and tracking for self-driving vehicles using object velocity
<p>The synthetic dataset was generated using KITTI-like specifications and annotations format. It is comprised by the training and testing sets, that include KITTI standard folders: label_2, image_2 and calib. Furthermore, there is a velodyne file for each of the following use cases:</p><ul><li>Point cloud 1: (x,y,z, (Float)Radial_Velocity): this point cloud has the relative radial velocity as an additional feature for each point. File: velodyne_radial_velocity;</li><li>Point cloud 2: (x,y,z,(Float)Absolute_Speed): in this point cloud, every point has the absolute speed of the object as the additional feature. File: velodyne_abs_speed;</li><li>Point cloud 3: (x,y,z,(Bool)Is_Moving): the additional feature of this point cloud is a Boolean value that is set to 1.0 if the object is moving; contrariwise, it is set to 0.0 for static objects. File: velodyne_is_moving;</li><li>Point cloud 4: (x,y,z,0): no additional feature information. If desired, requires post-processing to convert to (x,y,z) or changing the toolbox point cloud configuration to not consider the additional feature. File: velodyne_xyz;</li></ul><p>Additionally, the detections generated with the OpenPCDet toolbox and Second-IoU model are provided.</p><p>This work was made as part of a master thesis of Informatics Engineering in the University of Aveiro.</p>
Optimal planning of autonomous electric vehicles charging stations with photovoltaic generations and energy storage systems
<p>This database contains technical information on the 69-bus electrical distribution system. This system was tested in a mixed integer linear programming model for allocating autonomous electric vehicle charging stations equipped with photovoltaic generation and energy storage systems. Additionally, this document contains data related to charging stations, energy storage systems, and operational scenarios applied to the case studies.</p>
Dataset: "On the influence of AVAS directivity on electric vehicle speed perception"
<p>This repository contains experiment results and calibrated stimuli recordings accompanying the publication: </p> <blockquote> <p>Leon Müller and Wolfgang Kropp, <em>On the influence of AVAS directivity on electric vehicle speed</em><br><em>perception</em>, submitted for publication in Inter-Noise 2024 proceedings</p> </blockquote> <p>The stimuli recordings were obtained by placing a calibrated artificial head (<em>HEAD Acoustics HMS-V</em>) at the participant listening position in the anechoic chamber.</p> <p>The AVAS sounds were generated using the Electric Vehicle Auralization Toolbox presented in:</p> <blockquote> <p>Müller L. & Kropp W. 2024. Auralization of electric vehicles for the perceptual evaluation of acoustic vehicle alerting systems. Acta Acustica, 8, 27. https://doi.org/10.1051/aacus/2024025</p> </blockquote> <p>The .wav files contain 32-bit float values that correspond to pressure in Pa and are named according to the following table.</p> <table> <tbody> <tr> <td><strong>AVAS Signal</strong></td> <td><strong>Directivity</strong></td> <td><strong>Vehicle Speed</strong></td> </tr> <tr> <td>T: Tonal (VW ID.3)</td> <td>B: BEM</td> <td>10: 10 km/h</td> </tr> <tr> <td>N: Noise (Tesla Model Y)</td> <td>C: Cardioid</td> <td>20: 20 km/h</td> </tr> <tr> <td> </td> <td>S: Star</td> <td> </td> </tr> <tr> <td> </td> <td>O: Omnidirectional</td> <td> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p>
US Recreational Vehicle Market 2024 to 2033
<p>According to current market research conducted by the CMI Team, the <a href="https://www.custommarketinsights.com/report/us-recreational-vehicle-market/" target="_blank" rel="noopener"><strong>US Recreational Vehicle Market</strong></a> is expected to record a CAGR of <strong>3.4%</strong> from 2024 to 2033. In 2024, the market size is projected to reach a valuation of USD <strong>2,230.1 Million</strong>. By 2033, the valuation is anticipated to reach USD <strong>3,013.1 Million</strong><strong>.</strong></p> <p>The <a href="https://www.custommarketinsights.com/report/us-recreational-vehicle-market/" target="_blank" rel="noopener">US Recreational Vehicle (RV) market </a>is a dynamic sector that comprises travel, leisure, and consolidation of lifestyle patterns. These are all influenced by changing consumer preferences to travel more conveniently and flexibly. Vehicles in this market range from motor homes and camper vehicles to towable camping trailers, each serving a unique definition of travel and price range.</p> <p>The RV industry has long been popular among senior citizens, but younger and more active customers have recently become attracted to RVs’ mobility and ease. The sector is highly innovative, as companies are developing electric and hybrid RVs due to concerns about environmental sustainability. Moreover, advancements in digital technology have improved RVs.</p> <p>Simple control systems, solar panels, and internet access while traveling have improved the RV lifestyle. With changing patterns of economic variables like consumer disposable income and fuel prices, the market also undergoes changes, making it imperative for manufacturers and dealers to continuously adjust.</p> <p>DOWNLOAD FREE SAMPLE Now at <a href="https://www.custommarketinsights.com/request-for-free-sample/?reportid=59186" target="_blank" rel="noopener">https://www.custommarketinsights.com/request-for-free-sample/?reportid=59186</a></p>
Vehicle Interior Sound Dataset
<p>The used dataset is collected from the point of view (PoV) driving of different vehicle types from YouTube ("https://www.youtube.com/," 2020). These are only vehicle interior sounds. There is no driver or any human voice.5980 sounds were recorded with 8 classes. These vehicles were driven on asphalt roads in open-air. We didn’t prefer to collect interior vehicle sounds on unpaved roads in rainy weather.</p> <p>The file format of these data is wav. The length of the used sounds is in the range of 3-5 seconds with 48 kHz frequency. The chosen vehicle types are bus, minibus, pickup, sports car, jeep, truck, crossover, and car (automobile). The attributes of the collected vehicle interior sound (VIS) dataset are summarized in Table.</p> <table align="center"> <tbody> <tr> <td> <p><strong>No</strong></p> </td> <td> <p><strong>Class name</strong></p> </td> <td> <p><strong>Number of Samples</strong></p> </td> <td> <p><strong>No</strong></p> </td> <td> <p><strong>Class name</strong></p> </td> <td> <p><strong>Number of Samples</strong></p> </td> </tr> <tr> <td> <p><strong>1</strong></p> </td> <td> <p><strong>Bus </strong></p> </td> <td> <p><strong>850</strong></p> </td> <td> <p><strong>5</strong></p> </td> <td> <p><strong>Jeep</strong></p> </td> <td> <p><strong>600</strong></p> </td> </tr> <tr> <td> <p><strong>2</strong></p> </td> <td> <p><strong>Minibus</strong></p> </td> <td> <p><strong>600</strong></p> </td> <td> <p><strong>6</strong></p> </td> <td> <p><strong>Truck</strong></p> </td> <td> <p><strong>900</strong></p> </td> </tr> <tr> <td> <p><strong>3</strong></p> </td> <td> <p><strong>Pickup</strong></p> </td> <td> <p><strong>680</strong></p> </td> <td> <p><strong>7</strong></p> </td> <td> <p><strong>Crossover</strong></p> </td> <td> <p><strong>800</strong></p> </td> </tr> <tr> <td> <p><strong>4</strong></p> </td> <td> <p><strong>Sports Car</strong></p> </td> <td> <p><strong>800</strong></p> </td> <td> <p><strong>8</strong></p> </td> <td> <p><strong>Car (C Class – 4K)</strong></p> </td> <td> <p><strong>750</strong></p> </td> </tr> <tr> <td> <p><strong>Total</strong></p> </td> <td> <p><strong>5980</strong></p> </td> </tr> </tbody> </table> <p>This dataset was used in the article given below. Researchers who want to use the DataSet should cite the specified article.</p> <p>Akbal, E., Tuncer, T., & Dogan, S. (2022). Vehicle Interior Sound Classification Based on Local Quintet Magnitude Pattern and Iterative Neighborhood Component Analysis. <em>Applied Artificial Intelligence</em>, <em>36</em>(1), 2137653.</p>
Vehicle to Vehicle Comms Using Licensed & Unlicensed Frequecies
<p>V2V communications by selecting an appropriate frequency band through the selection of available licensed and unlicensed frequency bands for vehicles.</p>
Data for "Do electric vehicles mitigate urban heat? The case of a tropical city"
<p>This dataset contains the underlying data used in the publication "Do electric vehicles mitigate urban heat? The case of a tropical city", which is under review in <em>Front. Environ. Sci. .</em></p> <p>The dataset includes two folders:</p> <p>1. <strong>data</strong> <br> Include COSMO-DCEP-BEP model inputs and output needed to reproduce the results in the manuscript (NetCDF). </p> <p>2. <strong>script</strong><br> Include post-processing scripts used to generate the figures in the manuscript (Jupiter Python 3 Notebook).</p> <p><em> </em></p>
Paired Fusion Augmented Dataset for Vehicle Extraction and Counting (Domino Dataset)
<p>This dataset try to expedite the deep learning researcher's task of a model training to extract vehicles from aerial images in an urban environment. Vehicles included in the dataset are motorcycles and cars of any type, number of wheels and color. The specific process of acquisition, enhancing, fusion and augmentation is presented. Inclusion of height of cars using a Digital Surface Model (DSM) is described and comparison of the application of a U-net segmentation model over non height and height dataset is shown.</p>
GNSS Location Verification in Connected and Autonomous Vehicles Using in-Vehicle Multimodal Sensor Data Fusion (presentation recording)
<p>Video recording of the presentation for the publication N. Souli et al., "GNSS Location Verification in Connected and Autonomous Vehicles Using in-Vehicle Multimodal Sensor Data Fusion," 2020 22nd International Conference on Transparent Optical Networks (ICTON), Bari, Italy, 2020, pp. 1-4, doi: 10.1109/ICTON51198.2020.9203087.</p>
GPS Location Spoofing Attack Detection for Enhancing the Security of Autonomous Vehicles (presentation video)
<p>Video of the presentation for the publication M. Kamal, A. Barua, C. Vitale, C. Laoudias and G. Ellinas, "GPS Location Spoofing Attack Detection for Enhancing the Security of Autonomous Vehicles," 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall), 2021, pp. 1-7, doi: 10.1109/VTC2021-Fall52928.2021.9625567.</p>
UF & UAB's Phase 2 Demonstration Study: Developing a Model to Support Transportation System Decisions considering the Experiences of Drivers of all Age Groups with Autonomous Vehicle Technology (Project A3)
<p>Enclosed you will find the data collected during our STRIDE Phase II research project (A3) and a data dictionary.</p>
A Comprehensive Solution for Securing Connected and Autonomous Vehicles (presentation video)
<p>Video recording of the online presentation for the publication M. Kamal et al., "A Comprehensive Solution for Securing Connected and Autonomous Vehicles," 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2022, pp. 790-795, doi: 10.23919/DATE54114.2022.9774594.</p>
A Dataset for Exploring Wi-Fi Network Diversity in Vehicle-to-Infrastructure Communication
<p><strong>Introduction:</strong></p> <p>This dataset contains space and time-indexed performance data for Wi-Fi communication between a moving vehicle and a set of stationary Access Points (APs). In order to allow comparisons between technologies, 3 different types of Wi-Fi are used in parallel: 800.11n, ac, and ad.</p> <p>For more information, please consult the following article: <a href="https://www.cs.vassar.edu/~rpachecomeireles/research/papers/vnc-2020.pdf"><em>Exploring Wi-Fi Network Diversity for Vehicle-to-Infrastructure Communication</em></a>, Rui Meireles, António Rodrigues, Andrei Stanciu, Ana Aguiar, Peter Steenkiste, in the 2020 IEEE Vehicular Networking Conference (VNC 2020), December 2020, <a href="https://doi.org/10.1109/VNC51378.2020.9318407">doi:10.1109/VNC51378.2020.9318407</a>. Video presentation available <a href="https://youtu.be/IREMIGV4XLc">here</a>.</p> <p><strong>Experiment description:</strong></p> <ul> <li> <p>The AP was placed at the corner of a residential area intersection while the mobile client drove a circuit around it. The mobility pattern is shown in the animated file <code>vehicle-movement.gif</code>.</p> </li> <li> <p>The data is divided into traces, gathered on different dates, using different vehicles to support the AP on the roof, as shown below:</p> </li> </ul> <table> <tbody><tr> <th>trace nr</th> <th>date</th> <th>start time</th> <th>n(n)</th> <th>n(ac)</th> <th>n(ad)</th> <th>AP vehicle</th> <th>n(clients)</th> </tr> </tbody><tbody> <tr> <td>302</td> <td>2019-08-20</td> <td>10:28:45</td> <td>3262</td> <td>2787</td> <td>423</td> <td>2001 Honda Civic sedan</td> <td>1</td> </tr> <tr> <td>303</td> <td>2019-08-20</td> <td>11:26:23</td> <td>3374</td> <td>3027</td> <td>312</td> <td>-</td> <td>2</td> </tr> <tr> <td>304</td> <td>2019-08-20</td> <td>12:39:46</td> <td>1711</td> <td>216</td> <td>14</td> <td>-</td> <td>3 (n & ac) 2 (ad)</td> </tr> <tr> <td>401</td> <td>2019-08-22</td> <td>10:19:24</td> <td>1685</td> <td>1681</td> <td>545</td> <td>2003 Peugeot Partner</td> <td>1</td> </tr> <tr> <td>402</td> <td>2019-08-22</td> <td>10:48:26</td> <td>2859</td> <td>2827</td> <td>764</td> <td>-</td> <td>2</td> </tr> <tr> <td>403</td> <td>2019-08-22</td> <td>11:39:36</td> <td>135</td> <td>135</td> <td>116</td> <td>-</td> <td>2</td> </tr> <tr> <td>404</td> <td>2019-08-22</td> <td>11:42:50</td> <td>114</td> <td>114</td> <td>53</td> <td>-</td> <td>2</td> </tr> <tr> <td>405</td> <td>2019-08-22</td> <td>11:45:07</td> <td>2019</td> <td>2019</td> <td>507</td> <td>-</td> <td>2</td> </tr> </tbody> </table> <ul> <li><strong>APs:</strong> all positioned at coordinates {lat : 41.111879, lon : -8.631146}</li> </ul> <table> <tbody><tr> <th>ap</th> <th>device</th> <th>802.11 type</th> <th>channel</th> <th>cntr. freq (MHz)</th> <th>bw (MHz)</th> </tr> </tbody><tbody> <tr> <td>unifi-003</td> <td>ubiquiti ac lite</td> <td>n</td> <td>6</td> <td>2437</td> <td>20</td> </tr> <tr> <td>unifi-001</td> <td>-</td> <td>ac</td> <td>40</td> <td>5200</td> <td>40</td> </tr> <tr> <td>tp-01</td> <td>tp-link talon ad7200*</td> <td>ad</td> <td>1</td> <td>60480</td> <td>2160</td> </tr> </tbody> </table> <p>*running tp-link's original firmware, not OpenWrt</p> <ul> <li><strong>Main clients:</strong> all positioned in the moving vehicle's roof, a vw golf mk3</li> </ul> <table> <tbody><tr> <th>802.11 type</th> <th>radio</th> <th>nr. antennas</th> <th>laptop</th> </tr> </tbody><tbody> <tr> <td>n</td> <td>csl usb 2.0 wlan Adapter 300 Mbps</td> <td>2</td> <td>m1</td> </tr> <tr> <td>ac</td> <td>tp-link archer t4uh</td> <td>2</td> <td>w4</td> </tr> <tr> <td>ad</td> <td>tp-link talon ad7200 (tp-03)</td> <td>-</td> <td>w4</td> </tr> </tbody> </table> <ul> <li><strong>Background clients:</strong> the purpose is to increase channel util.</li> </ul> <table> <tbody><tr> <th>802.11 type</th> <th>radio</th> <th>nr. antennas</th> <th>laptop</th> <th>position</th> </tr> </tbody><tbody> <tr> <td>n</td> <td>tp-link wn722n</td> <td>1</td> <td>w2</td> <td>fixed, ~2m away from AP</td> </tr> <tr> <td>n</td> <td>tp-link wn722n</td> <td>1</td> <td>w3</td> <td>''</td> </tr> <tr> <td>ac</td> <td>csl usb 2.0 wlan Adapter 300 Mbps</td> <td>2</td> <td>w2</td> <td>''</td> </tr> <tr> <td>ac</td> <td>csl usb 2.0 wlan Adapter 300 Mbps</td> <td>2</td> <td>w3</td> <td>''</td> </tr> <tr> <td>ad</td> <td>tp-link talon ad7200 (tp-04)</td> <td>-</td> <td>macbook</td> <td>stopped vehicle's roof</td> </tr> </tbody> </table> <ul> <li><strong>Monitor nodes:</strong> all positioned in the moving vehicle's roof</li> </ul> <table> <tbody><tr> <th>802.11 type</th> <th>radio</th> <th>nr. antennas</th> <th>laptop</th> </tr> </tbody><tbody> <tr> <td>n</td> <td>csl usb 2.0 wlan Adapter 300 Mbps</td> <td>2</td> <td>m1</td> </tr> <tr> <td>ac</td> <td>tp-link talon ad7200 (tp-02)</td> <td>8</td> <td>w4</td> </tr> <tr> <td>ad</td> <td>tp-link talon ad7200 (tp-02)</td> <td>-</td> <td>w4</td> </tr> </tbody> </table> <p>Dataset structure:</p> <p>All the packet captures are already digested and ready to use in the file <code>wifi-exp-log-summary.csv</code>. An explanation of the fields below:</p> <ul> <li><strong>systime</strong> : system time (1 Hz resolution) that this row refers to. All node clocks were synchronized through NTP.</li> <li><strong>traceNr</strong> : nr. of the trace the row belongs to.</li> <li><strong>lon</strong> : longitude (in degrees) reported by the receiver's GPS at <code>systime</code></li> <li><strong>lat</strong> : latitude reported by the receiver's GPS at <code>systime</code></li> <li><strong>receiverAlt</strong> : altitude (in meters) reported by the receiver's GPS at <code>systime</code></li> <li><strong>receiverX</strong> : x coordinate of the receiver's position when space is discretized as a Cartesian plane and the sender is set to be the origin of the coordinate system. The x axis corresponds to east-west (positive values are east, negative values are west). Unit is meters.</li> <li><strong>receiverY</strong> : y coordinate of the receiver's position when space is discretized as a Cartesian plane</li> <li><strong>receiverDist</strong> : distance (in meters) of receiver to ap(s)</li> <li><strong>receiverSpeed</strong> : speed (in m/s) reported by the receiver's GPS at <code>systime</code></li> <li><strong>receiverId</strong> : system-specific id for the client (in the vehicle)</li> <li><strong>senderId</strong> : system-specific id for the ap serving the client (side of the road)</li> <li><strong>isIperfOn</strong> : 1 if row's <code>systime</code> corresponds to a period where our UDP packet consumer application is known to have been running on the receiver side.</li> <li><strong>isInLap</strong> : 1 if this row's systime has been marked as being part of a time period where clients were doing laps around the APs, 0 otherwise.</li> <li><strong>rssiMean</strong> : the mean of the RSSI (Received Signal Strength Indicator) values of frames received by the client from the ap during the 1-second period systime period the row refers to.</li> <li><strong>snrMean</strong> : SNR (signal to noise ratio) retrieved from 802.11ad sectore sweep frames.</li> <li><strong>channelFreq</strong> : center frequency of the WiFi channel used, in MHz.</li> <li><strong>channelBw</strong> : bandwidth of the WiFi channel used, in MHz.</li> <li><strong>channelUtil</strong> : percentage of time the wireless medium was sensed to be busy during the 1-second period systime period the row refers to. <strong>In traces 40x, the 802.11n and ac routers didn't log channel busy time, and as such we had to approximate channel util. based on x,y coordinates and nr. of active clients.</strong></li> <li><strong>wifiType</strong> : 802.11 type (e.g., n, ac or ad).</li> <li><strong>nrClients</strong> : nr. of parallel clients operating in <code>wifiType</code> mode, on the same channel and bandwidth as <code>receiverId</code>.</li> <li><strong>dataRateMedian</strong> : the median of the bitrate values of frames received by the client from the ap during the 1-second period systime period the row refers to.</li> <li><strong>dataRateMean</strong> : the mean of the bitrate values of frames received by the client from the ap during the 1-second period systime period the row refers to.</li> <li><strong>nBytesReceived</strong> : total number of bytes received by the client from the ap during the 1-second period systime period the row refers to.</li> <li><strong>tghptConsumer</strong> : throughput reported by the UDP packet consumer application, during the 1-second period systime period the row refers to.</li> <li><strong>nRetries</strong> : nr. of WLAN-level re-transmissions on 1 second period</li> <li><strong>meanBeaconRssi</strong> : mean RSSI measured from beacons in 1 second period. nan values are filled with -100 dBm.</li> <li><strong>meanInterBeaconTime</strong> : mean interval between consecutive beacons, within 1 second period. nan values are filled with 1 sec.</li> <li><strong>nBeacons</strong> : total nr. of beacons received by client within 1 second period.</li> </ul>
Scientific Applications of Unmanned Vehicles in Svalbard
<p>This database was generated in the scope of the State of Environmental Science in Svalbard (SESS) report. <a href="https://zenodo.org/record/4293283/files/SESS2020_UAV_Svalbard.pdf?download=1">Scientific Applications of Unmanned Vehicles in Svalbard</a>. <em>SESS Report 2020</em>, Svalbard Integrated Arctic Earth Observing System. DOI: 10.5281/zenodo.4293283</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.