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1,308 results for “Vehicle”
Electric Vehicle Fast-Charging Software: Architectural Considerations Towards Trustworthiness
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Dataset for Detecting Vehicle in Pedestrian Areas version 2
<p>The Car_Truck_Bus_Pedestrian-Area_v2.zip is an updated collection featuring a greater number of images, specifically 7808 images, each in very high resolution in .jpg format. The total file size is now 290 MB. It continues to serve the purpose of detecting vehicles in pedestrian areas within urban environments and is captured at a resolution of 1280×720 (HD). Each image is approximately 58 KB in size. The image data was collected from 100 strategically placed public CCTVs in Medellín, Colombia, under various light and weather conditions, recorded every 60 seconds between 8:00 a.m. and 3:00 p.m. For each image, it is necessary to identify cars, buses, trucks, and pedestrians in particular zones, differentiated from the background. This enhanced dataset aims to train and evaluate machine learning models for urban traffic management and pedestrian safety applications. Data in this document is published under the Creative Commons Attribution 4.0 International License.</p>
EVALUATION OF ADVANCED VEHICLE AND COMMUNICATION TECHNOLOGIES THROUGH TRAFFIC MICROSIMULATION
<p>This folder contains products developed from STRIDE I-5 project.</p> <p>This project builds on a previously funded STRIDE project (D4) where a simulation extension was built using the micro simulator VISSIM to accurately represent vehicle autonomy and connectivity, and their operational and environmental effects.</p> <p>In this project, the research team expanded the functionality of the simulation extension to:</p> <p>i. Model human driven vehicles in the presence of AVs. An aggressive merging behavior model was implemented in VISSIM to study potential queue-jumping behavior at a freeway on-ramp. The research team also considered implementing this model in the open-source simulator SUMO.</p> <p>ii. Consider advanced vehicle dynamics and enable users to customize driver, vehicle, operating environment, and operating mode separately.</p> <p>iii. Incorporate a real-time optimization tool (RIO) previously developed with funding from the National Science Foundation (NSF). RIO jointly optimizes vehicle trajectories and signal control by taking advantage of CAV technologies.</p> <p> </p> <p>In this project, the research team conducted the following educational activities:</p> <p>i. Developed and conducted a nation-wide survey to understand the needs of the CAV education.</p> <p>ii. Developed six instructional modules for CAV education.</p> <p>iii. Developed a training module to model CAVs in microsimulation environment.</p> <p> </p> <p>The products developed from this project are:</p> <p>i. An enhanced simulation extension that can be used as a “plug and play” solution to model CAVs and human driven vehicles in presence of CAVs with options to consider advanced vehicle dynamics and incorporate signal/trajectory optimization.</p> <p>ii. Educational modules on how CAVs impact planning, design, modeling, and analysis of transportation facilities along with a training module to model CAVs using VISSIM</p>
MC-GTA: A Synthetic Benchmark for Multi-Camera Vehicle Tracking
<p><strong>Dataset</strong></p> <p>The MC-GTA dataset is designed for multi-camera vehicle tracking (MCVT) in urban environments, crucial for city-scale traffic analysis, management, and security applications. Traditional MCVT systems face challenges due to the scarcity of annotated data necessary for training and testing deep learning-based computer vision models. To address this, the MC-GTA dataset offers a synthetic collection of urban scene images captured from the virtual environment of the Grand Theft Auto 5 (GTA) video game. This dataset features recordings from multiple cameras placed at various crossroads, with automatically generated annotations including bounding boxes and unique vehicle IDs consistent across different video sources. The dataset aims to provide a valuable benchmark for MCVT tasks, demonstrating its utility through performance evaluation with a state-of-the-art MCVT approach. Additionally, the dataset and tools for creating custom scenarios are publicly accessible at <a href="https://github.com/GaetanoV10/GT5-Vehicle-BB" target="_new" rel="noreferrer">https://github.com/GaetanoV10/GT5-Vehicle-BB</a>.<br><br></p> <p><strong>Citing the MC-GTA</strong></p> <p>The MC-GTA is released under a Creative Commons Attribution license, so please cite the MC-GTA if it is used in your work in any form.<br>Published academic papers should use the academic paper citation for our MC-GTA paper</p> <blockquote> <pre>@inproceedings{ciampi2023mc, title={Mc-gta: A synthetic benchmark for multi-camera vehicle tracking}, author={Ciampi, Luca and Messina, Nicola and Valenti, Gaetano Emanuele and Amato, Giuseppe and Falchi, Fabrizio and Gennaro, Claudio}, booktitle={International Conference on Image Analysis and Processing}, pages={316--327}, year={2023}, organization={Springer} }</pre> </blockquote> <p>Personal works, such as machine learning projects/blog posts, should provide a URL to the MC-GTA<strong> </strong>Zenodo page (<a href="https://doi.org/10.5281/zenodo.5996890">https://doi.org/10.5281/zenodo.5996890</a>), though a reference to our MC-GTA paper would also be appreciated.</p> <p><strong>Contact Information</strong></p> <p>If you would like further information about the MC-GTA<strong> </strong>or if you experience any issues downloading files, please contact us at luca.ciampi[at]isti.cnr.it</p> <p><strong>Acknowledgements</strong></p> <p>Supported by: MOST - Sustainable Mobility National Research Center, funded by the European Union Next-GenerationEU (Piano Nazionale di Ripresa E Resilienza (PNRR) - Missione 4 Componente 2, Investimento 1.4 - D.D. 1033 17/06/2022, CN00000023); AI4Media – A European Excellence Centre for Media, Society, and Democracy (EC, H2020 No. 951911); SUN – Social and hUman ceNtered XR (EC, Horizon Europe No. 101092612).</p>
[5G-IANA] UC1 - LiDAR information from the vehicle
<p> 360-degree LiDAR distance measurements from the vehicle.</p>
Dataset for "Learning Scene Semantics from Vehicle-centric Data for City-scale Digital Twins", Fürntratt et al.
<p>Dataset for "Learning Scene Semantics from Vehicle-centric Data for City-scale Digital Twins", Fürntratt et al.</p> <p>Data are anonymized and provided with segmentation mask ground truths. </p>
Analysis of public views of the motor vehicle financing services information system
<p><span>The survey method was used to collect quantitative data from a sample of the public through a questionnaire. The questionnaire method was used to collect data from a larger sample of the public through a questionnaire. The population of this study comprised the people of Jakarta who use motor vehicles. The population size was estimated to be 20,049,595 people, based on data from the Badan Pusat Statistik and Indonesiadata.id. The minimum sample size for this study was 399 respondents. The respondents were selected from among those who use motor vehicles and those who use motor vehicle financing services.</span></p>
FIGURE 7 in Vehicle-mounted net sampling of airborne micro-Heteroptera in western Liberia, West Africa: 1. Isometopinae (Miridae)
FIGURE 7. Ptisca liberiense sp. nov. male. A—habitus; B—head, thorax dorsad; C—head, frontal view; D—head ventrad, antennal sockets arrowed; E—head, laterad; F—hind femur; G—recessed hind femoral trichobothria, arrowed; H—parameres and aedeagus. Scales: A = 2 mm; B–E = 0.5 mm; F = 0.2 mm; H = 0.1 mm.
FIGURE 13 in Vehicle-mounted net sampling of airborne micro-Heteroptera in western Liberia, West Africa: 1. Isometopinae (Miridae)
FIGURE 13. Myiomma goellneri sp. nov. male. A—habitus; B—head, pronotum dorsad; C—head, frontal view; D—head, thorax laterad; E—parameres and aedeagus. Scales: A = 1 mm; B–E = 0.3 mm; F = 0.05 mm.
FIGURE 9 in Vehicle-mounted net sampling of airborne micro-Heteroptera in western Liberia, West Africa: 1. Isometopinae (Miridae)
FIGURE 9. Myiomma albostiolata sp. nov. male. A—habitus, ventrad; B—head, dorsad; C—head, frontal view; Dpronotum and scutellum; E—head, thorax laterad; F—right fore wing; G—parameres and aedeagus. Scales: A = 1 mm; B–E = 0.3 mm; F = 0.5 mm; G = 0.05 mm.
FIGURE 1 in Vehicle-mounted net sampling of airborne micro-Heteroptera in western Liberia, West Africa: 1. Isometopinae (Miridae)
FIGURE 1. Sketch map of the study area in Bong county, Liberia, west Africa. LRU: "Liberia Research Unit".
FIGURE 4. Isometopus slateri male. A in Vehicle-mounted net sampling of airborne micro-Heteroptera in western Liberia, West Africa: 1. Isometopinae (Miridae)
FIGURE 4. Isometopus slateri male. A—habitus; B—head, frontal view; C—head dorsad; D—head, laterad; E—parameres; F—aedeagus; G—antenna. Scales: A = 1 mm; B–D, G = 0.5 mm; E–F = 0.1 mm.
FIGURE 3 in Vehicle-mounted net sampling of airborne micro-Heteroptera in western Liberia, West Africa: 1. Isometopinae (Miridae)
FIGURE 3. Isometopus bongensis sp. nov. male. A—head, frontal view; B—head dorsad; C—head, thorax and antenna, laterad; D—antenna III+IV; E—habitus; F—parameres; G—aedeagus. Scales: A–D = 0.5 mm; E = 1 mm; F–G = 0.1 mm.
FIGURE 10 in Vehicle-mounted net sampling of airborne micro-Heteroptera in western Liberia, West Africa: 1. Isometopinae (Miridae)
FIGURE 10. Myiomma brunnea sp. nov. male. A—habitus; B—head, dorsad; C—head, frontal view; D—head, thorax laterad; E—antenna I–II; F—parameres and aedeagus. Scales: A = 1 mm; B–E = 0.3 mm; F = 0.05 mm.
FIGURE 12 in Vehicle-mounted net sampling of airborne micro-Heteroptera in western Liberia, West Africa: 1. Isometopinae (Miridae)
FIGURE 12. Myiomma fuscipes sp. nov. male. A—habitus; B—head, pronotum dorsad; C—head, frontal view; D—head, thorax laterad; E—antenna; F—parameres and aedeagus. Scales: A = 1 mm; B–E = 0.3 mm; F = 0.05 mm.
FIGURE 8 in Vehicle-mounted net sampling of airborne micro-Heteroptera in western Liberia, West Africa: 1. Isometopinae (Miridae)
FIGURE 8. Bongiella nodistylis sp. nov. male. A—habitus; B—head, frontal view; C—head dorsad; D—head, laterad; Eantenna; F—apical part of hind femur with two long trichia; G—recessed hind femoral trichobothria, arrowed; H—parameres and aedeagus. Scales: A = 1 mm; B–E = 0.3 mm; H = 0.05 mm.
FIGURE 11. Myiomma cobbeni male. A in Vehicle-mounted net sampling of airborne micro-Heteroptera in western Liberia, West Africa: 1. Isometopinae (Miridae)
FIGURE 11. Myiomma cobbeni male. A—habitus, dorsad; B—head, thorax dorsad; C—head, frontal view; D—head laterad; E—habitus, ventrad; F—antenna II; G—parameres and aedeagus. Scales: A+E = 1 mm; B–D, F = 0.4 mm; G = 0.05 mm.
FIGURE 2 in Vehicle-mounted net sampling of airborne micro-Heteroptera in western Liberia, West Africa: 1. Isometopinae (Miridae)
FIGURE 2. Vehicle-mounted "car-net", also called "elephant trap" in allusion to the long grey "trunk"-tube (Photo: R. Garms, 1986).
FIGURE 15 in Vehicle-mounted net sampling of airborne micro-Heteroptera in western Liberia, West Africa: 1. Isometopinae (Miridae)
FIGURE 15. Myiomma variabilis sp. nov. male. A—habitus; B—head dorsad; C—head, frontal view; D—head, thorax laterad; E—antenna; F—parameres; G—aedeagus, inverted and everted. Scales: A = 1 mm; B–E = 0.3 mm; F–G = 0.05 mm.
FIGURE 14. A–C—Myiomma rubra male. A in Vehicle-mounted net sampling of airborne micro-Heteroptera in western Liberia, West Africa: 1. Isometopinae (Miridae)
FIGURE 14. A–C—Myiomma rubra male. A—head, antenna laterad; B—head, frontal view; C—fore wing. D–F—Myiomma rubrovenata male. D—head, antenna laterad; E—head, frontal view; F—head, thorax dorsad. Scales: A–B, D–F = 0.5 mm; C = 0.5 mm.
ScienceDex guides
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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.