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1,308 results for “Vehicle”
Datasets for 'Estmating autonomous vehicle localization error using 2D Geographic Information'
<p>Datasets for 'Estmating autonomous vehicle localization error using 2D Geographic Information'</p>
Circularity of lithium-ion battery materials in electric vehicles
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Data from: Breeding Sternula antillarum (Least Terns) disturbance distances and duration of escape behaviors: pedestrians necessitate larger conservation buffers than do passing vehicles
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Using Unoccupied Aerial Vehicles (UAVs) to map and monitor changes in emergent kelp canopy after an ecological regime shift
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Using unoccupied aerial vehicles to estimate availability and group size error for aerial surveys of coastal dolphins
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Data from: PM2.5 exposure disparities persist despite strict vehicle emissions controls in California
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Data From: Emissions redistribution and environmental justice implications of California's Clean Vehicle Rebate Project
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A Comparison between Background Modelling Methods for Vehicle Segmentation in Highway Traffic Videos
<p>This dataset was used on the paper "A Comparison between Background Modelling Methods for Vehicle Segmentation in Highway Traffic Videos" for the comparison of three of the most common background modelling methods. The objective was to determine which of the models would be a better fit for the videos we had available at the time.</p> <p>Images are separated into folders, each corresponding to one of the videos used. To understand the naming convention, you can check <a href="https://arxiv.org/abs/1810.02835">the paper</a>, available at arXiv.</p>
Dreams4Cars Experimental data from Autonomous Test Vehicle
<p>The Horizon 2020 project Dreams4Cars (<a href="http://www.dreams4cars.eu">www.dreams4cars.eu</a>) has developed dream-like (offline) learning methods to be used for the development of Autonomous Driving and –more in general– as mechanisms to increase the Cognition abilities and Autonomy of robots. The purpose of dreamlike learning in Dreams4Cars is to deal with (possibly rare) dangerous events <em>synthetizing</em> correct behaviour and control without needing to experience the events, and more efficiently than via straightforward trial and errors. That is, to discover potential threats before they actually happen and prepare appropriate action strategies in advance.</p> <p>During the 3-years development process the project has collected and processed a wealth of experimental data from autonomous test vehicles. Parts of these data and advice how to use these data are made available to the public.</p> <p>The datasets and how they can be accessed is described in the attached report (project deliverable D5.5 Section 2), the datasets are provided in the ZIP-file.</p> <p><strong>Purpose of the Dataset</strong></p> <p>The data provided here have the purpose of demonstrating learning of forward models (the first building block of mental imagery and dreams). There are two sets of data: one for the lateral dynamics and another for the longitudinal dynamics. Each dataset has its own example of training of the corresponding forward model). Then following paper provides additional theoretical aspects: M. Da Lio, D. Bortoluzzi, e G. P. Rosati Papini, «Modelling longitudinal vehicle dynamics with neural networks», Vehicle System Dynamics, pagg. 1–19, lug. 2019, doi: <a href="http://10.1080/00423114.2019.1638947">10.1080/00423114.2019.1638947</a></p> <p><strong>Contacts:</strong></p> <p>Mauro Da Lio, University of Trento, <a href="mailto:mauro.dalio@unitn.it">mauro.dalio@unitn.it</a></p> <p>Elmar Berghoefer, Deutsches Forschungszentrum für Künstliche Intelligenz GmbH, Elmar.Berghoefer@dfki.de</p> <p>Mehmed Yueksel, Deutsches Forschungszentrum für Künstliche Intelligenz GmbH, Mehmed.Yueksel@dfki.de</p>
Project O2 - A Cooperative Bypassing Algorithm for Connected and Autonomous Vehicles in Mixed Traffic
<p>The dataset includes Python code and VISSIM file for the research paper "A Cooperative Bypassing Algorithm for Connected and Autonomous Vehicles in Mixed Traffic".</p> <p> </p> <p> </p>
Automotive Fleet Vehicle Data
<p>A synthetic research dataset of vehicle data across a fleet of vehicles, including a range of vehicles and drivers and driving conditions. The dataset contains a range of vehicle events, including vehicle telemetry, actuation events (engine start/stop, door lock/unlock, etc.). Vehicle instrumentation is derived from the OpenXC vehicle dynamics model.</p>
Fluorescence‐based detection of field targets using an autonomous unmanned aerial vehicle system
<p>This dataset comprises of the IDL code referenced in the 'Open Research' section of the Kaye and Pittman (2020) study 'Fluorescence‐based detection of field targets using an autonomous unmanned aerial vehicle system' published in <em>Methods in Ecology and Evolution</em> (<a href="https://doi.org/10.1111/2041-210X.13402">https://doi.org/10.1111/2041-210X.13402</a>).</p> <p>This study describes a proof‐of‐concept autonomous unmanned aerial vehicle (UAV) system that utilizes the fluorescence characteristics unique to different materials to scan and acquire targets in the field e.g. fossils, rocks and minerals, organisms and archaeological artefacts. This is possible because these targets are often highly fluorescent against lower fluorescence backgrounds and may exhibit different colours. Fluorescence is stimulated by a near‐UV laser that is projected across the ground as a horizontal line directly below the UAV. The IDL code is for laser line and colour extractions in the laser scan strip. The raw .jpeg data for the IDL code is not provided here as this depends on what target is being scanned. All image data are made available in the paper. Additional contextual information is provided in the '2 MATERIALS AND METHODS' section of the paper, especially in Figure 3.</p>
UIUC Autonomous Vehicles High Bay Lab Data
<p>ROS Bag files with various published ROS topics including LIDAR scans for SLAM.</p> <p>Data collected as part of course, CS598: Building Autonomous Vehicles in UIUC taught by Prof. David Forsyth.</p> <p>For details and usage, have a look at the GitHub repo: https://github.com/jatinarora2702/autonomous-vehicles</p>
Old Rusted Vehicle Frame on Historic Ranch
Old Rusted Vehicle Frame on the Historic Brown's Ranch South of Sierra Vista, AZ. This frame is in the grass on the west end of the property. It's easy to miss in the high grass but a gem if you can find it. From 387 Photos Source: Objaverse 1.0 / Sketchfab
Dataset: Greenhouse Gas and Noxious Emissions from Dual Fuel Diesel and Natural Gas Heavy Goods Vehicles
<p>This dataset contains the data underlying all figures of the paper entitled 'Greenhouse Gas and Noxious Emissions from Dual Fuel Diesel and Natural Gas Heavy Goods Vehicles'.</p>
Passivity-Based Control for a Micro Air Vehicle using Unit Quaternions
<p>In this paper the development and practical implementation of a Passivity-Based<br> Control (PBC) algorithm to stabilize an Unmanned Aerial Vehicle (UAV) described with unit<br> quaternions are presented. First, a mathematical model based on Euler-Lagrange formulation<br> using a logarithmic mapping in the quaternion space is introduced. Then, a new methodology:<br> a quaternion-passivity-based control is derived, which does not compute excessive and complex Partial Differential Equations (PDEs) for synthesizing the control law, making a significant advantage in comparison with other methodologies. Therefore, the control design to a system as the quad-rotor is easily solved by the proposed methodology. Another advantage is the possibility to stabilize quad-rotor full dynamics which may not be possible with classical PBC techniques. Experimental results and numerical simulations to validate our proposed scheme are presented.</p>
GODEEEP Light Duty Vehicle (LDV) Hourly Time Series Loads by County
<p>Each file contains projected light duty vehicle (LDV) load by county for a particular U.S. state, GCAM-USA scenario, and climate pathway as specified in the file name. For the full discussion of the methodology, please see <a href="https://godeeep.pnnl.gov/pubs/EV_Load_Shapes_GODEEEP_Arxiv.pdf">https://godeeep.pnnl.gov/pubs/EV_Load_Shapes_GODEEEP_Arxiv.pdf</a>. For the balancing authority level timeseries, please see <a href="https://doi.org/10.5281/zenodo.7888568">10.5281/zenodo.7888568</a>. The code used to produce this data is available at <a href="https://github.com/GODEEEP/transportation_electrification">https://github.com/GODEEEP/transportation_electrification</a>. Note that fleet sizes at the state scale are derived from the GCAM-USA scenario output. Downscaling to the county scale uses the electric vehicle penetration rates found in the appendix of <a href="https://www.pnnl.gov/sites/default/files/media/file/EV-AT-SCALE_1_IMPACTS_final.pdf">M. Kintner-Meyer, S. Davis, S. Sridhar, D. Bhatnagar, S. Mahserejian and M. Ghosal, "Electric vehicles at scale-phase I analysis: High EV adoption impacts on the western US power grid", Tech. Rep., 2020</a>. To harmonize the state scale LDV energy use from the GCAM-USA scenarios with the LDV load calculated with EV-Pro Lite, a scale factor was applied to the county level loads, so note that if the reported scale factor is much different than 1.0 there is potentially some disagreement between the load and the fleet size. This scale factor has already been applied to the loads reported in these files (but has NOT been applied to the fleet sizes).</p><h4><strong>GCAM-USA scenarios</strong></h4><p>See <a href="https://doi.org/10.5281/zenodo.7838871">10.5281/zenodo.7838871</a> and <a href="https://doi.org/10.5281/zenodo.8377778">10.5281/zenodo.8377778</a> for more details</p><ul><li>BAU_Climate - a business-as-usual scenario without IRA incentives</li><li>business_as_usual_ira_ccs_climate - a business-as-usual scenario with IRA incentives for CCS technology</li><li>NetZeroNoCCS_Climate - a scenario targeting net-zero by 2050 without IRA incentives, disallowing CCS technology</li><li>net_zero_ira_ccs_climate - scenario targeting net-zero by 2050 with IRA incentives for CCS technology</li></ul><h4><strong>Climate pathways</strong></h4><p>See <a href="https://doi.org/10.1038/s41597-023-02485-5">10.1038/s41597-023-02485-5</a> for more details</p><ul><li>rcp45cooler - historical weather patterns projected into the future with a warming signal applied commensurate with a cooler ensemble of RCP4.5 CMIP6 models</li><li>rcp85hotter - historical weather patterns projected into the future with a warming signal applied commensurate with a hotter ensemble of RCP4.5 CMIP6 models</li></ul><h4><strong>Fields in the data files:</strong></h4><ul><li>time - hourly timestamp in UTC representing the preceding hour of data</li><li>county - the county name</li><li>State - the state abbreviation for this county</li><li>FIPS - FIPS code for the county</li><li>balancing_authority - the balancing authority responsible for the load reported in this row; note that some counties span multiple balancing authorities and their load is divided between those balancing authorities proportional to the population residing within that balancing authority</li><li>load_MWh - load on the grid caused by the charging of LDVs during this hour within this county and balancing authority in megawatt hours</li><li>temperature_celsius - mean temperature within this county and balancing authority in degrees Celsius</li><li>fleet_size - number of electrified LDV cars within this county and balancing authority</li><li>daily_miles - average number of miles traveled per day per LDV within this county and balancing authority in miles/day</li><li>scale_factor - the values in the load_MWh field have been scaled by this multiplier in order to harmonize the state scale LDV loads with the GCAM-USA scenarios</li></ul><h4><strong>Changelog</strong></h4><ul><li>v1.0.1 - added fleet_size, daily_miles, and scale_factor to the output, and updated the README accordingly</li></ul><h4><strong>Acknowledgements</strong></h4><p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p><p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p>
Data for: Research and application of bag filter system for railway ballast bed coal suction vehicles
<p>The current bag filter system used by railway ballast bed coal suction vehicles for cleaning coal dust from railway tunnels has low operational efficiency and generates significant volumes of dust. This paper describes a simulation test unit designed to enhance the dust removal performance in railway tunnels. The flow field inside the simulation test unit is investigated under different operating conditions through numerical simulations, and the variations in air volume and working resistance, total dust collection efficiency, and optimal operating parameters of a pulse cleaning system are identified through a series of experiments. The numerical results show that the pulse cleaning system does not significantly affect the uniformity of the flow field distribution at the bottom of the filter cartridge during the process of operation. The experimental research indicates that the simulation test unit satisfies the design requirements, achieving an average total dust removal efficiency of 99.93%. A field application shows that the total dust mass concentration at the operator position can be reduced from 335.8 mg.m<sup>−3</sup> to 4.2 mg.m<sup>−3</sup>, effectively improving the operating environment within the tunnel.</p>
Coordinates of jungle cat vehicle collision locations and background points
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Transcriptional changes in macaques exposed to Sudan virus and treated with a vehicle controls or obeldesivir for 5 or 10 days
<p><span>Normalized Nanostring transcriptomic data (fold2-change- and Benjamini–Hochberg adjusted p-values) were exported as an .xlsx file. Groups include vehicle control (N=3), treated fatal (N=2), and treated survivor subjects administered ODV for 5 (N=3) or 10 days (N=5) compared against a pre-challenge baseline (0 DPI) at each collection timepoint. Any differentially expressed transcripts with a Benjamini-Hochberg false discovery rate (FDR) corrected p-value less than 0.05 were deemed significant. ODV, obeldesivir; DPI, days post infection.</span></p>
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.