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1,308 results for “Vehicle”
Dead mammal walking: a month-long march by a bison (Bison bison) after an ungulate-vehicle collision
<p>Globally, ungulate-vehicle collisions (UVC) are a major human safety concern and may also represent a significant source of mortality for some ungulate populations. However, records of UVC based on counts of roadside carcasses or reports by drivers involved in these incidents are assuredly underestimated because not all ungulates struck die immediately or at the roadside or are reported by drivers or authorities. Here, we provide an observation and analysis of the movements of a GPS-collared bison (<em>Bison bison</em>) that was involved in a UVC on the Alaska Highway and died in thick boreal forest 29 days later. During that time she moved 49.7 km from where she was hit. Her daily movement rate (km/hr) and daily net displacement (km/day) were significantly greater in the 29-day period before she was struck compared to 29 days afterward. This vivid example illustrates that individuals injured in a UVC can die several weeks later and at a considerable distance from where they were initially struck. Moreover, when they eventually die it may be where their carcass would not be found or associated with a UVC. Bison are the largest land mammal in North America and perhaps more robust to some lower-impact UVC than smaller-bodied species. Even so, if not for the GPS collar on this bison, we would have never known the fate of this individual, and the carcass likely never found. Taken together, the movements and final resting place of this bison illuminate how estimates of mortality as a result of UVC can be underestimated when the animal does not die immediately and in a location where it can be found. Given our data, we further urge managers to consider roadside counts of animals killed in UVC as a minimum estimate when considering options for mitigation.</p>
Self-Learning Vehicle Detection Dataset for Urban Environments
<p>This dataset was collected as part of a research study aimed at enhancing vehicle detection algorithms through a self-learning approach tailored for urban environments. The primary objective was to minimize dependency on extensive manual labeling and improve adaptability and effectiveness in dynamic urban conditions. The study utilized urban camera infrastructures to gather real-time traffic data, focusing on a diverse range of vehicle types.</p> <p>The dataset includes images captured from traffic cameras situated at the intersection of Calle de Alcalá and Calle de Velázquez in Madrid, Spain, operated by the Madrid City Council. Data collection spanned from November 30, 2023, to December 6, 2023, covering daytime traffic between 8:30 hours and 18:00 hours. A total of 770 images were captured at approximately 5-minute intervals.</p> <p>This dataset specifically targets five vehicle types: buses, cars, motorcycles, trucks, and vans, chosen to encompass a wide range of vehicle sizes, shapes, and functionalities commonly encountered in city traffic. A subset of 134 images was manually labeled, into sets for training, validation (fine-tuning phase), and validation (self-training phase). The remaining 653 images were labeled automatically via the self-learning process proposed in the research.</p>
Study new vehicle characteristics for analysis
<p>This dataset contains a good amount of new cars that are sold in Spain, with different characteristics that allow us to make an accurate selection of the type of vehicle we want to examine, such as length, engine type, power, price, CO2 emissions level, etc.</p>
dataset figures - Assessing the Performance of Fuel Cell Electric Vehicles Using Synthetic Hydrogen Fuel - Article Energies
<p>Dataset for Table 1 - 2 and for Figure 4</p>
Spine model and datasets for "Home Energy Optimization using Vehicle-to-Home"
<p>The Spine models are simulated for the publication of "Home Energy Optimization using Vehicle-to-Home" in the journal of Open Reserach Europe.</p> <p>The file lists are explained as follows:</p> <p>"Non-commuter_spine.zip" is the zipped folder of Spine model for non-commuting household.</p> <p>"Spine_commuter.zip" is the zipped folder of Spine model for commuting household.</p> <p>"Recorded Results and Paramater Variation Graphs - ORE.xlsx" describes the figures and outputs applied for the paper.</p> <p>"PV, elec, thermal_non_commuting.zip" denotes the non-commuting model input.</p> <p>"Model_input_commuter (1).zip" denotes the commuting model input.</p> <p>"Model output_commuter (2).xlsx" denotes the commuting model output.</p> <p> </p>
Dataset: "Auditory Localization of Multiple Stationary Electric Vehicles"
<p>This repository contains data accompanying the publication "Auditory Localization of Multiple Stationary Electric Vehicles", published in the <em>Journal of the Acoustical Society of America (JASA)</em>)</p> <p>The following abbreviations are used in the filenames and data</p> <p> </p> <table> <tbody> <tr> <td>c</td> <td>Combustion Noise</td> </tr> <tr> <td>n</td> <td>Noise AVAS</td> </tr> <tr> <td>t</td> <td>Two-Tone AVAS</td> </tr> <tr> <td>m</td> <td>Multi-Tone AVAS</td> </tr> <tr> <td>le</td> <td>Localization Error in °</td> </tr> <tr> <td>lt</td> <td>Localization Time in s</td> </tr> <tr> <td>fl</td> <td>Failed Localizations in %</td> </tr> </tbody> </table> <p> </p> <p>The dataset contains:</p> <ul> <li><em>stimuli.zip</em>: Sound pressure of all evaluated stimuli as calibrated 32-bit float .wav files</li> <li><em>trials.zip</em>: Binaural sound pressure recordings of all 72 trials as calibrated 32-bit float .wav files, including parking lot background noise. These recordings were obtained by placing a HeadAcoustics HMS-V artificial head at the listening position. The files are named as <em>TrialNr_Stim1Abbreviation_Stim1PositionInDegree_Stim2Abbreviation_Stim2PositionInDegree_Stim3Abbreviation_Stim3PositionInDegree.wav</em></li> <li><em>experimentProcedure.mp4</em>: Video showcasing the experiment procedure.</li> <li><em>rawData.xlsx</em>: The unprocessed experiment data. Each row represents one individual trial.</li> <li><em>processedData.xlsx</em>: The pre-processed experiment data for each participant. Each column represents the mean localization error (le), mean localization time (lt), or percentage of failed detections (fd) for a single stimulus. E.g., <em>le_mn_m</em> is the mean localization error of the Multi-Tone AVAS, averaged for all 4 trials where Multi-Tone AVAS and Noise AVAS were played simultaneously (TrialNr. 61-64, see Table in Paper). <em>fl_ccc</em> is the percentage of failed localizations for the Combustion Noise in the 4 trials where three combustion sounds were played simultaneously. For the statistical evaluations, participants "HZPS", "TGCI" and "XKOU" have been removed as outliers.</li> </ul> <p> </p> <p>All stimuli are compliant with both UNECE R138 and US FMVSS 141 as illustrated in Figure 3 of the paper.</p>
Bluetooth Low Energy based Dataset for Smartphone localization in moving vehicles for electronic toll collection.
<p><span>The collected Dataset presents a data collection based on Bluetooth Low Energy (BLE) technology and the use of smartphones for the purposes of vehicle localization and tracking. </span><span>The system is based on a two-dimensional 8 switched-beam directive panel antennas mounted on an electronic toll collection (ETC) gateway. The multibeam antenna topology is installed on the ETC at a height of 4.7 m and </span><span>the data collected from several experiments, together with the set-up and methodology, are reported here.</span><span> The data collection can be divided into two main sections. First, the system was calibrated in an 8 x 8 meter area below the ETC. The distance between adjacent test points is 0.5 m, with a total of 100 samples measured for each test point in every 8 switched-beam panel antenna. Secondly, the results of dynamic test with vehicles passing at different speeds carrying smartphones and using BLE are detailed. As a validation of the dataset, a benchmark analysis for data visualization and localization/tracking estimation applying MUltiple SIgnal Classification and fingerprinting algorithms is included.</span></p>
Impact on energy and air quality of connected and autonomous vehicles in an urban context
<p>Despite numerous studies related to autonomous vehicles and connected vehicles (CAVs) and their impact on the economy or on traffic performance (eg, flow management, accidents), there are not many studies that relate these benefits to the environmental component. In this context, the objective of this work consisted in the integrated assessment of the impacts of CAVs on traffic performance, atmospheric emissions CO<sub>2</sub> and NO<sub>x, </sub>and air quality.</p> <p>To this end, a roundabout in the city of Aveiro was selected as a case study, and different scenarios were created: base scenario, considering the current typology of vehicles (conventional); scenario 2, considering defensive behavior CAVs; scenario 3, considering assertive behavior CAVs; and scenario 1, considering all types of vehicles mentioned above. To ensure a comprehensive analysis, all scenarios were evaluated for a period of 24 hours, corresponding to the period of the experimental campaign carried out, and a cascade of models was applied.</p> <p>First, the PTV VISSIM model was applied which allowed, configuring, calibrating and validating the network under study for an evaluation of the traffic performance. Second, the VSP model was applied to estimate atmospheric emissions, Finally, the CFD VADIS model was applied to air quality assessment.</p> <p>The results obtained allowed us to conclude that the introduction of CAVs, promotes longer travel times, especially during times of higher traffic, and an increase in emissions, mainly by the CAVs with defensive behavior. In terms of air quality, there were large differences in terms of NO<sub>2</sub> concentrations, with the CAVs promoting a degradation of air quality, especially during peak traffic hours.</p>
In-vehicle applications among Malaysians
<p>This is an online survey dataset to study user acceptance of in-vehicle applications among Malaysians. </p>
Instances and solutions for the multi-depot electric vehicle scheduling problem with the objective of minimizing the fleet size (EVSP-MD-FS)
<p>The set of instances and corresponding solutions, which were used in the computational study of the paper “Multi-depot electric vehicle scheduling in in-plant production logistics considering non-linear charging models”.</p>
Electrification of Transportation Means a Lot More Than a Lot More Electric Vehicles
<p>Energy use in 2050 from the Annual Energy Outlook 2021 published by the U.S. Department of Energy's Energy Information Agency.</p>
Autonomous Vehicle Communication Strategies Modeled in Virtual Reality
<p>We sought to better understand how autonomous vehicle (AV) communication strategies impact human road users’ perceptions and behaviors. More specifically, we explored the impact of different external human-machine interface (eHMI) designs on understanding, task load, comfort, trust, acceptance, and reaction time. To accomplish this, we created virtual reality (VR) scenarios where human participants interacted with AVs. Participants experienced biking, driving, and pedestrian simulators and were brought back after initial testing to explore acclimation and learning effects. In terms of perceptions, the presence of an eHMI was the strongest predictor of understanding, comfort, trust, and acceptance outcomes in the statistical models when controlling for all other variables. There was a clear divide between text-based eHMIs and non-text eHMIs, with text-based eHMIs reporting better perception scores and the LED Windshield reporting the worst perception scores. There were perception acclimation effects detected (most notable for task load and comfort), but they had less of an impact than the presence of an eHMI. Perception outcomes had weaker relationships with participant characteristics than with AV characteristics. While behavioral outcomes should be interpreted with caution because of low participant sample sizes, behavioral results largely mirrored perception results in that significant reductions in reaction time were observed with the presence of an eHMI (3.69 second reduction), yielding (3.16 second reduction), and acclimation (0.134 second reduction per trial). Results suggest that eHMI design, AV behavior, and acclimation are most impactful in terms of both perceptions and reaction time.</p>
Smart Battery Management System for Electric Vehicles: Selflearning Algorithms for Simultaneous State and Parameter Estimation, and Stress Detection
<p>The project proposes to develop parameter-varying SOH-coupled models for lithium-ion battery and self-learning algorithms to learn the model for simultaneous state and parameter estimation and fault detection. The traditional battery models use constant parameters, limiting their accuracy for predicting the state of the charge and health over the complete life-cycle. In practice, the battery parameters vary with the change in the state of charge and state of health. SOH-coupled models can be used to estimate the state of charge and health accurately. Further, obtaining the model parameters is also a challenging task for designing filters or observers for state estimation. A self-learning algorithm can eliminate the requirement of the model parameters. In this project, three SOH-coupled models are proposed and validated experimentally. The models are also used to design extended Kalman filters (EKF) for the state of charge, state of health, core and surface temperature, and internal resistance estimation. The results showed that the SOHcoupled models are more effective when compared to the uncoupled models in the literature. Further, it was found that EKFs based state estimation errors were within 1%. The self-learning algorithm using a two-layer neural network showed the ability to learn the models in real-time. However, the state estimation errors are higher for the self-learning scheme compared to the EKF based approaches. This is due to the limited measurement and online training schemes utilized to train neural networks. This requires further investigation in hyper-parameter tuning for implementation. Finally, a model-based fault detection scheme was proposed to detect internal thermal fault at its onset. The SOHcoupled model is reformulated to incorporate the internal resistance as a state. The EKF is used as a fault detection observer. The proposed fault detection scheme is validated using numerical simulation. It was observed that the fault detection scheme with SOH coupled electro-thermal-aging model could effectively detect a thermal fault at its incipient state.</p>
Loudness and annoyance ratings of vehicle noise
<p>Loudness and annoyance ratings of the field experiment described in Llorach, Gerard; Oetting, Dirk; Krüger, Melanie; Vormann, Matthias; Fitschen, Christina; Schulte, Michael; Hohmann, Volker; Meis, Markus (2019, September). Vehicle noise: Loudness ratings, loudness models and future experiments with audiovisual immersive simulations. In <em>INTER-NOISE and NOISE-CON Congress and Conference Proceedings</em> (Vol. 259, No. 3, pp. 6752-6759). Institute of Noise Control Engineering. <a href="https://doi.org/10.5281/zenodo.4276090">https://doi.org/10.5281/zenodo.4276090</a>,</p> <p>and of the laboratory experiment in the process of publication.</p> <p>Do not hesitate to contact the main author for more information.</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>This work received funding from the EU’s H2020 research and innovation program under the MSCA GA 675324 (ENRICH), from the Deutsche Forschungsgemeinschaft (DFG, Cluster of Excellence EXC 1077/1 “Hearing4all”, and SFB1330 Projects B1 and C4).</p>
Data from: a physics-based digital twin for model predictive control of autonomous unmanned aerial vehicle landing
<p>This paper proposes a two-level, data-driven, digital twin concept for the autonomous landing of aircraft, under some assumptions. It features a digital twin instance for model predictive control; and an innovative, real-time, digital twin prototype for fluid-structure interaction and flight dynamics to inform it. The latter digital twin is based on the linearization about a pre-designed glideslope trajectory of a high-fidelity, viscous, nonlinear computational model for flight dynamics; and its projection onto a low-dimensional approximation subspace to achieve real-time performance, while maintaining accuracy. Its main purpose is to predict in real-time, during flight, the state of an aircraft and the aerodynamic forces and moments acting on it. Unlike static lookup tables or regression-based surrogate models based on steady-state wind tunnel data, the aforementioned real-time digital twin prototype allows the digital twin instance for model predictive control to be informed by a truly dynamic flight model, rather than a less accurate set of steady-state aerodynamic force and moment data points. The paper describes in detail the construction of the proposed two-level digital twin concept and its verification by numerical simulation. It also reports on its preliminary flight validation in autonomous mode for an off-the-shelf unmanned aerial vehicle instrumented at Stanford University.</p>
Night and Day Instance Segmented Park (NDISPark) Dataset: a Collection of Images taken by Day and by Night for Vehicle Detection, Segmentation and Counting in Parking Areas
<p><strong>The Dataset</strong></p> <p>A collection of images of parking lots for <em>vehicle detection, segmentation, and counting</em>.<br> Each image is <em>manually</em> labeled with pixel-wise masks and bounding boxes localizing vehicle instances.<br> The dataset includes about 250 images depicting several parking areas describing most of the problematic situations that we can find in a real scenario: seven different cameras capture the images under various weather conditions and viewing angles. Another challenging aspect is the presence of partial occlusion patterns in many scenes such as obstacles (trees, lampposts, other cars) and shadowed cars.<br> The main peculiarity is that <em>images are taken during the day and the night</em>, showing utterly different lighting conditions.</p> <p>We suggest a three-way split (train-validation-test). The train split contains images taken during the daytime while validation and test splits include images gathered at night.<br> In line with these splits we provide some annotation files:</p> <ul> <li> <p><em>train_coco_annotations.json</em> and <em>val_coco_annotations.json</em> --> JSON files that follow the golden standard MS COCO data format (for more info see <a href="https://cocodataset.org/#format-data">https://cocodataset.org/#format-data</a>) for the training and the validation splits, respectively. All the vehicles are labeled with the COCO category<em> 'car'</em>. They are suitable for vehicle detection and instance segmentation.</p> </li> <li> <p><em>train_dot_annotations.csv</em> and <em>val_dot_annotations.csv</em> --> CSV files that contain xy coordinates of the centroids of the vehicles for the training and the validation splits, respectively. Dot annotation is commonly used for the visual counting task.</p> </li> <li> <p><em>ground_truth_test_counting.csv</em> --> CSV file that contains the number of vehicles present in each image. It is only suitable for testing vehicle counting solutions.</p> </li> </ul> <p> </p> <p><strong>Citing our work</strong></p> <p>If you found this dataset useful, please cite the following paper</p> <blockquote> <pre>@inproceedings{Ciampi_visapp_2021, doi = {10.5220/0010303401850195}, url = {https://doi.org/10.5220%2F0010303401850195}, year = 2021, publisher = {{SCITEPRESS} - Science and Technology Publications}, author = {Luca Ciampi and Carlos Santiago and Joao Costeira and Claudio Gennaro and Giuseppe Amato}, title = {Domain Adaptation for Traffic Density Estimation}, booktitle = {Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications} } </pre> </blockquote> <p>and this Zenodo Dataset</p> <blockquote> <pre>@dataset{ciampi_ndispark_6560823, author = {Luca Ciampi and Carlos Santiago and Joao Costeira and Claudio Gennaro and Giuseppe Amato}, title = {{Night and Day Instance Segmented Park (NDISPark) Dataset: a Collection of Images taken by Day and by Night for Vehicle Detection, Segmentation and Counting in Parking Areas}}, month = may, year = 2022, publisher = {Zenodo}, version = {1.0.0}, doi = {10.5281/zenodo.6560823}, url = {https://doi.org/10.5281/zenodo.6560823} } </pre> </blockquote> <p> </p> <p><strong>Contact Information</strong></p> <p>If you would like further information about the dataset or if you experience any issues downloading files, please contact us at <a href="mailto:mobdrone@isti.cnr.it">luca.ciampi@isti.cnr.it</a></p> <p> </p>
Supporting Data for Human Factors in Developing Automated Vehicles:A Requirements Engineering Perspective
<p>This data set complements our manuscript in submission with the title:</p> <p>"Human Factors in Developing Automated Vehicles: A Requirements Engineering Perspective"</p> <p>We provide two files:</p> <p>a) the interview guide</p> <p>b) an overview that maps from themes to example quotes and codes derived from particular interview subjects</p>
autonomous-vehicle-interests-multivariate-modeling
<p>This is a release of data and analysis scripts of the "Private or on-demand autonomous vehicles? Modeling public interest using a multivariate model " research study. It contains the 2019 California Vehicle Survey data as well as the scripts followed to clean and analyze the data. A word document within the folder "Description.docx" presents the steps followed to get the outputs. All scripts are written in R.</p>
Famous Vehicles Fixed FREE
Introducing the Famous Vehicles Fixed made by ACBRadio, the programs used to make this object are as follows: Blender 2.93 & G.I.M.P 2.10.4…The 'Textures' inclued in this object are the following: Diffuse Solid Color…One single image of a 256 bit color palette…free of Charge Backdrop image by Micah Boerma over on https://www.pexels.com 360 backdrop's total poly count: Triangles: 960 Vertices: 482 Note: Have receipt to represent ownership...major change, interiors removed :D Source: Objaverse 1.0 / Sketchfab
Medieval Siege Engine Vehicle
This was created for a college project in 3ds max, and later imported into Unity as a vehicle which the player could drive around. It is a medieval siege engine. Source: Objaverse 1.0 / Sketchfab
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.