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1,308 results for “Vehicle”
Market Adoption of Electric Vehicles in Indonesia
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vehicle body markers dataset
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Data from: Synchronous effects produce cycles in deer populations and deer-vehicle collisions
<p>Population cycles are fundamentally linked with spatial synchrony, the prevailing paradigm being that populations with cyclic dynamics are easily synchronized. That is, population cycles help give rise to spatial synchrony. Here we demonstrate this process can work in reverse, with synchrony causing population cycles. We show that timescale-specific environmental effects, by synchronizing local population dynamics on certain timescales only, cause major population cycles over large areas in white-tailed deer. An important aspect of the new mechanism is specificity of synchronizing effects to certain timescales, which causes local dynamics to sum across space to a substantial cycle on those timescales. We also demonstrate, to our knowledge for the first time, that synchrony can be transmitted not only from environmental drivers to populations (deer), but also from there to human systems (deer-vehicle collisions). Because synchrony of drivers may be altered by climate change, changes to population cycles may arise via our mechanism.</p>
A Two-time-level Model for Mission and Flight Planning of an Inhomogeneous Fleet of Unmanned Aerial Vehicles
<p>We consider the mission and flight planning problem for an inhomogeneous fleet of unmanned aerial vehicles (UAVs). Therein, the mission planning problem of assigning targets to a fleet of UAVs and the flight planning problem of finding optimal flight trajectories between a given set of waypoints are combined into one model and solved simultaneously. Thus, trajectories of an inhomogeneous fleet of UAVs have to be specified such that the sum of waypoint-related scores is maximized, considering technical and environmental constraints. Several aspects of an existing basic model are expanded to achieve a more detailed solution. A two-level time grid approach is presented to smooth the computed trajectories. The three-dimensional mission area can contain convex-shaped restricted airspaces and convex subareas where wind affects the flight trajectories. Furthermore, the flight dynamics are related to the mass change, due to fuel consumption, and the operating range of every UAV is altitude-dependent. A class of benchmark instances for collision avoidance is adapted and expanded to fit our model and we prove an upper bound on its objective value. Finally, the presented features and results are tested and discussed on several test instances using GUROBI as a state-of-the-art numerical solver.</p>
Analysis of Circular Price Prediction Strategy for Used Electric Vehicles
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Supporting data for "Health benefits of US light-duty vehicle electrification: roles of fleet dynamics, clean electricity, and policy timing"
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Schematic representing the vehicle–to–cell hierarchical overview of a typical electrified powertrain architecture. This represents the system-level context within which the proposed layer optimisation framework has been developed. Two xEV powertrains — a) BEV and b) series PHEV — are chosen as examples to demonstrate how the methodology facilitates common module designs for such battery packs.
<p>Schematic representing the vehicle–to–cell hierarchical overview of a typical electrified powertrain architecture. This represents the<br> system-level context within which the proposed layer optimisation framework has been developed. Two xEV powertrains — a) BEV and b) series PHEV — are chosen as examples to demonstrate how the methodology facilitates common module designs for such battery packs.</p>
Glacial sediment-rich meltwater plume investigation using a high-resolution multispectral sensor embedded in an Unmanned Aerial Vehicle
<p>Methodology video</p>
Vehicle Sensing and Communications using LED Headlights to Enhance the Performance of Intelligent Transportation Systems: Proof of Concept, Implementation, and Applications
<p>Corresponding data set for Tran-SET Project No. 18ITSOKS01. Abstract of the final report is stated below for reference:</p> <p>"This project investigates the use of vehicle light-emitting diode (LED) headlamp devices for improving the accuracy and reliability of traffic (sensing and communication) data measurements required for developing effective intelligent transportation systems (ITS) technologies and solutions.</p> <p>Vehicular communication and sensing technologies are mainly based on conventional radio frequency (RF) or laser technologies. These systems suffer from several issues such as RF interference and poor performance in scenarios where the incidence angle between the speed detector and the vehicle is rapidly varying. Introducing a new sensing technology will add diversity to these systems and enhance the reliability of the real-time data. In this project, we proposed and investigated a novel speed estimation sensing system named “Visible Light Detection and Ranging (ViLDAR)” (patent pending).</p> <p>ViLDAR utilizes visible light-sensing technology to measure the variation of the vehicle’s headlamp light intensity to estimate the vehicle speed. Similarly, visible light sensing technology is used for data communication purposes, where the vehicle headlamp is utilized for wireless data transmission purposes. This project outlines the ViLDAR system simulations, implementation including hardware and software components, experimental evaluation in both laboratory and outdoor environments. The experimental measurement settings of the ViLDAR experiments are detailed. Encouraging results for both sensing and communication scenarios are obtained. The outcome of this proof-of-concept study both in the laboratory and outdoor validates the merit of the proposed technology in speed estimation (sensing) and data communication. The outcomes of this project will inspire a wide and diverse range of researchers, scientists and practitioners from the ITS community to explore this new and exciting technology. This project built initial steps in exploring this new sensing and communication modality using vehicle headlamps, leaving open a wide field for exploration and novel research."</p>
Smart Charging of Future Electric Vehicles Using Roadway Infrastructure
<p>Corresponding data set for Tran-SET Project No. 18ITSTSA03. Abstract of the final report is stated below for reference:</p> <p>"Inspired by the fact that there is an immense amount of renewable energy sources available on the roadways such as mechanical pressure and frictional heat, this study presented the development and implementation of an innovative charging technique for future electric vehicles (EVs) by fully utilizing the existing roadways and the state-of-the-art nanotechnology and power electronics. The project introduced a novel wireless charging system, SIC (Smart Illuminative Charging), that uses LEDs powered by piezoelectric nanomaterials as the energy transmitter source and thin film solar panels placed at the bottom of the EVs as the receiver, which is then poised to deliver the harvested energy to the vehicle’s battery. Through the project, the energy-harvestable 2D nanomaterials (EH2Ns) were tested for their mechanical-to-electrical energy conversion capabilities and the relatively large-area EH2N samples (2cm x 2cm) produced high output voltages of up to 52mV upon mechanical pressure. An electrically conductive glass fiber reinforced polymer (GFRP) was developed to be used as physical support in the integrated SIC system. Furthermore, a lab-scale prototype device was developed to testify the mechanism of illuminative charging. The project team was able to prove the feasibility of SIC concept and the start to end conversion efficiency was calculated to be 40%. The project team also provided field implementation recommended framework based on the results from the small-scale prototype developed. The framework discussed how the developed SIC can be implemented in the field and what are the expected outcomes. The team recommended inserting the EH2N embedded in the GFRP, the LEDs and the needed circuitry in the wheel path of the vehicles on the pavement by cutting a sawtooth compartment with a width of 18’’ and a length of 8’ every couple of miles. On the vehicle, a PV array will be placed on the underside between the wheel wells of each side of the EV to capture the illumination from the LEDs embedded in the roadway. The detailed strategy is presented in this report."</p>
Figure 3 in Identifying the hotspots of wildlife-vehicle collision on the Çankırı-Kırıkkale highway during summer
Figure 3. Monthly average traffic density by the hour on the Çankırı–Kırıkkale highway (above with moderate traffic density, below with low traffic density).
Figure 4 in Identifying the hotspots of wildlife-vehicle collision on the Çankırı-Kırıkkale highway during summer
Figure 4. The KDE map of the mammals that were killed in accidents on the Çankırı–Kırıkkale highway.
Data from: A new low-turbulence wind tunnel for animal and small vehicle flight experiments
Our understanding of animal flight benefits greatly from specialized wind tunnels designed for flying animals. Existing facilities can simulate laminar flow during straight, ascending and descending flight, as well as at different altitudes. However, the atmosphere in which animals fly is even more complex. Flow can be laminar and quiet at high altitudes but highly turbulent near the ground, and gusts can rapidly change wind speed. To study flight in both laminar and turbulent environments, a multi-purpose wind tunnel for studying animal and small vehicle flight was built at Stanford University. The tunnel is closed-circuit and can produce airspeeds up to 50 m s−1 in a rectangular test section that is 1.0 m wide, 0.82 m tall and 1.73 m long. Seamless honeycomb and screens in the airline together with a carefully designed contraction reduce centreline turbulence intensities to less than or equal to 0.030% at all operating speeds. A large diameter fan and specialized acoustic treatment allow the tunnel to operate at low noise levels of 76.4 dB at 20 m s−1. To simulate high turbulence, an active turbulence grid can increase turbulence intensities up to 45%. Finally, an open jet configuration enables stereo high-speed fluoroscopy for studying musculoskeletal control in turbulent flow.
Data from: Clap-and-fling mechanism in a hovering insect-like two-winged flapping-wing micro air vehicle
This study used numerical and experimental approaches to investigate the role played by the clap-and-fling mechanism in enhancing force generation in hovering insect-like two-winged flapping-wing micro air vehicle (FW-MAV). The flapping mechanism was designed to symmetrically flap wings at a high flapping amplitude of approximately 192°. The clap-and-fling mechanisms were thereby implemented at both dorsal and ventral stroke reversals. A computational fluid dynamic (CFD) model was constructed based on three-dimensional wing kinematics to estimate the force generation, which was validated by the measured forces using a 6-axis load cell. The computed forces proved that the CFD model provided reasonable estimation with differences less than 8%, when compared with the measured forces. The measurement indicated that the clap and flings at both the stroke reversals augmented the average vertical force by 16.2% when compared with the force without the clap-and-fling effect. In the CFD simulation, the clap and flings enhanced the vertical force by 11.5% and horizontal drag force by 18.4%. The observations indicated that both the fling and the clap contributed to the augmented vertical force by 62.6% and 37.4%, respectively, and to the augmented horizontal drag force by 71.7% and 28.3%, respectively. The flow structures suggested that a strong downwash was expelled from the opening gap between the trailing edges during the fling as well as the clap at each stroke reversal. In addition to the fling phases, the influx of air into the low-pressure region between the wings from the leading edges also significantly contributed to augmentation of the vertical force. The study conducted for high Reynolds numbers also confirmed that the effect of the clap and fling was insignificant when the minimum distance between the two wings exceeded 1.2c (c = wing chord). Thus, the clap and flings were successfully implemented in the FW-MAV, and there was a significant improvement in the vertical force.
Deer-vehicle collisions in Denmark
<p>Vehicles collide with hundreds of thousands of deer on European roads each year. This leads to animal deaths and suffering, economic damage and risks for human safety, making the reduction of road mortality a major field in conservation biology. In order to successfully reduce roadkill, we need improved knowledge regarding spatio-temporal patterns of deer-vehicle collisions (DVCs) on a landscape scale. Here, we analyzed >85,000 DVCs collected over 17 years in Denmark to investigate changes in the number of DVCs over time and to find spatio-temporal patterns of DVC occurrence. We used a use-availability design – originally developed for habitat selection analyses – to compare DVCs involving roe deer (<i>Capreolus capreolus</i>), red deer (<i>Cervus elaphus</i>) and fallow deer (<i>Dama dama</i>) with random road locations on a landscape scale. This approach enabled us to combine temporal (seasonal and diel variation), spatial (land cover, road density and type) and other relevant variables (deer population density, traffic, and deer activity) within the same analysis. We found that factors related to infrastructure and land cover were most important in explaining patterns of DVCs, but seasonal and diel changes, deer activity, and population density were also important in predicting the occurrence of DVCs. Importantly, patterns of DVCs were largely similar between the three deer species, with more DVCs occurring at intermediate traffic density, increasing forest cover, during dusk and dawn, and with increasing deer activity and population density. The strong and consistent patterns found here will allow the development of flexible mitigation measures. We propose that our findings could be used to develop a spatio-temporally flexible warning system for smartphones and navigation systems that is based on existing map providers, making it a widely available and cheap mitigation measure.</p>
Data set for reliability-based lift-to-power consumption optimization with an accelerated Kriging model for clapping-wing micro air vehicles
<p>Procedures of the reliability-based lift-to-power consumption optimization with an accelerated Kriging model</p> <p>Step 1: Run the file “LHS.m” to generate initial samples.</p> <p>Step 2: Modify the aerodynamic model according to initial samples (e.g. flapping1_Def.xml, flapping1.bat), and then run the “.bat file” to obtain the original force data.</p> <p>Step 3: Run the file “Kriging.m” to obtain the average lift using a filter.</p> <p>Step 4: Run the file “FW_2.m”, “FW_3.m” to obtain sub-optimal-result.</p> <p>Step 5: Find the new training sample and obtain the eigenvalue of the new training sample.</p> <p>Step 6: Rerun the file “FW_2.m”, “FW_3.m” to obtain sub-optimal-result by reloading the new “.mat” files (e.g. FW_2_41.mat, FW_2_P_20.mat).</p> <p>Step 7: Go to Step 4 until the convergence criteria are satisfied.</p> <p>Step 8: Obtain the optimal result. PS: Other files are function files.</p>
A Fine-Grained Vehicle Detection (FGVD) Dataset for Unconstrained Roads
<p>The previous fine-grained datasets mainly focus on classification and are often captured in a controlled setup, with the camera focusing on the objects. We introduce the first Fine-Grained Vehicle Detection (FGVD) dataset in the wild, captured from a moving camera mounted on a car. It contains 5502 scene images with 210 unique fine-grained labels of multiple vehicle types organized in a three-level hierarchy. While previous classification datasets also include makes for different kinds of cars, the FGVD dataset introduces new class labels for categorizing two-wheelers, autorickshaws, and trucks. The FGVD dataset is challenging as it has vehicles in complex traffic scenarios with intra-class and inter-class variations in types, scale, pose, occlusion, and lighting conditions. The current object detectors like yolov5 and faster RCNN perform poorly on our dataset due to a lack of hierarchical modeling. Along with providing baseline results for existing object detectors on FGVD Dataset, we also present the results of a combination of an existing detector and the recent Hierarchical Residual Network (HRN) classifier for the FGVD task. Finally, we show that FGVD vehicle images are the most challenging to classify among the fine-grained datasets.</p>
A Fine-Grained Vehicle Detection (FGVD) Dataset for Unconstrained Roads
<p>The previous fine-grained datasets mainly focus on classification and are often captured in a controlled setup, with the camera focusing on the objects. We introduce the first Fine-Grained Vehicle Detection (FGVD) dataset in the wild, captured from a moving camera mounted on a car. It contains 5502 scene images with 210 unique fine-grained labels of multiple vehicle types organized in a three-level hierarchy. While previous classification datasets also include makes for different kinds of cars, the FGVD dataset introduces new class labels for categorizing two-wheelers, autorickshaws, and trucks. The FGVD dataset is challenging as it has vehicles in complex traffic scenarios with intra-class and inter-class variations in types, scale, pose, occlusion, and lighting conditions. The current object detectors like yolov5 and faster RCNN perform poorly on our dataset due to a lack of hierarchical modeling. Along with providing baseline results for existing object detectors on FGVD Dataset, we also present the results of a combination of an existing detector and the recent Hierarchical Residual Network (HRN) classifier for the FGVD task. Finally, we show that FGVD vehicle images are the most challenging to classify among the fine-grained datasets.</p>
The Electric Vehicle Travelling Salesman Problem on Digital Elevation Models for Traffic-Aware Urban Logistics Supplementary Material
<p>These files correspond to the supplementary material of the article <em>The Electric Vehicle Travelling Salesman Problem on Digital Elevation Models for Traffic-Aware Urban Logistics</em>. </p> <p><strong>Code</strong></p> <ul> <li><strong>algorithm.py</strong> corresponds to an implementation of the algorithm developed in the paper to solve the EV-TSP for the city of Madrid. It takes as input a list of nodes from the graph of Madrid city <strong>madrid_elevation_energy.pckl</strong> and the output consists of an ordered list of all the nodes representing the solution to the TSP.</li> <li><strong>bellmanFord.py</strong> is a Python implementation of the Bellman-Ford algorithm. </li> <li><strong>evaluation.py</strong> is the script that offers the evaluation of the algorithm offered in Tables 1 and 2 in the paper.</li> <li><strong>neuralNetworkTraining.py</strong> is the script used to train and save the Neural Network model using the data generated by <strong>simulation.py</strong>.</li> <li><strong>nn_model_predictor.py</strong> is a script where the model trained in <strong>neuralNetworkTraining.py</strong> can be used to generate predictions.</li> <li><strong>simulation.py</strong> is the script that simulated the routes through the months of October and November 2022 using the data in <strong>snapshots_2022.zip</strong>. It generates the routes in <strong>simulationOctober.csv</strong> and <strong>simulationNovember.csv</strong></li> <li><strong>twoOptNearestNeighnors.py</strong> is a Pyhton implementation of the 2-Opt algorithm that uses Nearest Neighbors to generate the initial tour.</li> </ul> <p><strong>Files</strong></p> <ul> <li><strong>Madrid{5,10,15}.pkl</strong> are the test instances for the city of Madrid. Correspond to Python list of list. Each list is a set of stops to visit in the city graph of Madrid (<strong>madrid_elevation_energy.pckl</strong>) </li> <li><strong>energy_estimation_full.h5</strong> is a Keras model trained using <strong>nn_model_predictor.py</strong> to estimate the energy.</li> <li><strong>scaler_full.pkl</strong> is the scaler needed to use the <strong>energy_estimation_full.h5</strong> model.</li> <li><strong>simulation{October, November}.pkl</strong> are the routes generated for each month using <strong>simulation.py</strong>.</li> <li><strong>snapshots_2022.zip</strong> are the traffic data for the months of October and November 2022</li> </ul>
Environmental conditions and vehicle disturbance influence stress behaviours in a working Harris's hawk (Parabuteo unicinctus)
<p>Data set - Environmental conditions and vehicle disturbance influence stress behaviours in a working Harris’s hawk (<em>Parabuteo unicinctus</em>)</p>
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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.