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155 results for “Vehicle Data”
Simulated Highway Lane Change Data with Uncertain Vehicle Parameters
<p>This data set consists of closed-loop simulations for highway lane changes by means of time series data. Thereby, the lane changes are planned with three different planners (state variable filter, fifth order polynomial, optimization-based), and executed with four different controllers (flatness-based feedforward with PD feedback control, input/output linearization, higher order sliding mode controller, inversion & invariance based control). The vehicle dynamics model is a non-linear bicycle model. The lane change scenarios vary in lane change duration, vehicle velocity, vehicle parameters, and odometry errors.<br>Additionally to the data, the Matlab code to repeat the analysis on your own is provided.<br>For more details, refer to:</p> <p>M. Gurtner, J. Weber, P. Zips and A. Kugi, "The Role of Trajectory Planners in Lane Change Tracking Control: A Monte Carlo Evaluation of Four Controllers under Uncertainty," <em>2024 European Control Conference (ECC)</em>, Stockholm, Sweden, 2024, pp. 3847-3853, doi: 10.23919/ECC64448.2024.10590871.</p> <p> </p> <p>The research leading to these data has received funding from the Mobility of the Future programme (Grant No. 884344).<br>Mobility of the Future is a research, technology and innovation funding programme of the Republic of Austria, Ministry of Climate Action.<br>The Austrian Research Promotion Agency (FFG) has been authorised for the programme management.</p>
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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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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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>
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>
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>
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>
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>
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>
Vehicle trajectory data in simulation network
<p>The project will use vehicle trajectory data generated from simulation platform. The simulation network was built in VISSIM containing a four-leg intersection with left-turn, through, and right-turn movements. The trajectory data were generated based on various traffic demand levels. The data set contains second-by-second vehicle speed and location. Details of the data set are explained as:</p> <p>Column 1 (NO): Number (Number/Index of the vehicle)<br> Column 2 (SimSec): Simulation second (Simulation time [s]) [s]<br> Column 3 (Lane\Link\No): Lane\Link\Number (Unique number of the link or connector)<br> Column 4 (Lane\Index): Lane\Index (Unique number of the lane)<br> Column 5 (Speed): Speed (Speed at the end of the time step) [km/h]<br> Column 6 (Pos): Position (Distance on the link from the beginning of the link or connector) [m]</p>
Decision-Making Tool for Road Preventive Maintenance Using Vehicle Vibration Data
<p>Corresponding data set for Tran-SET Project No. 18PLSU08. Abstract of the final report is stated below for reference:</p> <p>"Automated and timely road pavement damage inspection is critical to the preventive maintenance and the long-term sustainability and resilience of roads in Region 6. Current road inspection practices rely heavily on a manual process. Sensor-based methods (e.g., LiDAR scanning) are promising but can be too expensive for a wider adoption. This study employs a crowdsourcing approach of using the vibration patterns of regular vehicles in inferring specific types of road damages. A cloud-based smart phone app and system was developed to collect real-time vehicle vibrations, location data, and road damage images for training the detection model. However, there is a great challenge in using classic classification methods with crowdsourced vibration data containing high level of noises, as vehicle vibrations are greatly affected by the types and conditions of the vehicles, as well the varying driving behaviors of drivers. The study thus employed the recent developments in Deep Learning methods, including a Self-Taught Learning (STL) algorithm and Sparse Coding to tackle with the low-quality issues of collected data. A total of 310 miles of road-induced vehicle vibration data was collected in Texas and Louisiana, and the road damage detection model was trained on Texas A&M University (TAMU) supercomputing server. The results show that the features generated from Sparse Coding greatly contribute to enhancing detection performances, by addressing low-quality data issues."</p>
Source Data for Crowdsourcing Bridge Dynamic Monitoring with Smartphone Vehicle Trips
<p>This data accompanies the study "Crowdsourcing Bridge Dynamic Monitoring with Smartphone Vehicle Trips" published in (Nature) Communications Engineering. This paper focuses on using large and inexpsensive datasets for obtaining information on the dynamics of bridges. In this study, data is collected by smartphones in moving vehicles as the cross over a bridge, in three distinct applications. Smartphone data was collected in controlled field experiments and uncontrolled Uber rides on a long-span suspension bridge in the USA (The Golden Gate Bridge) and an analytical method was developed to accurately recover modal properties. The method was also successfully applied to partially-controlled crowdsourced data collected on a short-span highway bridge in Italy. The results suggest that larve and inexpensive datasets collected by smartphones could play a role in monitoring the health of existing transportation infrastructure.</p> <p>The data provided includes the source data for the figures in the publication as well as the "controlled data" referenced in the study.</p>
Data from: Multiobjective optimization algorithm for accurate MADYMO reconstruction of vehicle-pedestrian accidents
<p>Uncertainty in reconstruction accuracy is a critical problem faced in the current traffic accident reconstruction process. The purpose of this study is to explore the use of an improved optimization algorithm combined with MAthematical DYnamic MOdels (MADYMO) multibody simulations and crash data to conduct accurate reconstructions of vehicle–pedestrian accidents. The performance of three commonly employed multiobjective optimization algorithms, including nondominated sorting genetic algorithm-II (NSGA-II), neighbourhood cultivation genetic algorithm (NCGA) and multiobjective particle swarm optimization (MOPSO) were compared and evaluated. The effects of the number of objective functions, the selection of different objective functions and the optimal number of iterations are also investigated. The present study indicated that NSGA-II had better convergence and generated more noninferior solutions and better final solutions than NCGA and MOPSO. And multibody simulations coupled with optimization algorithms can be used to accurately reconstruct vehicle-pedestrian collisions.</p>
Private vehicles greenhouse gas emissions at street level for Berlin based on open data
<p>We estimated the annual average daily GHG emissions from individual motor traffic for the OSM road network in Berlin by combining the estimated Annual Average Daily Traffic Volume (AADTV) with respective emission factors. The AADTV was calculated by simulating car trips with the open routing engine Openrouteservice, weighted by activity functions based on statistics of the German Mobility Panel.</p>
Vehicle trajectory and pavement behavior data
<p>The dataset includes three documents.</p> <p><strong>HDV_data_NGSIM_I_80.xlsx</strong></p> <p>The vehicle trajectory data from Next Generation SIMulation (NGSIM) dataset was collected on eastbound I-80 in the San Francisco Bay area, in Emeryville, CA, on April 13, 2005, from 4:03:56 pm to 4:08:56 pm. Including vehicle id, frame id, the total count of frames of each vehicle, global time, local position, global position, vehicle length, vehicle width, vehicle class, speed, acceleration, lane id, preceding vehicle id, following vehicle id, space headway, time headway, and time.</p> <p><strong>CAV_data_CARLA_SUMO.xml</strong></p> <p>The simulated CAV trajectory data with CARLA and SUMO, including vehicle id, position, angle, type, speed, lane id, and slope of each frame.</p> <p><strong>LTPP_data.csv</strong></p> <p>The table including 21 columns is calculated from the Long-Term Pavement Performance (LTPP) database.</p> <ul> <li>IRI The IRI value measured when age was 0. (m/km)</li> <li>Cr_Gator Area of alligator cracking in square meters. (m^2)</li> <li>Cr_Lwp Length of longitudinal cracks within the defined wheel paths in meters. (m)</li> <li>Cr_Lnwp Length of longitudinal cracks not in the defined wheel paths in meters. (m)</li> <li>Pt_A Area of patches in square meters. (m^2)</li> <li>Pt_N Number of patches in square meters. (m^2)</li> <li>Cr_Wp Length of wheelpath cracks in meters. (m)</li> <li>Cr_Gt183 Total length of transverse cracks greater than 1.83. (m)</li> <li>Rt The depth of rutting in millimeters. (mm)</li> <li>Fr Friction number between the vehicle wheel tire and the pavement</li> <li>IRI_0 The IRI value measured when age was 0. (m/km)</li> <li>Tk_Sb Layer thickness measurement for surface coarse and binder course. (in)</li> <li>Md_s Average backcalculated elastic modulus of the surface layer.(psi)</li> <li>Hydr Average measured hydraulic conductivity of the specimen. (cm/sec)</li> <li>Prcp Average monthly precipitation in millimeters. (mm)</li> <li>Fz Average freeze index. (℃/day)</li> <li>Esal Annual average ESAL (kESAL)</li> <li>Esal_q quadratic form of Kesal (kESAL^2)</li> <li>Age Time duration between new construction to roughness survey date. (year)</li> <li>Gr Mean specific gravity of asphalt cement</li> <li>Pt_Ca Coarse aggregate amount percent by total weight of aggregate in percentage. (%) </li> </ul>
Dataset for Vehicle Indoor Positioning in Industrial Environments with Wi-Fi, inertial, and odometry data
<p>Dataset collected in an indoor industrial environment using a mobile unit (manually pushed trolley) that resembles an industrial vehicle equipped with several sensors, namely, Wi-Fi, wheel encoder (displacement), and Inertial Measurement Unit (IMU).</p> <p>Sensors were connected to a Raspberry Pi (RPi 3B +), which collected the data from the sensors. Ground truth information was obtained with video camera pointed towards the floor, registering the times when the trolley passed by reference tags.</p> <p>List of sensors:</p> <ul> <li>4x <strong>Wi-Fi interfaces</strong>: Edimax EW7811-Un</li> <li>2x <strong>IMUs</strong>: Adafruit BNO055</li> <li>1x <strong>Absolute Encoder</strong>: US Digital A2 (attached to a wheel with a diameter of 125 mm)</li> </ul> <p>This dataset includes:</p> <ul> <li>1x <strong>Wi-Fi radio map</strong> that can be used for Wi-Fi fingerprinting.</li> <li>6x <strong>Trajectories</strong>: including sensor data + ground truth.</li> <li><strong>APs Information</strong>: list of APs in the building, including their position and transmission channel.</li> <li><strong>Floor plan:</strong> image of the building's floor plan with obstacles and non-navigable areas.</li> <li><strong>Python package</strong> provided for: <ul> <li>parsing the dataset into a data structure (Pandas dataframes).</li> <li>performing statistical analysis on the data (number of samples, time difference between consecutive samples, etc.).</li> <li>computing Dead Reckoning trajectory from a provided initial position.</li> <li>computing Wi-Fi fingerprinting position estimates.</li> <li>determining positioning error in Dead Reckoning and Wi-Fi fingerprinting.</li> <li>generating plots including the floor plan of the building, dead reckoning trajectories, and CDFs.</li> </ul> </li> </ul> <p> </p> <p>When using this dataset, please cite its data description paper:</p> <p>Silva , I.; Pendão, C.; Torres-Sospedra, J.; Moreira, A. Industrial Environment Multi-Sensor Dataset for Vehicle Indoor Tracking with Wi-Fi, Inertial and Odometry Data. <em>Data</em> <strong>2023</strong>, <em>8</em>, 157. <a href="https://doi.org/10.3390/data8100157" target="_blank" rel="noopener">https://doi.org/10.3390/data8100157</a> </p> <p> </p>
Data from: Multiobjective optimization algorithm for accurate MADYMO reconstruction of vehicle-pedestrian accidents
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Data from: a physics-based digital twin for model predictive control of autonomous unmanned aerial vehicle landing
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Data from: Empirical evidence for the potential climate benefits of decarbonizing light vehicle transport in the U.S. with bioenergy from purpose-grown biomass with and without BECCS
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Allen Brain Atlas
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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.