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60 results for “automotive”
The TREASURE semantic social network data on the circular economy aspect of automotive manufacturing
<p>The <a href="https://www.treasureproject.eu/">TREASURE</a> project looks at industrial innovation to address the problem of making onboard electronics in the automotive industry easier to recycle, increasing the industry's contribution to the circular economy. As part of it, a team of ethnographers generated and coded the corpus contained in this dataset. interviews conducted between January 2022 and June 2023 with car owners and enthusiasts at car industry events. The interviews focus on experiences with car electronics and perspectives on sustainability and the circular economy. The dataset is pseudonymized. TREASURE is supported by the European Union's Horizon 2020 programme, grant n. 101003587.</p>
Low-Voltage Icing Protection Film for Automotive and Aeronautical Industries
<p>dataset on </p> <p>Dynamic Mechanical Analysis, Electro-Mechanical Measurement, Dynamic Light Scattering</p> <p>FTIR spectroscopy, Thermogravimetric analysis, Differential Scanning Calorimetry,</p> <p>Electro-Temperature Measurement, Thermal Image Camera, Water sorption measurement,</p> <p>Transmission Electron Microscopy and Stress Strain</p>
The Automotive Visual Inspection Dataset (AutoVI): A Genuine Industrial Production Dataset for Unsupervised Anomaly Detection
<p><strong>See the official website: <a href="https://autovi.utc.fr">https://autovi.utc.fr</a></strong></p> <p>Modern industrial production lines must be set up with robust defect inspection modules that are able to withstand high product variability. This means that in a context of industrial production, new defects that are not yet known may appear, and must therefore be identified.</p> <p>On industrial production lines, the typology of potential defects is vast (texture, part failure, logical defects, etc.). Inspection systems must therefore be able to detect non-listed defects, i.e. not-yet-observed defects upon the development of the inspection system. To solve this problem, research and development of unsupervised AI algorithms on real-world data is required.</p> <p>Renault Group and the Université de technologie de Compiègne (Roberval and Heudiasyc Laboratories) have jointly developed the <em>Automotive Visual Inspection Dataset (AutoVI)</em>, the purpose of which is to be used as a scientific benchmark to compare and develop advanced unsupervised anomaly detection algorithms under real production conditions. The images were acquired on Renault Group's automotive production lines, in a genuine industrial production line environment, with variations in brightness and lighting on constantly moving components. This dataset is representative of actual data acquisition conditions on automotive production lines.</p> <p>The dataset contains 3950 images, split into 1530 training images and 2420 testing images.</p> <p>The evaluation code can be found at <a href="https://github.com/phcarval/autovi_evaluation_code">https://github.com/phcarval/autovi_evaluation_code</a>.</p> <p><strong>Disclaimer</strong><br>All defects shown were intentionally created on Renault Group's production lines for the purpose of producing this dataset. The images were examined and labeled by Renault Group experts, and all defects were corrected after shooting.</p> <p><strong>License</strong><br>Copyright © 2023-2024 Renault Group</p> <p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of the license, visit <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>.</p> <p>For using the data in a way that falls under the commercial use clause of the license, please contact us.</p> <p><strong>Attribution</strong><br>Please use the following for citing the dataset in scientific work:</p> <p>Carvalho, P., Lafou, M., Durupt, A., Leblanc, A., & Grandvalet, Y. (2024). The Automotive Visual Inspection Dataset (AutoVI): A Genuine Industrial Production Dataset for Unsupervised Anomaly Detection [Dataset]. <a href="https://doi.org/10.5281/zenodo.10459003">https://doi.org/10.5281/zenodo.10459003</a></p> <p><strong>Contact</strong><br>If you have any questions or remarks about this dataset, please contact us at philippe.carvalho@utc.fr, meriem.lafou@renault.com, alexandre.durupt@utc.fr, antoine.leblanc@renault.com, yves.grandvalet@utc.fr.</p> <p><strong>Changelog</strong></p> <ul> <li><em>v1.0.0</em> <ul> <li>Cropped engine_wiring, pipe_clip and pipe_staple images</li> <li>Reduced tank_screw, underbody_pipes and underbody_screw image sizes</li> </ul> </li> <li><em>v0.1.1</em> <ul> <li>Added ground truth segmentation maps</li> <li>Fixed categorization of some images</li> <li>Added new defect categories</li> <li>Removed tube_fastening and kitting_cart</li> <li>Removed duplicates in pipe_clip</li> </ul> </li> </ul>
STREAM - Sub-THz Radar sensing of the Environment for future Autonomous Marine platforms: Multi-Perspective Sensing - Automotive Environment
<p>This dataset contains the files corresponding to which results have been included in the journal paper. The full description of the conducted trials and data structure is mentioned in the attached PDF document.</p> <p>The trials were conducted at the University of Birmingham using distributed radar sensors installed on the mobile laboratory. The data will be used to develop algorithms to extract the information needed for high-resolution multi-modal and multi-perspective sensing.</p> <p>The experiments were performed with automotive radars operating in the 79 GHz band to investigate the Doppler and imaging capabilities of these radars.</p> <p>This report describes the measurement scenarios and data structure of INRAS Radarlog (76 GHz – 81 GHz) used for the data collection campaign.</p> <p>Contact: a.a.a.pirkani@bham.ac.uk, anum.apirkani@gmail.com, or m.s.gashinova@bham.ac.uk</p>
Automotive Lidar and Vibration: Resonance, Inertial Measurement Unit and Effects on the Point Cloud
<p>This data repository contains vibration tests of an Ouster OS1-64 lidar including a docker based python environment and documentation.</p> <p>It consists of movement data, Fotos, IMU data, pointclouds and a ground truth measurements with an Riegl VZ6000 laser scaner.</p> <p>For further details see the linked publication and the example.ipynb notebook.</p> <p><strong>Quick start</strong></p> <ul> <li> <p>download the repo</p> </li> <li> <p>unzip the archive</p> </li> <li> <p>install VS code with the remote development extension</p> </li> <li> <p>install docker desktop</p> </li> <li> <p>open the folder in a new VS code window</p> </li> <li> <p>Say "yes" to open the folder inside a docker container</p> </li> <li> <p>wait for the container to start</p> </li> <li> <p>open the example jupyter notebook</p> </li> </ul> <p><strong>Structure</strong></p> <blockquote> <pre>├── .devcontainer .... VS code devcontainer (arm64 and amd64)</pre> <pre>├── Acceleromenter_A_z_deflection .... Accelerometer data</pre> <pre>├── Foto .... Fotos of the setup</pre> <pre>│ ├── VZ6000 .... Fotos of the ground truth measurements</pre> <pre>│ └── test_setup .... Fotos of the test setup</pre> <pre>├── Notebook .... A jupyter notebook with examples</pre> <pre>├── OS1_64_IMU .... Ouster IMU data</pre> <pre>├── OS1_64_pointcloud .... point cloud data</pre> <pre>├── VZ6000_groundtruth .... Ground truth data from Riegl VZ6000</pre> <pre> └── targets .... contains 2 different sets of reference</pre> <pre>│ ├── scene_aligned_by_reflectors</pre> <pre>│ └── targets_aligned</pre> <pre>└── files.csv .... an overview of the files and meta data</pre> </blockquote>
Dataset of "PEMFC performance decay during real-world automotive operation: evincing degradation mechanisms and heterogeneity of ageing"
<p>This is the underlying dataset of "PEMFC performance decay during real-world automotive operation: evincing degradation mechanisms and heterogeneity of ageing"</p>
Automotive Domain Property Specification Pattern Dataset
<p>This dataset contains 1000 requirements written in the Property Specification Pattern (PSP) format, specifically tailored for the automotive domain. The dataset serves as a valuable resource for researchers and practitioners working on the formal verification of automotive software, providing a comprehensive set of standardized requirements that can be used for validation, testing, and benchmarking of verification tools and methodologies.</p>
ViF-GTAD: A new Automotive Data Set with Ground Truth for ADAS/AD Development, Testing and Validation
<p>A new dataset for automated driving, which is the subject matter of this paper, identifies and addresses a gap in existing similar perception data sets. While the most state-of-the-art perception data sets primarily focus on provision of various on-board sensor measurements along with the semantic information under various driving conditions, the provided information is often insufficient since the object list and position data provided include unknown and time-varying errors. The current paper and the associated data-set describes the first publicly available perception measurement data that include not only the on-board sensor information from camera, Lidar and radar with semantically classified objects, but also the high precision ground-truth position measurements enabled by the accurate RTK assisted GPS localization systems available on both the ego vehicle and the dynamic target objects. This paper provides insight on the capturing of the data, explicitly explaining the meta data structure and the content, as well as the potential application examples where it has been, and can potentially be, applied and implemented in relation to automated driving and environmental perception systems development, testing and validation.</p>
Source data for road transportation applications (road surface assessment, authentication of automotive vehicles)
<p>This data set records the driving using an Inertial Measurement Units of 12 different vehicles on the road infrastructure of the European Commission Joint Research Centre.</p> <p>The data set is described more in detail in the paper:</p> <p>Baldini, G.; Geib, F.; Giuliani, R. Continuous Authentication of Automotive Vehicles Using Inertial Measurement Units. <em>Sensors</em> <strong>2019</strong>, <em>19</em>, 5283.</p> <p><a href="https://doi.org/10.3390/s19235283">https://doi.org/10.3390/s19235283</a></p> <p>Please, cite this paper if you use this data set.</p>
Supplementary Material of "Fuel Starvation in Automotive PEMFC Stacks: Stack Current and Bipolar Plate Resistance"
<p>This video contains the discussed experimental data of the following journal publication, which explains experimental setup, test cycle and the shown data in detail.</p> <p><strong>Nissen, J., Boye, J. P., Schrievers, M., Schwämmlein, J. N., & Hölzle, M. (2025). Fuel Starvation in Automotive PEMFC Stacks: Stack Current and Bipolar Plate Resistance. <em>Journal of Physics: Energy</em>. </strong></p> <p><strong><a href="https://doi.org/10.1088/2515-7655/ada184">https://doi.org/10.1088/2515-7655/ada184</a></strong></p> <p>The time-dependent behavior of the respective fuel cell is furthermore discussed in a follow-up publication:</p> <p><strong>Nissen, J., Boye, J. P., Schwämmlein, J. N., Willich, C., & Hölzle, M. (2025). Fuel starvation in automotive PEMFC stacks: A self-enhancing overheating mechanism. <em>Journal of Physics: Energy</em>. <br><a href="https://doi.org/10.1088/2515-7655/ade288">https://doi.org/10.1088/2515-7655/ade288</a></strong></p>
GP22: A Car Styling Dataset for Automotive Designers
<p>GP22 is the dataset relevant to the designer's perspective and practices. <br> The dataset involves side profiles of cars produced or introduced from the 1960s to 2021.</p> <p><em>Images folder contains dataset images labeled with: SEGEMENT_BRAND_COUNT.jpg</em><br> <strong>SEGMENT</strong> were labeled in single alphabet: A, B, C, D, E, F, J, S, P<br> <strong>BRAND</strong> were labeled in three alphabet: Alf(AlfaRomeo), Ast(Aston Martin), Aud(Audi), Ben(Bentley), Bmw(BMW), Che(Chevrolet), Chr(Chrysler), Cit(Citroen), Fer(Ferrari), Fia(Fiat), For(Ford), Gen(Genesis), GMC(GMC), Hon(Honda), Hum(Hummer), Hyu(Hyundai), Jag(Jaguar), Jep(Jeep), Kia(Kia), Lan(LandRover), Lex(Lexus), Mas(Maserati), Maz(Mazda), Mer(Mercedes Benz), Mit(Mitsubishi), Nis(Nissan), Ope(Opel), Peu(Peugeot), Pon(Pontiac), Por(Porsche), Ren(Renault), Riv(Rivian), Rol(Rolls Royce), Sko(Skoda), Ssa(Ssang Yong), Vwg(Volkswagen), and Vol(Volvo)</p> <p><em>Labels folder contain labels in text file: SEGEMENT_BRAND_COUNT.txt</em><br> Single text file structured in:<br> <strong>0</strong>(tire1): x1, y1, x2, y2 positions of the single box<br> <strong>0</strong>(tire2): x1, y1, x2, y2 positions of the single box<br> <strong>1</strong>(wheel1): x1, y1, x2, y2 positions of the single box<br> <strong>1</strong>(wheel2): x1, y1, x2, y2 positions of the single box<br> <strong>2</strong>(body): x1, y1, x2, y2 positions of the single box<br> <strong>3</strong>(cabin): x1, y1, x2, y2 positions of the single box<br> <strong>4</strong>(day light opening): x1, y1, x2, y2 positions of the single box<br> <strong>5</strong>(bodyside): x1, y1, x2, y2 positions of the single box</p> <p><em>This dataset also contained the YOLO v5 l based detection model trained on GP22: DetectionModel.pt</em></p>
Dataset: ECD Automotive Design, Inc. (ECDA) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: China Automotive Systems, Inc. (CAAS) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: REE Automotive Ltd. (REE) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Polestar Automotive Holding UK PLC (PSNY) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: O'Reilly Automotive, Inc. (ORLY) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Supplementary Material of "Fuel Starvation in Automotive PEMFC Stacks: Hydrogen Stoichiometry and Electric Cell-to-Cell Interaction"
<p>This video contains the discussed experimental data of the following journal publication, which explains experimental setup, test cycle and the shown data in detail.</p> <p><strong>Nissen, J., Boye, J. P., Schwämmlein, J. N., & Hölzle, M. (2024). Fuel starvation in automotive PEMFC stacks: hydrogen stoichiometry and electric cell-to-cell interaction. <em>Journal of Physics: Energy</em>. <br><a title="https://doi.org/10.1088/2515-7655/ad5f54" href="https://doi.org/10.1088/2515-7655/ad5f54">https://doi.org/10.1088/2515-7655/ad5f54</a></strong></p> <p>Version 01: Video as .MKV file. Quite large and not supported for in-browser visualization by zenodo.</p> <p>Version 02: Changed video format from .MKV to .MP4 to reduce file size and allow in-browser visualization by zenodo. Identical content as Version 01.</p>
Dataset to: Realistic accelerated stress tests for PEM fuel cells: Test procedure development based on standardized automotive driving cycles
<p>This is the dataset to the published article "Realistic accelerated stress tests for PEM fuel cells: Test procedure development based on standardized automotive driving cycles" (DOI: 10.1016/j.ijhydene.2023.08.292) in which the degradation of two commercial PEM fuel cell stacks was analyzed. <strong>Please cite this publication if you use the dataset in a publication as follows</strong>:</p> <p>P. Thiele, Y. Yang, S. Dirkes, M. Wick, S. Pischinger, Realistic accelerated stress tests for PEM fuel cells: Test procedure development based on standardized automotive driving cycles, Int. J. Hydrogen Energy 52 (Part D) (2024) 1065–1080, https://doi.org/10.1016/j.ijhydene.2023.08.292.</p>
Automotive CAN bus data: An Example Dataset from the AEGIS Big Data Project
<p>Here you find an example research data dataset for the automotive demonstrator within the "AEGIS - Advanced Big Data Value Chain for Public Safety and Personal Security" big data project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 732189. The time series data has been collected during trips conducted by three drivers driving the same vehicle in Austria.</p> <p>The dataset contains 20Hz sampled CAN bus data from a passenger vehicle, e.g. WheelSpeed FL (speed of the front left wheel), SteerAngle (steering wheel angle), Role, Pitch, and accelerometer values per direction.</p> <p>GPS data from the vehicle (see signals 'Latitude_Vehicle' and 'Longitude_Vehicle' in h5 group 'Math') and GPS data from the IMU device (see signals 'Latitude_IMU', 'Longitude_IMU' and 'Time_IMU' in h5 group 'Math') are included. However, as it had to be exported with single-precision, we lost some precision for those GPS values.</p> <p> </p> <p>For data analysis we use R and R Studio (https://www.rstudio.com/) and the library h5.</p> <p>e.g. check file with R code:</p> <p>library(h5)</p> <p>f <- h5file("file path/20181113_Driver1_Trip1.hdf")</p> <p>summary(f["CAN/Yawrate1"][,])</p> <p>summary(f["Math/Latitude_IMU"][,])</p> <p>h5close(f)</p>
Synthetic automotive LiDAR dataset with radial velocity additional feature - (x,y,z,v)
<p>The synthetic dataset was generated using KITTI-like specifications and annotations format. It is comprised by the KITTI standard folders: label_2, image_2 and calib. Furthermore, there is a velodyne file for each of the following use cases:</p> <ul> <li>Point cloud 1: (x,y,z, (Bool)Is_Object): In this point cloud, the best performance of the Deep Learning model is expected as ground truth information is provided as the additional feature of each point. <ul> <li>Point cloud 1A: (x,y,z, (Bool)Is_Car): the additional feature of each point that belongs to an object of the ’Car’ type has a Boolean 1.0 value; contrariwise, the 0.0 value was used. File: velodyne_1A_isCar;</li> <li>Point cloud 1B: (x,y,z, (Bool)Is_Ped): the additional feature of each point that belongs to an object of the ’Pedestrian’ type has a Boolean 1.0 value; contrariwise, the value 0.0 was used. File: velodyne_1A_isPed.</li> </ul> </li> <li>Point cloud 2: (x,y,z, (Float)Radial_Velocity): this point cloud has the relative radial velocity as an additional feature for each point. File: velodyne_2_radial_velocity;</li> <li>Point cloud 3: (x,y,z,(Float)Absolute_Speed): in this point cloud, every point has the absolute speed of the object as the additional feature. File: velodyne_3_abs_speed;</li> <li>Point cloud 4: (x,y,z,(Bool)Is_Moving): the additional feature of this point cloud is a Boolean value that is set to 1.0 if the object is moving; contrariwise, it is set to 0.0 for static objects. File: velodyne_4_is_moving;</li> <li>Point cloud 5: (x,y,z,0): no additional feature information. If desired, requires post-processing to convert to (x,y,z) or changing the toolbox point cloud configuration to not consider the additional feature. File: velodyne_5_xyz;</li> </ul> <p>Additionally, the label split for testing and training sets used can be found at file: Labels_split.</p> <p>This work was made as part of a master thesis. For further details, please check the dataset generation source code [1]. Any further questions please contact Leandro Alexandrino (l.alexandrino@ua.pt).</p> <p> </p> <p>[1] - Fork deepgtav-presil - leandro alexandrino, https://github.com/leandroalexandrino1995/DeepGTAVPreSIL.</p>
ScienceDex guides
Understand access before you commit
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.