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60 results for “automotive”
Replication Package: An Expert Survey on the Use of Informal Models in the Automotive Industry
<p>This repository contains the replication package for the paper <em>An Expert Survey on the Use of Informal Models in the Automotive Industry</em> by <a href="https://orcid.org/0000-0001-6410-6769">Dominik Fuchß</a>, <a href="https://orcid.org/0000-0001-7312-2891">Thomas Kühn</a>, <a href="https://orcid.org/0000-0002-8953-1064">Jérôme Pfeiffer</a>, <a href="https://orcid.org/0000-0003-3534-253X">Andreas Wortmann</a>, and <a href="https://orcid.org/0000-0002-1593-3394">Anne Koziolek</a>. The paper has been accepted at the <a href="https://www.iese.fraunhofer.de/en/twinarch.html">TwinArch 2023: The 2nd International Workshop on Digital Twin Architecture</a> co-located with <a href="https://conf.researchr.org/home/ecsa-2023">ECSA 2023</a>.</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>
Automotive Sensor Data. An Example Dataset from the AEGIS Big Data Project
<p>This is 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 by using a BeagleBone single plate computer which has been developed at VIF to collect data for driving analytics. The BeagleBoard can be connected to the OBD2 interface of a vehicle to capture data from CAN bus and has been additionally equipped with further sensors (GPS, gyroscope, acceleration). The data in this research dataset was collected during 35 different trips conducted by one driver driving one vehicle in the Graz area in Austria.</p>
Synthetic automotive LiDAR with non-systematic error and automotive LiDAR based on active stereo dataset
<p>The synthetic dataset was generated by transforming the original dataset using several methods. Each of these transformations occurs from a use case:</p><ul><li>UC1 is the original dataset obtained from [1] and represents a point cloud dataset captured by an ideal LiDAR</li><li>UC2 is a realistic point cloud dataset obtained by simulating the non-systematic error of a Velodyne HDL-64E and applying to UC1</li><li>UC3 is our approach to replace the LiDAR with an active stereo setup. Where the point cloud are captured using two cameras, operating stereoscopically, and a dot projector. The cameras are perfectly calibrated and the triangulation is always correct.</li><li>UC4 also obtains the point clouds through triangulation. However we introduced a calibration error on the right camera. The error has the value of 1 pixel and is added to every dimension of the rotation matrix of the right camera.</li><li>UC5 performs an ideal triangulation, same as UC3. However, in this use case, we introduce camera noise to the point clouds.</li><li>UC6 is a combination of the triangulation from UC4 and the camera noise from UC5.</li></ul><p>Additionally, the labels, images and calibration file are also in [1]. For further details, please check the dataset generation source code [2]. </p><p>[1] https://zenodo.org/records/7184990</p><p>[2] https://github.com/RobertoGraca/Active_Stereo_Based_LiDAR</p>
Data for: Automotive braking is a source of highly charged aerosol particles
<p>Although the last several decades have seen a dramatic reduction in emissions from vehicular exhaust, non-exhaust emissions (e.g., brake and tire wear) represent an increasingly significant class of traffic-related particulate pollution. Aerosol particles emitted from the wear of automotive brake pads contribute roughly half of the particle mass attributed to non-exhaust sources, while their relative contribution to urban air pollution overall will almost certainly grow, coinciding with vehicle fleet electrification and the transition to alternative fuels. To better understand the implications of this growing prominence, a more thorough understanding of the physicochemical properties of brake wear particles (BWPs) is needed. Here we investigate the electrical properties of BWPs as emitted from ceramic and semi-metallic brake pads. We show that up to 80% of BWPs emitted are electrically charged, and demonstrate a dependence of charge state on particle size and charge polarity. We find that brake wear produces both positive and negative charged particles that can hold in excess of 30 elementary charges, and show evidence that more negative charges are produced than positive. Our results will provide insights into the currently limited understanding of how BWPs behave in the atmosphere, including future investigations into their atmospheric lifetimes and potential climatic relevance. In addition, our study will inform future efforts to remove BWP emissions before entering the atmosphere by taking advantage of their electric charge.</p>
Example data and scripts for the paper: Team maturity and reorganization during a very large-scale agile transformation in the automotive industry
<p>Example data and scripts for the paper: Team maturity and reorganization during a very large-scale agile transformation in the automotive industry. </p>
Application of optimized CNN to fixture layout in automotive parts. Training Dataset.
<p>Fixture layout has aroused substantial interest in the research community during the last decades. It affects the design and production of fixtures themselves, and therefore manufacturing costs. While fixturing may seem simple in conception, it requires expertise and well-trained engineers. </p> <p>A general principle, usually called the 3-2-1 locating principle, ensures fixing an object, restringing its degrees of freedom. Fixtures must always comply with the principle. While most research approaches automation of the fixture layout using optimization or rule-based frameworks, this paper proposes supervised learning. The presented framework solves the 3-2-1 locating principle for sheet metal designs based on the experience of previous designs, using automotive b-pillars as a test study.</p> <p>There are three main contributions. 1. A novel idea to introduce sheet metal design data in a Convolutional Neural Network (CNN), projecting the geometry over a plane. The Z coordinate transforms into gray-scale pixel values, generating a topographic map. 2. The framework reuses knowledge about fixturing to layout new workpieces. The framework is an add-in integrated with the CAD environment. 3. A hyperparameter-tuned CNN for regression generates the final output.</p> <p>The results show high accuracy (≈ 100%) in classifying b-pillars and fast convergence in regression, proving model usability for industrial cases.</p> <p> </p> <p>Source Code: https://colab.research.google.com/drive/18lS83mZEdwY5S-41pvcGXgUP6GbKpun_?usp=sharing</p>
Circular economy standards for automotive electronics: from a state of the art analysis to a new pre-standardization document
<p>The Standardization Toolkit, developed by <a href="https://www.uni.com/en/" target="_blank" rel="noopener">UNI (Italian Standards Body)</a> within the TREASURE project, is an open reserach tool designed to simplify reserach of international and european standards (EN and ISO) relevant to circular economy strategies in the automotive sector.</p> <p>This dynamic tool, powered by Microsoft Power BI, provides an extensive list of national, European, and international standards, detailing key information such as standard numbers, URLs for document retrieval, titles, publication years, and scopes.</p> <p>The Toolkit maps out 73 standards, including 61 current standards and 12 works in progress, overseen by prominent technical committees such as ISO and CEN. It covers crucial areas like life-cycle assessment, substance determination, and decision support frameworks, offering a valuable resource for stakeholders aiming to implement effective and sustainable practices.</p> <p>The Toolkit is freely available at <a href="https://www.treasureproject.eu/standardization-toolkit/">this link</a>.</p> <p>This publication includes the dateset behind the Standardization Toolkit, while D8.4 "Standardization Toolkit" describes the previous version of the mapping and the methodology behind the state of the art analysis.</p> <p>It has also been uploaded the D8.5 "Strategic standardization roadmap" which includes a description of the Standardization Toolkit, and the new pre-standardization document (CEN Workshop Agreement - CWA) developed within Treasure project. </p> <p> </p> <p><strong>Disclaimer:</strong> The standards retrieved from the Standardization Toolkit cannot be fully downloaded. The Toolkit serves to simplify the search for these standards and to provide an overview of what has been mapped during the TREASURE project’s standardization activities.</p>
HARTU AUTOMOTIVE DATASET
<p>This dataset is created using different automotion parts CAD models. There are four distinct datasets: part1, part2, mosaic and carboard boxes For each image, the location of each part is provided at the box level with labels in YOLO format. Each dataset is divided on train, val and test subsets. </p> <p><br>The HARTU project supports this work. This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101092100.</p>
Simulation of an Agile Automotive Assembly Lane
<p><strong>General Description</strong></p> <p>The provided collection of datasets comprises 14 simulated event logs of an flexible automotive assembly lane. Each event log is stored in a csv-file that can be easily converted to other standards such as the eXtensible Event Stream (XES) standard. For a more detailed explanation, we refer to the provided supplementary document.</p> <p> </p> <p><strong>Simulation</strong></p> <p>Starting from a base model, 13 different scenarios were implemented that cover changes in processing times, station operator availability, operator efficiency, or bottlenecks in the supply chain. Each scenario was implemented in CPN Tools and, in addition to the event log, we also provide the respective simulation model and configurations.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>Funded under the Excellence Strategy of the Federal Government and the Länder<em>. </em>Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC-2023 Internet of Production - 390621612. We also thank the Alexander von Humboldt (AvH) Stiftung for supporting our research.</p>
DARTER: Digital twins for Accessible Real Testing grounds for automotive Engineers and Researchers
<p>The DARTER dataset contains a sample (for now) of driving data (camera, LiDAR, IMU, steering wheel angle, etc.) without labels gathered at AstaZero's test track using the Chalmers ReVeRe lab's SnowFox test vehicle. DARTER stands for Digital twins for Accessible Real Testing grounds for automotive Engineers and Researchers. This research was possible thanks to the funds of a SAFER pre-study grant.</p> <p> </p> <p><strong>About DARTER:</strong></p> <p>Verification and validation (V&V) of Intelligent Transport Solutions and their components in real traffic is difficult (costs, passers-by, etc.) and running tests under all possible conditions (weather, traffic, etc.) is impossible. For these reasons, controlled proving grounds and simulations are being used to safely increase the coverage of V&V. </p> <p>The DARTER project, a SAFER pre-study, addresses two issues connected to these alternatives. On the one hand, the limited access to real proving grounds and their corresponding high-fidelity simulations of academic researchers, key in evaluating the benefits and social and environmental harms that technological advances can pose. On the other hand, the fidelity gap between the virtual and the real world, that prevents the usage of simulations at vehicle integration test level and needs to be understood and measured. </p> <p>SAFER pre-studies https://www.saferresearch.com/content/safer-pre-studies </p>
Testing Context-Aware Software Systems in the Automotive Domain: A Multi Vocal Literature Review Protocol and Dataset
<h2>Testing Context-Aware Software Systems in the Automotive Domain: A Multi Vocal Literature Review Protocol and Dataset</h2><p>A Multi-Vocal Literature Review (MVLR) is a form of a systematic review that includes grey literature in addition to peer-review literature (Garousi, Felderer, and Mäntylä 2019). The decision to justify an MVLR is drawn from the results of recent literature reviews, in particular the recent results from (Matalonga et al. 2022) where it is shown that there is little evidence in the white literature on the approaches to testing non-academic CASS Systems.</p><p>Previous academic works (including our Quasi-Systematic Literature Reviews and Rapid Reviews) operate under the following assumptions and observations:</p><ul><li>Assumption 1. CASS are widespread and being deployed for commercial or industrial use.</li><li>Observation 1. The software engineering and software testing communities have had time to adopt (or develop new) techniques to deal with the context and effects of testing software systems.</li><li>Observation 2. Academics have been able to work with software organizations to transfer or study the approaches used to test CASS, yet the published case studies we are aware of describe a partial picture of the overall adoption and approach of the problem.</li></ul><p>In spite of these assumptions and the availability of systematic literature review studies, there is little evidence of how software organizations are testing CASS.</p><h3>Research Goals</h3><h4><strong>Aim: </strong>To uncover evidence on how the automotive industry reports their working with the dynamic testing process regarding CASS.</h4><p>We use the term industry to broaden our scope to include stakeholders with an interest in the quality of CASS like NGOs, standard-setting organizations and regulation-setting organizations who can influence how software must be treated in different domains.</p><p>The following research questions convey the general interest of our enquiries. These are driven by our previous research and expectations on the sources.</p><ul><li><strong>RQ1</strong> Are there sources to support the understanding and indicate directions to deal with the problem of testing CASS?</li><li><strong>RQ2</strong> What are the challenges of using these dynamic testing process solutions?</li><li><strong>RQ3 </strong>How are the dynamic testing processes that deal with the context of CASS described in the sources?</li></ul><h3>Dataset </h3><p>This dataset contains the following artifacts:</p><ol><li><strong>Testing CASS MVLR Protocol.pdf</strong>: protocol containing the methodological details of performing the MVLR</li><li><strong>Sources Identification, Selection and Data Extraction.xlsx</strong>: spreadsheet used to record and control discovered and selected sources</li><li><strong>Extraction_Documents.zip</strong>: compressed file containing all data collection forms filled with data extracted from the</li><li><strong>MVLR_Analysis-Codebook.xlsx</strong>: listing of codes emerging from the collected data</li></ol><p> </p>
Data for: Automotive braking is a source of highly charged aerosol particles
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Data from: Organic composition of ultrafine particles formed from automotive braking
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Shares of stock during COVID 19 in automotive sector
<p>This data set includes stock information for the companies Tesla, Porsche, Nio and Ferrari for each day from the date 11/08/2019 to 11/08/2020. Specifically, it shows information about the opening, closing, maximum and minimum price of the session, as well as the volume, the dividends granted to investors and the presence of stock splits generated per day. This dataste has been created with the aim to analyze how the quotes have been evolving during the COVID-19 pandemic in the automotive sector.</p> <p>The AccionesSectorAutomovil.xlsx dataset contains 4 sheets (TESLA, PAH3.DE, NIO, RACE ) and 9 variables per sheet:</p> <p>- Fecha: date in dd/MM/yyyy format<br> - Abrir: value of the share at the market opening expressed in US dollars (USD)<br> - Max: maximum value of the share throughout the day expressed in USD<br> - Cierre*: value of the share at the close of the market expressed in USD<br> - Cierre ajus.*: estimated share value at market close, expressed in USD.<br> - Volumen: the amount of a specific asset invested in during a day.<br> - Dividends: money received by shareholders in the form of dividends that day.<br> - Stock Splits: Whether or not a stock split operation was carried out that day.</p> <p>For more information about the project visit the link on [Github](<a href="https://github.com/shiny-data-scientist/webscrap_pract_1/">https://github.com/paulamlago/Financial-Web-Scrapping</a>)</p>
PSS in the Automotive sector: Dataset and case study narratives
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Transformed raw data and R scripts for the paper Problem reports and team maturity in agile automotive software development published at CHASE2022
<p>Transformed raw data and R scripts for the paper Problem reports and team maturity in agile automotive software development published at CHASE2022</p> <p>15th International Conference on Cooperative and Human Aspects of Software Engineering, May 21–22, 2022, Pittsburgh, PA, USA}´</p>
Modeling Languages for Digital Twins - A Survey Among the German Automotive Industry
<p>This repository contains the replication package for the paper _Modeling Languages for Digital Twins: A Survey Among the German Automotive Industry_ by Jérôme Pfeiffer, Dominik Fuchß, Thomas Kühn, Robin Liebhart, Dirk Neumann, Christer Neimöck, Christian Seiler, Anne Koziolek, and Andreas Wortmann. <br>The paper has been submitted to the practice track of [MODELS 2024](https://conf.researchr.org/track/models-2024/models-2024-technical-track#Practice-Track).</p> <h3>Data</h3> <p>This replication package contains all information from the survey:<br>- `results.csv`: A csv version of all data exported from LimeSurvey (German). Personal information from the participants has been removed. This file can be imported to reproduce the extraction results described in our paper.<br>- `survey_german.pdf`: The pdf version of the original survey in German. <br>- `survey_german.md`: A markdown version of the original survey in German. <br>- `survey_english.md`: A markdown version of the survey translated into English. </p> <h3>Selection of participants and distribution</h3> <p>With both versions, the survey can be executed again with a different target audience in English or German. In our case we wanted to reach as much participants from diverse work areas as possible, where we invited the participants by email via an internal mailing list of 189 members of the SofDCar project. To improve the response rate, we implemented two deadline extensions from the initial one-month-long time frame with 2 weeks of additional response time. Together with the deadline extension, we sent a mail to inform and remind the members of the consortium of the survey.</p> <h3>Data extraction</h3> <p>In total, we had 96 participants, of which 43 completed the questionnaire. For incomplete survey responses, we took only the available answers and did not include the missing answers in our data analysis. For data analysis we utilized the commercial Tool IBM SPSS and custom python scripts.</p> <h2>Research Questions </h2> <p>- RQ1: How is the DT understood in the automotive industry?<br> - RQ1.1: For which phases of automotive development are DTs<br>important?<br> - RQ1.2: What are desired properties of DTs?<br> - RQ1.3: What are desired purposes of using DTs?<br> - RQ1.4: How do these purposes change in relation to different phases of automotive development?<br>- RQ2: Which modeling languages and modeling tools are currently employed in the automotive industry?<br> - RQ2.1: Which kinds of models are important during automotive development?<br> - RQ2.2: How important are which models in the phases of automotive development?<br> - RQ2.3: Which tools are used to create and maintain these models?</p>
Dataset and code for "Higher labor intensity in US automotive assembly plants after transitioning to electric vehicles"
<p>Dataset and code for the article: "<span>Higher labor intensity in US automotive assembly </span><span>plants after transitioning to electric vehicles" submitted to Nature Communications. </span></p>
Enabling Automated Integration of Architectural Languages:an Experience Report from the Automotive Domain
<p>Dataset for the journal "Enabling Automated Integration of Architectural Languages:an Experience Report from the Automotive Domain"</p>
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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.