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14 results for “Automotive Data”
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>
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>
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>
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>
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>
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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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>
Data from manuscript: Assessment of the 7075-T6-aluminum-alloy/microalloyed-dual-phase-steel joint for automotive chassis
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Dataset from "A systematic dataset generation technique applied to data-driven automotive aerodynamics"
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Inventory data for LCA & LCC of ECOBULK's automotive sector demonstrator
<p>Inventory data gathered from ECOBULK project participants to perform LCA & LCC analyses.</p>
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