The dataset contains high bandwidth voltage and current measurements of the main inverter of an electric vehicle. They were acquired from a Mercedes-Benz E-Vito on a testing ground in many different Operation Points (OP) listed in the following table:

Instructions: 

The dataset consists of 14 hdf5 files containing the measured data. In addition, there are two python examples on how to handle the data and plot same results, and one readme file.
The hdf5 dataset can be accessed with many different tools like matlab, octave or python. If you want to use the python example, you must place the python-file and the dataset in the same folder. A recent version of python (it was tested with Python 3.9.2) with the following packages is needed: h5py; matplotlib; numpy; random; os; sys and scipy.

Python demo:
There are two python example demos to read and plot the hdf5 datasets included:
The first one reads a single operation point and plots the data in the time and frequency domain. (import_hdf5_plot_single_demo.py)
The second one reads one dataset and calculates the short time Fourier transformation of all operations points in the dataset and plots a spectrogram. (import_hdf5_plot_dataset_demo.py)
The demo is made as an example on how to handle the data and can be used for further analysis.
The datasets can also be accessed via matlab/octave, but therefore I refer to the online support.
If you have any further question about the dataset, please contact the author.

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The proposed dataset, termed PC-Urban (Urban Point Cloud), is captured with an Ouster LiDAR sensor with 64 channels. The sensor is installed on an SUV that drives through the downtown of Perth, Western Australia (WA), Australia. The dataset comprises over 4.3 billion points captured for 66K sensor frames. The labelled data is organized as registered and raw point cloud frames, where the former has a different number of registered consecutive frames. We provide 25 class labels in the dataset covering 23 million points and 5K instances.

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98 Views

This dataset contains road networks used in experiments for DRL-Router, including Sioux Falls, Anaheim, Winnipeg and Barcelona.

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27 Views

The dataset collects the results of a survey of 325 respondents. Each respondent is asked to design a route from an origin to a destination taking into account the following considerations:

  • The route should avoid crowds to avoid getting COVID-19.
  • They should take into account the context provided: day, time, month, holiday period.

A total of 10 scenarios located in the city of Ciudad Real were designed.

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118 Views

Another raw ADS-B signal dataset with labels, the dataset is captured using a BladeRF2 SDR receiver @ 1090MHz with a sample rate of 10MHz

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118 Views

This dataset represents a subnetwork of public transportation in the city of Johannesburg. It contains counting of bus occupation of three significant routes as well as GPS location of Bus stations.

Instructions: 

The data can be freely downloaded. It is made available for academic purposes

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144 Views

Both passenger demand and service supply are among the most important factors that determine the performance of urban rail transit system. It is not easy to find out optimal solution for the match between the passenger demand and service supply with traditional methods, due to the complexity of the combinatorial intelligent supply — demand matching problem. In order to get the comprehensively optimal matching degree, this paper transforms the multi-criteria problem into the distributed artificial intelligence optimization by using multi-agent dynamic interaction technique.

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118 Views

This dataset is released with our research paper titled “Scene-graph Augmented Data-driven Risk Assessment of Autonomous Vehicle Decisions” (https://arxiv.org/abs/2009.06435). In this paper, we propose a novel data-driven approach that uses scene-graphs as intermediate representations for modeling the subjective risk of driving maneuvers. Our approach includes a Multi-Relation Graph Convolution Network, a Long-Short Term Memory Network, and attention layers.

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235 Views

<p>This is&nbsp;<span style="font-family: Verdana, Arial, Helvetica, sans-serif;">Charlotte street netowrk data.</span></p>

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39 Views

This dataset contains road network information of Chengdu with travel time data during four time slots: weekday peak hour, weekday off-peak hour, weekend peak hour and weekend off-peak hour.

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165 Views

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