Transportation

SWAN is a Large-Scale Outdoor Point Cloud semantic segmentation dataset .  The dataset is targeted explicitly at the challenging urban environment, which aligns well with the needs of the intelligent transportation systems. The data is collected in the Central Business District (CBD) of Perth city in Australia, covering nearly 150km. It additionally used specialized equipment (portable trolley) to capture scenes of no-through roads and narrow streets.

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Taxi trajectories in New York City

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The dataset is available in GitHub and the link is given below:

https://github.com/sahilarora3117/capstone-19BCE1366-2023

 

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The dataset for the vehicles collusion detection in internet of vehicles is generated from internet of vehicles enviornment using VSSIM. The detail procedure of the data collection and the preprocessing procedure can be found in the publihsed paper - https://doi.org/10.1016/j.eswa.2022.119033.

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In nighttime driving scenes, due to insufficient and uneven lighting, and the scarcity of high-quality datasets, the miss rate of nighttime pedestrian detection (PD) is much higher than that of daytime. Vision-based distance detection (DD) has the advantages of low cost and good interpretability, but the existing methods have low precision, poor robustness, and the DD is mostly performed independently of PD.

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This dataset is in support of my research paper - Short Circuit Analysis of 666 Wh Li-Ion NMC

 Faults and datasets can be copied to submit in fire cause investigation reports or thesis. The simulation is run for 20 hours (72000 seconds) of simulation time for each fault of 100 faults. 

PrePrint : (Make sure you have read Caution.)

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This dataset includes all the test traces used in the paper. The image shows what looks like inside the zip file.

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For academic purposes, we are happy to release our datasets. This dataset is in support of my research paper 'TOW-IDS: Intrusion Detection System based on Three Overlapped Wavelets in Automotive Ethernet'. If you want to use our dataset for your experiment, please cite our paper.

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Future mobile communication systems include millimeter wave (mmWave) frequency bands and high mobility scenarios. To learn how wave propagation and scattering effects change from classical sub 6 GHz to mmWave frequencies, measurements in both bands have to be conducted. We perform wireless channel measurements at 2.55 GHz and 25.5 GHz center frequency at velocites of 40 km/h and 100 km/h. To ensure a fair comparison between these two frequency bands, we perform repeatable measurements in a controlled environment.

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In recent years, it has become more difficult to identify road traffic signage and panel guide material. Few studies have been made to solve these two issues at the same time, especially in the Arabic language. Additionally, the limited number of datasets for traffic signs and panel guide content makes the investigation more interesting. the Tunisian research groups in intelligent machines of the University of Sfax (REGIM laboratory of Sfax) will provide the NaSTSArLaT dataset free to researchers in traffic detection signs and traffic road scene text detection.

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