Transportation

The GPS track data comes from the open datasets provided by Open ITS Open Alliance, and two open datasets provided by this organization are used in the experiment. The GPS track dataset1 was collected from the experimental road near Zhongshan University in Guangzhou, China, and dataset2 is located in the north-central region of Dongguan, China. The original GPS data contains several sampling frequencies (time intervals), from 10s - 360s. The city road network data used in the experiment is derived from the wiki map of the open-source project OSM.

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This dataset encompasses cycling experiments conducted on lithium-ion batteries, involving 5 distinct batches, each originating from a different manufacturer. Within each batch, there were 6 batteries subjected to testing. The battery types employed for testing included cylindrical 21700 cells and 18650 type and nickel manganese cobalt or nickel cobalt aluminum.

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Ensuring the safe and reliable operation of autonomous vehicles under adverse weather remains a significant challenge. 

To address this, we have developed a comprehensive dataset composed of sensor data acquired in a real test track and reproduced in the laboratory for the same test scenarios.

The provided dataset includes camera, radar, LiDAR, inertial measurement unit (IMU), and GPS data recorded under adverse weather conditions (rainy, night-time, and snowy conditions). 

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This dataset provides valuable insights into Received Signal Reference Power (RSRP) measurements collected from four User Equipment (UE) devices strategically positioned within a moving train. 

Additionally, it includes RSRP values obtained from an external reference source using the rooftop train antenna, all systematically interpolated on a cell and frequency basis, with both datasets available.

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827 Views

"LaneVisionIITR: A Comprehensive High-Resolution Dataset for Lane Detection recorded at IIT Roorkee ", which is a newly built high-resolution dataset for developing Lane detection dataset for advanced driver assistance systems. 

This folder consists of three files for each image: 

1. The image captured in .jpg format.

2. Annotations (.json) having left and center line coordinates represented as “L” and “C” respectively.

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This paper presents a vehicle side wireless synchronization controller suitable for high-power inductive power transfer (IPT) systems for electric vehicle (EV) wireless charging under stationary and semi-dynamic conditions. The objective of this work is to enable improved functionality, efficiency, and power density of high-power IPT systems under misaligned conditions. An existing 50 kW LCL-LCL tuned IPT system with dual active bridges (DAB) demonstrates the controller’s performance.

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This upload contains the data and scripts used for a research paper in preparation for publication. The paper presents a probabilistic resilience assessment framework where failure scenarios and network disruptions are generated using weather profile data from climate prediction models with component-level fragility functions. A case study is then carried out to quantify the resilience of Great Britain’s railway passenger transport system to high-temperature-related track buckling under the Representative Concentration Pathway 8.5 (RCP8.5) climate change scenario.

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This dataset is made for traditional, machine learning, and deep neural-network-based virtual sensor development and evaluation.

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Experimental measurement data was obtained utilizing RCbenchmark 1780 with full-range PWM signals. Measurements were made for two series of setups.

First series is related to low-voltage setups using the following T-MOTOR components: - motors: MN4014 400Kv, MN5212 340Kv, MN501-S 360Kv, U7 280Kv, MN6007 320Kv, P60 340Kv, MN701-S 280Kv; - ESC: Air 40A, Flame 40A, Flame 70A, Alpha 60A, Flame 100A; - propellers: P17×5.8, P18×6.1, P20×6, P22×6.6, P24×7.2, G26×8.5; - battery: 6-cell (6S) Lithium polymer (LiPo).

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