Transportation

This dataset provides valuable insights into Received Signal Reference Power (RSRP) measurements collected by User Equipment (UE) devices strategically positioned within a moving train, featuring the hexagonal frequency selective pattern on its windows. Additionally, it includes RSRP values obtained from an external reference source using the rooftop train antenna.

All the data in this dataset corresponds to the research conducted in our work titled  "Enhancing Mobile Communication on Railways: Impact of Train Window Size and Coating". 

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Bengaluru has been ranked the most congested city in India in terms of traffic for several years now. This hackathon is aimed at creating innovative solutions to the traffic management problem in Bengaluru, and is co-sponsored by the Bengaluru Traffic Police, the Centre for Data for Public Good, and the Indian Institute of Science (IISc). The hackathon will have two phases. The first phase will be about short-term traffic volume prediction, given video feeds from cameras installed at junctions.

Last Updated On: 
Thu, 06/20/2024 - 09:25
Citation Author(s): 
Raghu Krishnapuram, Rakshit Ramesh, and Arun Josephraj

This is the collection of the Ecuadorian Traffic Officer Detection Dataset. This can be used mainly on Traffic Officer detection projects using YOLO. Dataset is in YOLO format. There are 1862 total images in this dataset fully annotated using  Roboflow Labeling tool.  Dataset is split as follow, 1734 images for training, 81 images for validation and 47 images for testing. Dataset is annotated only as one class-Traffic Officer (EMOV). The dataset produced a Mean Average Precision(mAP) of 96.4 % using YOLOv3m, 99.0 % using YOLOv5x  and 98.10 % using YOLOv8x.

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Despite the existence of road image datasets, these datasets predominantly focus on European roads with less variability in traffic and road conditions. To address this limitation, we have developed an image dataset tailored to Indian road conditions, capturing the extensive variations in traffic and environment.

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This archive includes three popular traffic datasets: Abilene, GEANT, and TaxiBJ. 

Abilene and GEANT is network traffic datsets and TaxiBJ is urban traffic datset.

 

This archive includes three popular traffic datasets: Abilene, GEANT, and TaxiBJ. 

Abilene and GEANT is network traffic datsets and TaxiBJ is urban traffic datset.

 

This archive includes three popular traffic datasets: Abilene, GEANT, and TaxiBJ. 

Abilene and GEANT is network traffic datsets and TaxiBJ is urban traffic datset.

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DIRS24.v1 presents a dataset captured in campus environment. These images are curated suitably for the utilization in developing perception modules. These modules can be very well employed in Advanced Driver Assistance Systems (ADAS). The images of dataset are annotated in diversified formats such as COCO-MMDetection, Pascal-VOC, TensorFlow, YOLOv7-PyTorch, YOLOv8-Oriented Bounding Box, and YOLOv9.

 

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319 Views

X-CANIDS Dataset (In-Vehicle Signal Dataset)

In March 2024, one of our recent research "X-CANIDS: Signal-Aware Explainable Intrusion Detection System for Controller Area Network-Based In-Vehicle Network" was published in IEEE Transactions on Vehicular Technology. Here we publish the dataset used in the article. We hope our dataset facilitates further research using deserialized signals as well as raw CAN messages.

Real-world data collection. Our benign driving dataset is unique in that it has been collected from real-world environments.

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596 Views

PEMS03, PEMS04, PEMS07 and PEMS08, these four datasets are constructed from four areas in California. All the data was collected from the Caltrans Performance Measurement System (PeMS) and the spatial adjacency matrices for each dataset were built using a distance-based real road network. PEMS03 has 358 sensors with a duration of 3 months. PEMS04 has 307 sensors with a duration of 2 months. PEMS07 has 883 sensors with a duration of 3 months. PEMS08 has 170 sensors with a duration of 2 months.

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Safety of the Intended Functionality (SOTIF) addresses sensor performance limitations and deep learning-based object detection insufficiencies to ensure the intended functionality of Automated Driving Systems (ADS). This paper presents a methodology examining the adaptability and performance evaluation of the 3D object detection methods on a LiDAR point cloud dataset generated by simulating a SOTIF-related Use Case.

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