Datasets
Standard Dataset
Urban Mobility Research Dataset (Generated with the Quanser Interactive Lab)
- Citation Author(s):
-
Murat ArdaOnsu
University of OttawaPoonamLohanUniversity of OttawaBurakKantarciUniversity of OttawaAishaSyedNokia Bell Labs - Submitted by:
- Murat Onsu
- Last updated:
- Thu, 04/24/2025 - 16:35
- DOI:
- 10.21227/t4xs-rf05
- Data Format:
- Research Article Link:
- Links:
- License:
75 Views- Categories:
- Keywords:
Abstract
Fair Use for Academic Research: If you use this dataset, please cite the following paper to ensure proper attribution
M. A. Onsu, P. Lohan, B. Kantarci, A. Syed, M. Andrews, S. Kennedy, "Leveraging Multimodal-LLMs Assisted by Instance Segmentation for Intelligent Traffic Monitoring," 30th IEEE Symposium on Computers and Communications (ISCC), July 2025, Bologna, Italy.
Preprint available here: https://arxiv.org/pdf/2502.11304
It is an urban city environment dataset containing camera captions of 1898x1898 RGB color images. 4 different cameras are attached to various places in the environment to collect data in different driving scenarios.
Fair Use for Academic Research: If you use this dataset, please cite the following paper to ensure proper attribution
M. A. Onsu, P. Lohan, B. Kantarci, A. Syed, M. Andrews, S. Kennedy, "Leveraging Multimodal-LLMs Assisted by Instance Segmentation for Intelligent Traffic Monitoring," 30th IEEE Symposium on Computers and Communications (ISCC), July 2025, Bologna, Italy.
Preprint available here: [Bu arada bizim publication'larda C187 ve C188'in doi linkleri yanlis publication'lara
Dataset includes solely 1898x1898 RGB image data collected from 4 different cameras attached in various places in the Quanser interactive Lab environment. Using these data, we can observe the traffic flow, collision detection, pedestrians and vehicle activities, and so on. Various AI models, such as object detection, CNN, LLM, can be utilized to process these images, to understand the environment comprehensively.
Comments
Dataset is collected using QLab tools and can be applied many urban traffic scenario and traffic management tasks