Abstract
Ensuring accurate traffic perception and road safety in complex urban environments remains a significant challenge. Advanced traffic monitoring increasingly relies on deep learning, which requires large data volumes. However, existing datasets are often limited to CCTV video footage or focus on dynamic scenarios captured by sensors mounted on ego vehicles. This narrow perspective reduces the effectiveness of comprehensive traffic monitoring, particularly for LiDAR sensors, which typically capture only the vehicle's viewpoint and miss critical areas such as intersections and pedestrian crossings. To address these limitations, we propose a holistic strategy for rapid data collection in urban settings using simulated 3D intersections. Our approach introduces a point cloud collection framework using static LiDAR sensors to provide a global view of the entire traffic scene. By incorporating randomized traffic patterns observed from multiple angles, this method generates a diverse, comprehensive dataset for object detection and instance segmentation, showcasing its advantages for benchmarking smart mobility applications.
Recommended Citation
E. Binshaflout et al., "Annotated 3D Point Cloud Dataset for Traffic Management in Simulated Urban Intersections," Proceedings IEEE International Symposium on Circuits and Systems, Institute of Electrical and Electronics Engineers, Jan 2025.
The definitive version is available at https://doi.org/10.1109/ISCAS56072.2025.11044184
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
3D simulation; Dataset; LiDAR; point cloud; traffic monitoring
International Standard Serial Number (ISSN)
0271-4310
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2025
Included in
Digital Communications and Networking Commons, Electrical and Computer Engineering Commons
