7 papers
Camera and LiDAR BEV Fusion for Cooperative 3D Object Detection on TUMTraf V2X
Muhammad Shahbaz, Shaurya Agarwal
We describe a Camera and LiDAR fusion detector developed for the TUMTraf V2X cooperative 3D object detection track of the DriveX 2026 challenge. The detector fuses three roadside c…
Physics-Informed Teacher-Student Ensemble Learning for Traffic State Estimation with a Varying Speed Limit Scenario
Archie J. Huang, Dongdong Wang, Shaurya Agarwal +3
Physics-informed deep learning (PIDL) neural networks have shown their capability as a useful instrument for transportation practitioners in utilizing the underlying relationship b…
Spatial-Temporal Nonlocal Traffic Dynamics: Analytical Properties, Adaptive Kernel Formulation, and Empirical Validation
Animesh Biswas, Archie Huang, Shaurya Agarwal +1
This paper presents a new spatial-temporal nonlocal traffic flow model formulated to overcome the boundedness limitations inherent in classical local formulations. The model introd…
UrbanTwin: Synthetic Roadside LiDAR Datasets
Muhammad Shahbaz, Shaurya Agarwal
This article presents UrbanTwin datasets, high-fidelity, realistic replicas of three public roadside lidar datasets: LUMPI, V2X-Real-IC, and TUMTraf-I. Each UrbanTwin dataset conta…
UrbanTwin: Building High-Fidelity Digital Twins for Sim2Real LiDAR Perception and Evaluation
Muhammad Shahbaz, Shaurya Agarwal
LiDAR-based perception in intelligent transportation systems (ITS) relies on deep neural networks trained with large-scale labeled datasets. However, creating such datasets is expe…
High-Fidelity Digital Twins for Bridging the Sim2Real Gap in LiDAR-Based ITS Perception
Muhammad Shahbaz, Shaurya Agarwal
Sim2Real domain transfer offers a cost-effective and scalable approach for developing LiDAR-based perception (e.g., object detection, tracking, segmentation) in Intelligent Transpo…