Intersection Scenario
Intersection scenarios, encompassing diverse applications from traffic management to object detection and 3D vision, focus on improving efficiency and safety where multiple entities converge. Current research utilizes various approaches, including reinforcement learning for traffic control, deep learning models (like UNet and DETR variants) for image processing and object tracking, and novel loss functions (e.g., Gr-IoU, Focaler-IoU) to enhance accuracy. These advancements are significant for improving autonomous systems, optimizing urban infrastructure, and enhancing the reliability of machine learning models in complex environments.
Papers
Leveraging Large Language Models (LLMs) for Traffic Management at Urban Intersections: The Case of Mixed Traffic Scenarios
Sari Masri, Huthaifa I. Ashqar, Mohammed Elhenawy
Data-Driven Traffic Simulation for an Intersection in a Metropolis
Chengbo Zang, Mehmet Kerem Turkcan, Gil Zussman, Javad Ghaderi, Zoran Kostic