INtersection Dataset

Intersection datasets are collections of multi-modal sensor data (camera images, LiDAR point clouds, etc.) from road intersections, crucial for training and evaluating algorithms for autonomous driving and intelligent transportation systems. Current research focuses on developing efficient and accurate models, such as graph attention networks, for simulating and predicting traffic flow, as well as leveraging large multimodal models for real-time safety assessments, particularly for vulnerable road users. These datasets are vital for advancing research in areas like traffic optimization, automated driving safety, and the development of robust perception systems, ultimately contributing to safer and more efficient transportation networks.