A Roundabout Video Dataset for Vehicle Trajectory Prediction

Predicting vehicle trajectories at roundabouts is crucial for road safety, as it enables advanced driver-assistance systems (ADAS) and autonomous vehicles to anticipate and respond to other drivers' intentions effectively. This capability enhances situational awareness, reduces the risk of collisions, and contributes to smoother traffic flow. This paper presents an open-source dataset designed to predict vehicle turning intentions at roundabouts, integrating YOLOv8 for object detection and DeepSORT for multi-target tracking. The dataset includes vehicle timestamps, pixel coordinates and heading angles, utilizing monocular ranging to map vehicles to an actual coordinate system. It supports real-time collision prediction, driver alerts, and the detection of abnormal behaviors such as sudden lane changes or harsh braking. This dataset contributes to the development of safer and more efficient traffic management and autonomous driving systems. The dataset is available for public access on GitHub<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>Details of the roundabout video dataset can be found in: https://github.com/zhoudashi2016/Roundabout-Video-Dataset-for-ITS..