*Article* **Ship Intention Prediction at Intersections Based on Vision and Bayesian Framework**

**Qianqian Chen <sup>1</sup> , Changshi Xiao 1,2,3,\* , Yuanqiao Wen 2,4, Mengwei Tao <sup>1</sup> and Wenqiang Zhan <sup>3</sup>**


3 Institute of Ocean Information Technology, Shandong Jiaotong University, Weihai 264200, China;


**Abstract:** Due to the high error frequency of the existing methods in identifying a ship's navigational intention, accidents frequently occur at intersections. Therefore, it is urgent to improve the ability to perceive ship intention at intersections. In this paper, we propose an algorithm based on the fusion of image sequence and radar information to identify the navigation intention of ships at intersections. Some existing algorithms generally use the Automatic Identification System (AIS) to identify ship intentions but ignore the problems of AIS delay and data loss, resulting in unsatisfactory effectiveness and accuracy of intention recognition. Firstly, to obtain the relationship between radar and image, a cooperative target composed of a group of concentric circles and a central positioning radar angle reflector is designed. Secondly, the corresponding relationship of radar and image characteristic matrix is obtained after employing the RANSAC method to fit radar and image detection information; then, the homographic matrix is solved to realize radar and image data matching. Thirdly, the YOLOv5 detector is used to track the ship motion in the image sequence. The visual measurement model based on continuous object tracking is established to extract the ship motion parameters. Finally, the motion intention of the ship is predicted by integrating the extracted ship motion features with the position information of the shallow layer using a Bayesian framework. Many experiments on real data sets show that our proposed method is superior to the most advanced method for ship intention identification at intersections.

**Keywords:** ship intention identification; AIS; RANSAC; Bayesian framework; YOLO; intersection
