A Method of Identifying Collision Risk of Container Trucks in Port Terminal Areas under an Integrated Connected Vehicle BSM and Roadside Video Surveillance Data

Large container terminals involve high-frequency transportation activities, and limited visibility in areas of stack aisles and exchange zones may easily lead to crashes between port container trucks and facilities, operators, and other vehicles. To improve the trajectory tracking accuracy and driving safety perception ability of intelligent container trucks in the densely populated port areas, a method for identifying container truck collision risks by integrating Connected Vehicle Basic Safety Messages (BSM) and roadside video surveillance data is proposed. A YOLOv5s algorithm is used to extract target vehicles and operators within the video surveillance, and a non-maximum suppression anchor box is designed based on the large size characteristics of the target container to improve detection accuracy. A perspective transformation principle is used to convert the target pixel coordinates into geographical coordinates, and a Deep-SORT algorithm is applied to match the vehicle trajectory information of each frame image. An interactive multi-model method (IMM) is used to fuse video trajectory information and vehicle positioning data of on-board units (OBU), reducing observation errors during target maneuvering process. Based on the trajectory fusion results, a new trajectory conflict risk assessment model is proposed, which can monitor vehicle collision risks in real-time according to the relative motion state of the target container and surrounding target trajectories. The detection of the collision risk can be broadcasted in real-time to on-board terminals and operator terminals through roadside equipment under most of practical scenarios. Experimental results show that the Root Mean Square Error (RMSE) of the IMM adaptive tracking method is only 0.29 m, which is 81.05% lower than that of the on-board tracking trajectory. It verifies that fusing roadside surveillance video with vehicle BSM positioning data can overcome the problem of increased errors from the on-board positioning systems under the dense stack environments. Study results also show that the recall rate, precision, and accuracy of collision risk identification results (with a pre-set ETTC threshold of 2 s) is improved by 7.39%, 4.27%, and 2.50%, respectively. The results indicate that the proposed method can more accurately identify collision risks in cases of obstructed visibility, when compared to the previous methods only using on-board detection techniques.