Dual‐Path Transformer Fusion Model for Traffic Accident Prediction Using Dashboard Cameras

ABSTRACT Traffic accident prediction using dashboard cameras is of great importance in the safety of self‐driving systems and the mitigation of accidents. This is because the prediction of accidents early is not easy because of the complex traffic conditions and the large variety of different object movements. In order to solve this problem, a new model of predicting traffic accidents will be suggested based on the Dual‐Path transformer fusion (DualTF) model. Video itself is divided into time and space. There is one frame that contains the background and position of the object information, which is spatial information. The camera and object movement through various frames is what gives the time information. The proposed DualTF model will detect the co‐occurring objects on the road using the Unified Transformer Framework. The Optical Flow is obtained by using the FlowFormer. Subsequently, the space and time characteristics will be obtained from co‐occurring objects and optical flow. The characteristics will then be amalgamated to predict traffic accidents. Lastly, the proposed approach has a greater degree of performance compared to other approaches and achieves an accuracy of 99.5%.