Low Computational Vehicle Lane Changing Prediction Using Drone Traffic Dataset

Safe autonomous driving assistance systems are actively being developed based on Convolutional Neural Network (CNN). Unlike understanding the road environment through the image viewed from the existing vehicle, it has the advantage of a drone image that can see a large area at once. It is used as safe driving assistance information by understanding the movements of various vehicles and predicting movement information according to time. In this paper, vehicle movement is predicted using LSTM by extracting vehicle time series information. Use YOLOv5 to detect the vehicle on the road. Road areas are collected as drone flight images. YOLOv5 is learned by labeling the vehicle through the collected image. Time-series vehicle movement information is extracted from the detected vehicle and the movement of each vehicle is predicted using the LSTM model. The predicted vehicle information is represented by an error through the MSE.