Transfer Learning-Based Vehicle Collision Prediction

Abstract

Traffic accident is an important problem in modern society. Vehicle collision prediction is one of the key technical points that must be broken through in the future driving system. However, due to the complexity of traffic environment and the difference of emergency ability of drivers, it is very difficult to predict vehicle collision. Although experts and scholars have tried to monitor and predict accidents in real time according to environmental conditions, overly agile warning or inaccurate prediction may cause serious consequences. Therefore, in order to more accurately predict the occurrence of vehicle collision, this paper analyses and models the driving mode of the vehicle based on transfer learning and using the previous performance data of the vehicle, so as to predict the future collision situation and even the collision time of the vehicle. Finally, using a real-world Internet of Vehicles data set, this paper implements a large number of experiments to verify the effectiveness of the proposed model.