With the increasing number of vehicles,traffic accidents pose a great threat to human lives.Hence,aiming at reducing the occurrence of traffic accidents,this paper proposes an algorithm based on a deep convolutional n...With the increasing number of vehicles,traffic accidents pose a great threat to human lives.Hence,aiming at reducing the occurrence of traffic accidents,this paper proposes an algorithm based on a deep convolutional neural network and a random forest to predict accident risks.Specifically,the proposed algorithm includes a feature extractor and a feature classifier,where the former extracts key features using a convolutional neural network and the latter outputs a probability value of traffic accidents using a random forest with multiple decision trees,which indicates the degree of accident risks.Simulations show that the proposed algorithm can achieve higher performance in terms of the Area Under the Curve(AUC)of the Receiver Characteristic Operator as well as accuracy than the existing algorithms based on the Adaboost or the pure convolutional neural networks.展开更多
基金supported by the National Key Research and Development Program(2019YFB2103004)the National Natural Science Foundation of China(No.61871446,92067201)+1 种基金the Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu(BK20212001)the Future Network Scientific Research Fund Project(FNSRFP-2021-ZD8,FNSRFP-2021-YB-31)。
文摘With the increasing number of vehicles,traffic accidents pose a great threat to human lives.Hence,aiming at reducing the occurrence of traffic accidents,this paper proposes an algorithm based on a deep convolutional neural network and a random forest to predict accident risks.Specifically,the proposed algorithm includes a feature extractor and a feature classifier,where the former extracts key features using a convolutional neural network and the latter outputs a probability value of traffic accidents using a random forest with multiple decision trees,which indicates the degree of accident risks.Simulations show that the proposed algorithm can achieve higher performance in terms of the Area Under the Curve(AUC)of the Receiver Characteristic Operator as well as accuracy than the existing algorithms based on the Adaboost or the pure convolutional neural networks.
