The classification of Chinese traditional settlements(CTSs)is extremely important for their differentiated development and protection.The innovative double-branch classification model developed in this study comprehen...The classification of Chinese traditional settlements(CTSs)is extremely important for their differentiated development and protection.The innovative double-branch classification model developed in this study comprehensively utilized the features of remote sensing(RS)images and building facade pictures(BFPs).This approach was able to overcome the limitations of previous methods that used only building facade images to classify settlements.First,the features of the roofs and walls were extracted using a double-branch structure,which consisted of an RS image branch and BFP branch.Then,a feature fusion module was designed to fuse the features of the roofs and walls.The precision,recall,and F1-score of the proposed model were improved by more than 4%compared with the classification model using only RS images or BFPs.The same three indexes of the proposed model were improved by more than 2%compared with other deep learning models.The results demonstrated that the proposed model performed well in the classification of architectural styles in CTSs.展开更多
The realization of personalized lane-changing(LC)for intelligent vehicles(IVs)is important for enhancing the social acknowledgment,user acceptance,adaptability,and trust of IVs.The LC style classification of human dri...The realization of personalized lane-changing(LC)for intelligent vehicles(IVs)is important for enhancing the social acknowledgment,user acceptance,adaptability,and trust of IVs.The LC style classification of human drivers represents a crucial foundation for achieving personalized LC.Therefore,this study constructs an LC style classification method based on driving behavioral primitives,which enables the classified LC styles to fully embody the implicit behavioral semantics and patterns of human drivers.First,a disentangled sticky hierarchical Dirichlet process hidden Markov model is proposed for the LC behavioral segment segmentation.The model can suppress frequent transitions of the hidden states,and vector autoregression is used to accurately describe the LC explicit behavioral parameters.Subsequently,the K-shape is employed to cluster all LC behavior segments to obtain interpretable and reasonable LC behavior primitives.Then,clustering features based on the LC behavioral primitives are constructed.Finally,LC styles are classified using density peak clustering,which does not require a manual specification of the number of clustering centers.Verification is performed on the Next Generation Simulation dataset,and the results indicate that this method can accurately and reasonably classify LC styles.The quantitative comparison with four state-of-the-art methods further demonstrates the advantages of the proposed method in LC style classification and confirms the effectiveness of introducing LC behavioral primitives.展开更多
基金The Science and Technology Project of Hebei Education Department,No.BJK2022031The Open Fund of Hebei Key Laboratory of Geological Resources and Environmental Monitoring and Protection,No.JCYKT202310。
文摘The classification of Chinese traditional settlements(CTSs)is extremely important for their differentiated development and protection.The innovative double-branch classification model developed in this study comprehensively utilized the features of remote sensing(RS)images and building facade pictures(BFPs).This approach was able to overcome the limitations of previous methods that used only building facade images to classify settlements.First,the features of the roofs and walls were extracted using a double-branch structure,which consisted of an RS image branch and BFP branch.Then,a feature fusion module was designed to fuse the features of the roofs and walls.The precision,recall,and F1-score of the proposed model were improved by more than 4%compared with the classification model using only RS images or BFPs.The same three indexes of the proposed model were improved by more than 2%compared with other deep learning models.The results demonstrated that the proposed model performed well in the classification of architectural styles in CTSs.
基金jointly supported by the National Natural Science Foundation of China(52172386,52302494)the China Postdoctoral Science Foundation under Grant Number 2023M741339.
文摘The realization of personalized lane-changing(LC)for intelligent vehicles(IVs)is important for enhancing the social acknowledgment,user acceptance,adaptability,and trust of IVs.The LC style classification of human drivers represents a crucial foundation for achieving personalized LC.Therefore,this study constructs an LC style classification method based on driving behavioral primitives,which enables the classified LC styles to fully embody the implicit behavioral semantics and patterns of human drivers.First,a disentangled sticky hierarchical Dirichlet process hidden Markov model is proposed for the LC behavioral segment segmentation.The model can suppress frequent transitions of the hidden states,and vector autoregression is used to accurately describe the LC explicit behavioral parameters.Subsequently,the K-shape is employed to cluster all LC behavior segments to obtain interpretable and reasonable LC behavior primitives.Then,clustering features based on the LC behavioral primitives are constructed.Finally,LC styles are classified using density peak clustering,which does not require a manual specification of the number of clustering centers.Verification is performed on the Next Generation Simulation dataset,and the results indicate that this method can accurately and reasonably classify LC styles.The quantitative comparison with four state-of-the-art methods further demonstrates the advantages of the proposed method in LC style classification and confirms the effectiveness of introducing LC behavioral primitives.
