Speeding is one of the primary contributors to rural road crashes.Self-explaining theory offers a solution to reduce speeding,which suggests that well-designed facility environments(i.e.,road facilities and surroundin...Speeding is one of the primary contributors to rural road crashes.Self-explaining theory offers a solution to reduce speeding,which suggests that well-designed facility environments(i.e.,road facilities and surrounding landscapes)can automatically guide drivers to choose appropriate speeds on different road categories.This study proposes an improved lightweight convolutional neural network(LW-CNN)that includes drivers’visual perception characteristics(i.e.,depth perception and dynamic vision)to conduct the self-explaining analysis of the facility environment on 2-lane rural roads.Data for this study are gathered through naturalistic driving experiments on 2-lane rural roads across five Chinese provinces.A total of 3502 visual facility environment images,alongside their corresponding operation speeds and speed limits,are collected.The improved LW-CNN exhibits high accuracy and efficiency in predicting operation speeds with these visual facility environment images,achieving a train loss of 0.05%and a validation loss of 0.15%.The semantics of facility environments affecting operation speeds are further identified by combining this LW-CNN with the gradient-weighted class activation mapping(Grad-CAM)algorithm and the semantic segmentation network.Then,six typical 2-lane rural road categories perceived by drivers with different operation speeds and speeding probability(SP)are sum-marized using k-means clustering.An objective and comprehensive analysis of each category’s semantic composition and depth features is conducted to evaluate their influence on drivers’speeding probability and road category perception.The findings of this study can be directly used to optimize facility environments from drivers’visual perception to decrease speeding-related crashes.展开更多
A novel no-reference(NR) image quality assessment(IQA) method is proposed for assessing image quality across multifarious distortion categories. The new method transforms distorted images into the shearlet domain usin...A novel no-reference(NR) image quality assessment(IQA) method is proposed for assessing image quality across multifarious distortion categories. The new method transforms distorted images into the shearlet domain using a non-subsample shearlet transform(NSST), and designs the image quality feature vector to describe images utilizing natural scenes statistical features: coefficient distribution, energy distribution and structural correlation(SC) across orientations and scales. The final image quality is achieved from distortion classification and regression models trained by a support vector machine(SVM). The experimental results on the LIVE2 IQA database indicate that the method can assess image quality effectively, and the extracted features are susceptive to the category and severity of distortion. Furthermore, our proposed method is database independent and has a higher correlation rate and lower root mean squared error(RMSE) with human perception than other high performance NR IQA methods.展开更多
基金supported by the National Natural Science Foundation of China(No.52102416)the Natural Science Foundation of Shanghai(No.22ZR1466000)the Fundamental Research Funds for the Central Universities of China(No.22120240159).
文摘Speeding is one of the primary contributors to rural road crashes.Self-explaining theory offers a solution to reduce speeding,which suggests that well-designed facility environments(i.e.,road facilities and surrounding landscapes)can automatically guide drivers to choose appropriate speeds on different road categories.This study proposes an improved lightweight convolutional neural network(LW-CNN)that includes drivers’visual perception characteristics(i.e.,depth perception and dynamic vision)to conduct the self-explaining analysis of the facility environment on 2-lane rural roads.Data for this study are gathered through naturalistic driving experiments on 2-lane rural roads across five Chinese provinces.A total of 3502 visual facility environment images,alongside their corresponding operation speeds and speed limits,are collected.The improved LW-CNN exhibits high accuracy and efficiency in predicting operation speeds with these visual facility environment images,achieving a train loss of 0.05%and a validation loss of 0.15%.The semantics of facility environments affecting operation speeds are further identified by combining this LW-CNN with the gradient-weighted class activation mapping(Grad-CAM)algorithm and the semantic segmentation network.Then,six typical 2-lane rural road categories perceived by drivers with different operation speeds and speeding probability(SP)are sum-marized using k-means clustering.An objective and comprehensive analysis of each category’s semantic composition and depth features is conducted to evaluate their influence on drivers’speeding probability and road category perception.The findings of this study can be directly used to optimize facility environments from drivers’visual perception to decrease speeding-related crashes.
基金supported by the National Natural Science Foundation of China(No.61405191)the Jilin Province Science Foundation for Youths of China(No.20150520102JH)
文摘A novel no-reference(NR) image quality assessment(IQA) method is proposed for assessing image quality across multifarious distortion categories. The new method transforms distorted images into the shearlet domain using a non-subsample shearlet transform(NSST), and designs the image quality feature vector to describe images utilizing natural scenes statistical features: coefficient distribution, energy distribution and structural correlation(SC) across orientations and scales. The final image quality is achieved from distortion classification and regression models trained by a support vector machine(SVM). The experimental results on the LIVE2 IQA database indicate that the method can assess image quality effectively, and the extracted features are susceptive to the category and severity of distortion. Furthermore, our proposed method is database independent and has a higher correlation rate and lower root mean squared error(RMSE) with human perception than other high performance NR IQA methods.
