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Rear-end collision prediction has gained an increasing attention for safety improvement in smart cities. It is urgent to design efficient warning strategies for rear-end collisions which is one of the main causes of traffic accidents. The existing researches have been conducted to predict the collisions. Learning-based methods are proposed to solve this complicated issue, which the traditional methods are difficult to solve. However, due to some limitations in terms of the feature extraction and prediction performance, back-propagation learning methods are facing challenge. In this paper, we proposed a novel Rear-end Collision Prediction Mechanism with deep learning method (RCPM), in which a convolutional neural network model is established. In RCPM, the dataset is smoothed and expanded based on genetic theory to alleviate the class imbalance problem. The preprocessed dataset is divided into training and testing sets as the input to train our convolutional neural network model. The experimental results show that compared with the Honda, Berkeley and multi-layer perception neural-network-based algorithms, RCPM effectively improves performance to predict rear-end collisions. [ABSTRACT FROM AUTHOR]

In zero-shot learning, attributes play as a bridge from original images to class labels. Therefore, to achieve accurate zero-shot image classification, we mainly focus on improving attribute prediction accuracy by taking full advantage of prior information about attribute from two aspects. First, we present a new attribute classifier called deep attribute prediction (DeepAP) model by using supervised deep convolutional neural networks (DCNNs), where the attribute label information participates in the training of DCNNs. Unlike common DCNNs that are usually used to extract image features, the constructed DCNNs are used to directly predict attribute values from the original input images. Thus, the designed DeepAP model can serve as the mapping from low-level image features to high-level semantic attributes in the traditional direct attribute prediction (DAP) model. Second, another prior information about attribute, i.e., class-attribute matrix is used to mine the attribute-class correlation with sparse representation coefficients. Since the attribute-class correlation can reflect different contributions of attributes to classification, we use it to define attribute weights and incorporate the idea of weighted attributes into DeepAP to form the deep weighted attribute prediction (DWAP) model. Experiments on three real datasets show that DWAP outperforms the deep attribute network and DAP on attribute prediction and zero-shot image classification. [ABSTRACT FROM AUTHOR]