The automatic recognition of traffic signs is essential to autonomous driving, assisted driving, and driving safety. Currently, convolutional neural network (CNN) is the most popular deep learning algorithm in traffic sign recognition. However, the CNN cannot capture the poses, perspectives, and directions of the image, nor accurately recognize traffic signs from different perspectives. To solve the problem, the authors presented an automatic recognition algorithm for traffic signs based on visual inspection. For the accuracy of visual inspection, a region of interest (ROI) extraction method was designed through content analysis and key information recognition. Besides, a Histogram of Oriented Gradients (HOG) method was developed for image detection to prevent projection distortion. Furthermore, a traffic sign recognition learning architecture was created based on CapsNet, which relies on neurons to represent target parameters like dynamic routing, path pose and direction, and effectively capture the traffic sign information from different angles or directions. Finally, our model was compared with several baseline methods through experiments on LISA (Laboratory for Intelligent and Safe Automobiles) traffic sign dataset. The model performance was measured by mean average precision (MAP), time, memory, floating point operations per second (FLOPS), and parameter number. The results show that our model consumed shorter time yet better recognition performance than baseline methods, including CNN, support vector machine (SVM), and regionbased fully convolutional network (R-FCN) ResNet 101.
This paper aims to disclose the reliability of driving behaviour in road traffic system. For this purpose, the drivers' electroencephalography (EEG) signals were collected with Emotiv, a portable device, and used for an experiment in actual driving environment. Through the analysis on the synchronization of 14-channel EGG signals, the author identified a proper threshold, and determined whether the brain network nodes are connected or not. On this basis, a brain network model was created for the drivers. The driving behaviour reliability of the drivers was discussed in detailed considering brain network parameters (clustering coefficient and global efficiency) and the power spectrum features of EEG signals. The research results show that, with the increase in driving time, the intercity drivers became increasingly fatigued and their brain network continued to densify, pushing up the network parameters like clustering coefficient and global efficiency. In this case, the neuronal activities became increasingly synchronized across the brain regions. In addition, the two brain network parameters of the drivers were less discrete and more accurate than the fatigue indicator of EEG power spectrum features. Therefore, the analysis of brain network parameters is a precise and feasible method for discussing driving behaviour reliability.
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