This study is to analyze the influence of visibility in a foggy weather environment on the accuracy of machine vision obstacle detection in assisted driving. We present a foggy day imaging model and analyze the image characteristics, then we set up the faster region convolutional neural network (Faster R-CNN) as the basic network for target detection in the simulation experiment and use Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) data for network detection and classification training. PreScan software is used to build weather and traffic scenes based on a foggy imaging model, and we study object detection of machine vision in four types of weather condition—clear (no fog), light fog, medium fog, and heavy fog—by simulation experiment. The experimental results show that the detection recall is 91.55%, 85.21%, 72.54~64.79%, and 57.75% respectively in no fog, light fog, medium fog, and heavy fog environments. Then we used real scenes in medium fog and heavy fog environment to verify the simulation experiment. Through this study, we can determine the influence of bad weather on the detection results of machine vision, and hence we can improve the safety of assisted driving through further research.

No abstract

The percutaneous endoscopic transpedicle approach is an effective technique for managing HNP in the lumbar hidden zone.

In order to study the influence of cooperative behavior in the evacuation process of subway station personnel, and considering the heterogeneity of evacuees, the heterogeneous cellular automata method is adopted to establish a human evacuation model of subway station under cooperative behavior based on the floor field model. In the research process, the evacuated persons are divided into two types, which are seeking cooperation and accepting cooperation. Then, the effects of different cooperative behavior probability ratios of seeking cooperative personnel on evacuation efficiency, evacuation process, and evacuation bottleneck are analyzed through simulation. The result shows that cooperative behavior can effectively improve evacuation efficiency of the subway station, but it is limited by cooperative probability and the proportion of people seeking cooperation; Cooperative behavior plays a role in the whole evacuation process, which is mainly reflected in the later stage of evacuation and will promote the gathering of evacuees. The higher the probability of cooperation, the shorter the evacuation bottleneck formation time, the duration, and overall evacuation time, which will help improve the emergency safety of subway stations.