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.

Internet of vehicles is a specific application of Internet of things technology in the field of intelligent transportation. The rapid development of 5G communication technology promotes the development of Internet of vehicles. Car for cellular network communication node random distribution and complex multi-source interference and mobile terminal security calculation ability is limited, this article in view of the actual scene, was proposed based on random geometry contains eavesdropper (Eve) honeycomb-V2V heterogeneous physical layer security system model, the introduction of automatic (PB) as artificial floating vehicle noise, the analysis of cellular network users in the system (CU), V2V users (VU) and interference of the eavesdropper, each user letter simulation with dry to noise ratio (SINR) of the cumulative distribution function, and by using the random geometry tools related safety expression deduction, Then, data mining was carried out on the distance between PB and VU receiver, PB transmitting power and other related variables through genetic algorithm, and the value process was visualized to extract valuable information, providing a mathematical analysis framework and theoretical guidance for the future design, deployment and operation of cellular vehicle network. The results show that the proposed system model can significantly improve the security of vehicle-network communication.

As an advanced machine vision task, traffic sign recognition is of great significance to the safe driving of autonomous vehicles. Haze has seriously affected the performance of traffic sign recognition. This paper proposes a dehazing network, including multi-scale residual blocks, which significantly affects the recognition of traffic signs in hazy weather. First, we introduce the idea of residual learning, design the end-to-end multi-scale feature information fusion method. Secondly, the study used subjective visual effects and objective evaluation metrics such as Visibility Index (VI) and Realness Index (RI) based on the characteristics of the real-world environment to compare various traditional dehazing and deep learning dehazing method with good performance. Finally, this paper combines image dehazing and traffic sign recognition, using the algorithm of this paper to dehaze the traffic sign images under real-world hazy weather. The experiments show that the algorithm in this paper can improve the performance of traffic sign recognition in hazy weather and fulfil the requirements of real-time image processing. It also proves the effectiveness of the reformulated atmospheric scattering model for the dehazing of traffic sign images.