Analysis of the Influence of Foggy Weather Environment on the Detection Effect of Machine Vision Obstacles 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 RCNN) 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 conditionxe2x80x94clear (no fog), light fog, medium fog, and heavy fogxe2x80x94by simulation experiment. The experimental results show that the detection recall is 91.55%, 85.21%, 72.5464.79%, 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.

A Convolutional Network for Joint Deraining and Dehazing from A Single Image for Autonomous Driving in Rain In this paper, we focus on a rain removal task from a single image of the urban street scene for autonomous driving in rain. We develop a Convolutional Neural Network which takes a rainy image as input, and directly recovers a clean image in the presence of rain streaks, atmospheric veiling effect (haze, fog, mist) caused by distant rain streak accumulation. We propose a synthetic dataset containing images of urban street scenes with different rain intensities, orientations and haziness levels for training and evaluation. We evaluate our method quantitatively and qualitatively on the synthetic data. Experiments show that our model outperforms stateoftheart methods. We also test our method qualitatively on the realworld data. Our model is fast and it takes 0.05s for an image of 1024 times 512. Our model can be seamlessly integrated with existing imagebased highlevel perception algorithms for autonomous driving in rain. Experiment results show that our deraining method improves semantic segmentation and object detection largely for autonomous driving in rain.