A snow scene construction algorithm based on perception estimation was proposed to address the problems of difficulty in collecting data from existing snow scene sample libraries and limited sample sizes during the training of autonomous driving perception performance. The snow scene was divided into two models: snow-covered model and snow-line model, and a snow-covered plane construction algorithm based on spatial perception was proposed to analyze the subtle gradient changes in the image and estimate the preliminary snow area. The preliminary snow areas were refined using connected domain analysis, and the areas were fused with the original image to obtain snow-covered scene images. A snow scene construction algorithm based on a random snow-line model was proposed to generate different motion directions for snowflakes. Snow-covered model and snow-line model were integrated, and various basic snow-line forms were utilized to construct snow scenes. Experimental results from the comparison of multi-frame traffic video data show that, in the snowy scenes constructed using the snowy scene fusion method, as the amount of snow increases, both the interference information and the details of the image are significantly enhanced, subjectively approaching the actual snow scene. Moreover, objective evaluation metrics for snow image quality decrease with the increase of snow cover and snow-line density.