Vehicle detection is very vital to the assisted driving system. Due to the serious degradation of the foggy road scene, the vehicle information in the image is not obvious, resulting in missed detection and false detection problems in vehicle detection. Aiming at the above problems, a vehicle detection method in foggy weather combining millimeter-wave radar and machine vision is proposed. First, the dark channel dehazing algorithm is used to preprocess the image to improve the salience of vehicle information in the image under foggy conditions. Then, the knowledge distillation is used to improve the YOLOv5s algorithm, and the knowledge distillation is introduced into the feature extraction network of YOLOv5s, which is used in the target positioning and classification stages to calculate the distillation loss and backpropagate the loss to train a small network model to improve the detection speed while ensuring the accuracy of visual detection. Finally, the distance matching algorithm based on the search of potential target detection areas is used to compare the visual detection results and the millimeter-wave radar detection results decision-making fusion. Based on the type and distance of the detected target, the interference information and erroneous information is filtered out, and the targets with high confidence after fusion in millimeter-wave radar detection and visual detection is retained. Thereby, the accuracy of vehicle detection is improved. The experimental results show that the method has the highest detection accuracy rate of 92.8% and the recall rate of 90.7% in foggy weather, which can realize the detection of vehicles in foggy weather.