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With the increasing number of private cars, traffic safety issues in rainy and foggy days have become an urgent problem to be solved. A voice assisted driving system for driving users in rainy and foggy weather based on machine vision is designed, with limited embedded hardware resources. The system combines humidity sensors, lightweight dehazing neural network AOD-NET, and object detection model YOLOv5n. On the object detection model YOLOv5n, K-means++ algorithm is used to redesign the anchor frame. A better backbone network and model pruning is selected to further compress the model size. The experimental results show that the FPS of the improved model on Jetson nano reached 17.78, and the final mAP reached 65.8% on the artificially fogged and resolution changed TT100K (Tsinghua-Tencent 100K) dataset, meeting the practical application of driving assistance in normal weather and rainy and foggy weather.