Vehicle re-identification technology plays a crucial role in intelligent transportation systems. Its accurate and efficient performance is decisive for significantly enhancing urban traffic safety and efficiency. However, complex weather conditions such as fog can lead to reduced imaging visibility, severely distorting vehicle appearance information. Existing vehicle re-identification methods still suffer from lower average precision and inadequate generalization capabilities under these conditions. To address these issues, a method that integrates multi-scale features for vehicle re-identification in foggy weather has been proposed. This method aims to enhance the re-identification accuracy and robustness on real-world data under foggy conditions. This method is divided into two branches: image dehazing and vehicle Re-ID. By leveraging the concept of shared weights, this approach balances the two tasks, enabling the model to extract stable and representative features from foggy images. The image dehazing module utilizes a two-stage restoration and pyramid enhancement technique to generate clear images, providing key features of vehicles in foggy conditions, there-by reducing the impact of haze on the accuracy of Re-ID. The vehicle Re-ID module leverages a feature pyramid and convolutional block attention mechanism to capture richer and more significant features across different scales, enhancing the entire branch’s Re-ID capability. Experiments were conducted on the FVRID dataset, comparing this method with various other Re-ID approaches. The results showed that on real-world data, the mean average precision reached 83.32%, and the cumulative matching characteristic at rank 1 was 94.70%. Both metrics outperformed other methods, indicating that the proposed multi-scale feature fusion method for foggy weather vehicle Re-ID significantly improves performance under such conditions, demonstrating stronger accuracy and generalization capability. This research not only advances the current state of technology for foggy weather vehicle re-identification but also provides valuable insights for future studies. As the demand increases for applications such as intelligent traffic management and autonomous driving systems, this improved re-identification method holds great promise for advancing these related fields.