Navigating through dense traffic situations like merging onto highways and making unprotected left turns remains a challenge for the existing autonomous driving system. Classifying vehicles into parked, stopped, and moving vehicles can benefit the decision-making system in this case because they play different roles during the vehicle-to-vehicle negotiation process. Existing works in vehicle classification focused on trivial cases and used methods that are not generalized enough. To fill this gap, after analyzing this problem and summarizing the necessary information needed for this problem, we propose a multi-modal model that can leverage information from lidar, radar, camera, and high-definition maps. To meet the complexity of our task and the needs of our model, we collect the dataset in real driving scenario and then preprocess and label it. By utilizing a pretrained vision encoder for fine-grained visual feature extraction and vision foundation model (CLIP) for scene understanding, our model achieves a 97.63% test accuracy on our dataset. Through visualization methods, experiments, and quantitative analyses, we investigate the effectiveness and importance of different encoders used in our model. We interpret and explain the successes and failures of our model to give a better understanding of how different latent features contribute to the final result. In the end, the limitations of our model and potential improvements are discussed.