In the present vehicle re-identification methods, it is hard to separate the samples near the decision boundaries in the embedding feature space because of the multi-view vehicle appearance. A large margin metric learning method was proposed to tackle the above problem. In the representation stage, a large margin loss function was presented to tackle the identity confusion issue among similar vehicles effectively. Meanwhile, an invader-defector sampler was adopted, which was used to find the hard samples in the training dataset to train the network specifically and speed up the training process. A kernelized re-rank method was adopted to further enhance the re-identification performance in the retrieval stage. Experiments on three common databases show that the proposed method can achieve a higher vehicle re-identification accuracy, and at the same time the time cost of training and the inference procedure are improved. Theoretical analysis and experiments also indicate that, the large margin metric learning method can mine the hard samples near the decision boundaries, and can solve the multi-view issue in the vehicle re-identification domain.