As one of the key technologies of intelligent transportation systems, vehicle re-identification (Re-ID) aims to retrieve the same vehicle from different monitoring scenes and plays an important role in building a safe and smart city. With the continuous development of computer vision, the Re-ID method of using supervised learning suffers from the problems of strong reliance on manual annotation in the training process and weak scene generalization ability, so unsupervised learning of vehicle Re-ID gradually becomes the focus of research in recent years. Firstly, the present mainstream vehicle Re-ID datasets and the commonly used model evaluation metrics are introduced. Then, latest unsupervised learning-based vehicle Re-ID methods are grouped into two categories: gene-rative adversarial networks and clustering algorithms according to the current research ideas. Starting from the problems of domain deviation, cross-view deviation and insufficient information of data samples, the former is further divided into