Multi-modal fusion is a basic task of autonomous driving system perception, which has attracted many scholars' attention in recent years. The current multi-modal fusion methods mainly focus on camera data and LiDAR data, but pay little attention to the kinematic information provided by the sensors of the vehicle, such as acceleration, vehicle speed, angle of rotation. These information are not affected by complex external scenes, so it is more robust and reliable. In this article, we introduce the existing application fields of vehicle information and the research progress of related methods, as well as the multi-modal fusion methods based on information. We also introduced the relevant information of the vehicle information dataset in detail to facilitate the research as soon as possible. In addition, new future ideas of multi-modal fusion technology for autonomous driving tasks are proposed to promote the further utilization of vehicle information.