  Multi-modal fusion is a basic task of autonomous driving system perception,
which has attracted many scholars' interest 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 bottom 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 paper, we introduce the existing application fields of
vehicle bottom information and the research progress of related methods, as
well as the multi-modal fusion methods based on bottom information. We also
introduced the relevant information of the vehicle bottom information data set
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 bottom information.
