Multi-modal Fusion Technology based on Vehicle Information: A Survey
It identifies a gap in autonomous driving perception for researchers, but is incremental as it surveys existing work rather than proposing new solutions.
This survey addresses the underutilization of vehicle kinematic information (e.g., acceleration, speed) in multi-modal fusion for autonomous driving, highlighting its robustness and reliability compared to camera and LiDAR data, and reviews existing applications, methods, datasets, and future ideas to promote its use.
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.