Multi-modal Fusion Technology based on Vehicle Information: A Survey

TL;DR

This survey reviews multi-modal fusion in autonomous driving, emphasizing vehicle bottom sensor data like acceleration and speed, which are robust and underutilized, and discusses future research directions.

Contribution

It comprehensively summarizes existing multi-modal fusion methods incorporating vehicle bottom information and proposes future research ideas to enhance autonomous driving perception.

Findings

Vehicle bottom information is robust against external scene changes.

Current fusion methods mainly focus on camera and LiDAR data.

Future fusion strategies should integrate bottom sensor data more effectively.

Abstract

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.