论文标题
基于车辆信息的多模式融合技术：调查
Multi-modal Fusion Technology based on Vehicle Information: A Survey
论文作者
论文摘要
多模式融合是自主驾驶系统感知的一项基本任务，近年来吸引了许多学者的兴趣。当前的多模式融合方法主要集中于相机数据和激光雷达数据，但很少关注车辆底部传感器提供的运动学信息，例如加速度，车辆速度，旋转角度。这些信息不受复杂的外部场景的影响，因此它更加稳健和可靠。在本文中，我们介绍了车辆底部信息的现有应用程序和相关方法的研究进度，以及基于底部信息的多模式融合方法。我们还详细介绍了车辆底部信息数据集的相关信息，以尽快促进研究。此外，提出了用于自动驾驶任务的多模式融合技术的新思想，以促进对车辆底部信息的进一步利用。
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.
