论文标题
多模式传感器融合用于自动驾驶感知：调查
Multi-modal Sensor Fusion for Auto Driving Perception: A Survey
论文作者
论文摘要
多模式融合是对自主驾驶系统感知的基本任务，该系统最近吸引了许多研究人员。但是，由于嘈杂的原始数据，未充分利用的信息以及多模式传感器的未对准，实现相当好的性能并不是一件容易的事。在本文中，我们对自动驾驶中现有的基于多模式的方法进行了文献综述。通常，我们进行了详细的分析，包括利用感知传感器在内的50多篇论文，包括LiDAR和试图解决对象检测和语义分割任务的相机。与传统的融合方法分类的融合模型不同，我们提出了一种创新的方式，将它们分为两个主要类别，在融合阶段的视图中通过更合理的分类法进行了四个次要类别。此外，我们深入研究了当前的融合方法，重点关注其余问题，并就潜在的研究机会进行开放讨论。总而言之，我们希望在本文中要做的是为自动驾驶感知任务提供多模式融合方法的新分类法，并激发人们对基于融合的技术的想法。
Multi-modal fusion is a fundamental task for the perception of an autonomous driving system, which has recently intrigued many researchers. However, achieving a rather good performance is not an easy task due to the noisy raw data, underutilized information, and the misalignment of multi-modal sensors. In this paper, we provide a literature review of the existing multi-modal-based methods for perception tasks in autonomous driving. Generally, we make a detailed analysis including over 50 papers leveraging perception sensors including LiDAR and camera trying to solve object detection and semantic segmentation tasks. Different from traditional fusion methodology for categorizing fusion models, we propose an innovative way that divides them into two major classes, four minor classes by a more reasonable taxonomy in the view of the fusion stage. Moreover, we dive deep into the current fusion methods, focusing on the remaining problems and open-up discussions on the potential research opportunities. In conclusion, what we expect to do in this paper is to present a new taxonomy of multi-modal fusion methods for the autonomous driving perception tasks and provoke thoughts of the fusion-based techniques in the future.
