Multi-modal Sensor Fusion for Auto Driving Perception: A Survey
This survey addresses the problem of noisy data and misalignment in multi-modal sensor fusion for autonomous driving perception, offering a novel taxonomy to guide future research, though it is incremental as it builds on existing review work.
The paper provides a literature review of multi-modal fusion methods for autonomous driving perception, analyzing over 50 papers and proposing a new taxonomy based on fusion stages to categorize methods into two major and four minor classes.
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.