  With autonomous driving developing in a booming stage, accurate object
detection in complex scenarios attract wide attention to ensure the safety of
autonomous driving. Millimeter wave (mmWave) radar and vision fusion is a
mainstream solution for accurate obstacle detection. This article presents a
detailed survey on mmWave radar and vision fusion based obstacle detection
methods. First, we introduce the tasks, evaluation criteria, and datasets of
object detection for autonomous driving. The process of mmWave radar and vision
fusion is then divided into three parts: sensor deployment, sensor calibration,
and sensor fusion, which are reviewed comprehensively. Specifically, we
classify the fusion methods into data level, decision level, and feature level
fusion methods. In addition, we introduce three-dimensional(3D) object
detection, the fusion of lidar and vision in autonomous driving and multimodal
information fusion, which are promising for the future. Finally, we summarize
this article.
