  Visual perception plays an important role in autonomous driving. One of the
primary tasks is object detection and identification. Since the vision sensor
is rich in color and texture information, it can quickly and accurately
identify various road information. The commonly used technique is based on
extracting and calculating various features of the image. The recent
development of deep learning-based method has better reliability and processing
speed and has a greater advantage in recognizing complex elements. For depth
estimation, vision sensor is also used for ranging due to their small size and
low cost. Monocular camera uses image data from a single viewpoint as input to
estimate object depth. In contrast, stereo vision is based on parallax and
matching feature points of different views, and the application of deep
learning also further improves the accuracy. In addition, Simultaneous Location
and Mapping (SLAM) can establish a model of the road environment, thus helping
the vehicle perceive the surrounding environment and complete the tasks. In
this paper, we introduce and compare various methods of object detection and
identification, then explain the development of depth estimation and compare
various methods based on monocular, stereo, and RDBG sensors, next review and
compare various methods of SLAM, and finally summarize the current problems and
present the future development trends of vision technologies.
