  Autonomous vehicles rely on their perception systems to acquire information
about their immediate surroundings. It is necessary to detect the presence of
other vehicles, pedestrians and other relevant entities. Safety concerns and
the need for accurate estimations have led to the introduction of Light
Detection and Ranging (LiDAR) systems in complement to the camera or
radar-based perception systems. This article presents a review of
state-of-the-art automotive LiDAR technologies and the perception algorithms
used with those technologies. LiDAR systems are introduced first by analyzing
the main components, from laser transmitter to its beam scanning mechanism.
Advantages/disadvantages and the current status of various solutions are
introduced and compared. Then, the specific perception pipeline for LiDAR data
processing, from an autonomous vehicle perspective is detailed. The
model-driven approaches and the emerging deep learning solutions are reviewed.
Finally, we provide an overview of the limitations, challenges and trends for
automotive LiDARs and perception systems.
