  Estimating and understanding the surroundings of the vehicle precisely forms
the basic and crucial step for the autonomous vehicle. The perception system
plays a significant role in providing an accurate interpretation of a vehicle's
environment in real-time. Generally, the perception system involves various
subsystems such as localization, obstacle (static and dynamic) detection, and
avoidance, mapping systems, and others. For perceiving the environment, these
vehicles will be equipped with various exteroceptive (both passive and active)
sensors in particular cameras, Radars, LiDARs, and others. These systems are
equipped with deep learning techniques that transform the huge amount of data
from the sensors into semantic information on which the object detection and
localization tasks are performed. For numerous driving tasks, to provide
accurate results, the location and depth information of a particular object is
necessary. 3D object detection methods, by utilizing the additional pose data
from the sensors such as LiDARs, stereo cameras, provides information on the
size and location of the object. Based on recent research, 3D object detection
frameworks performing object detection and localization on LiDAR data and
sensor fusion techniques show significant improvement in their performance. In
this work, a comparative study of the effect of using LiDAR data for object
detection frameworks and the performance improvement seen by using sensor
fusion techniques are performed. Along with discussing various state-of-the-art
methods in both the cases, performing experimental analysis, and providing
future research directions.
