Over the last period, the advancement of autonomous driving technology has made huge steps forward with regard to perception algorithms, i.e. the tools capable of translating sensory data into understandable data. This thesis project aims to delve into the different perception techniques present on the autonomous driving market. In particular, to achieve this goal, CARLA, a cutting-edge open source system for this topic, was used: the main focus is therefore to develop a perception algorithm, obtaining the best possible results in terms of performance within a simulated environment as similar as possible to a complex real-life condition. Understanding and being able to interpret the surrounding environment in real time is one of the most important aspects of autonomous driving, because all the subsequent choices made by the machine itself and consequently the success of the algorithm depend on it.