It has been noted that numerous universities and companies worldwide are very active in these kinds of events, which indubitably will accelerate research on this field. From the academic perspective, several survey papers in this area have taken different approaches to review this field. [PERSON] et al. [2] compared several representative online Multi-Object Tracking (MOT) for AVs. Later on, the authors proposed M\({}^{3}\)OT (i.e., a 3D variation of MOT), an approach suitable for tracking objects in the real world. It is worth mentioning that M\({}^{3}\)OT can work with any type and number of cameras as long as they are calibrated. [PERSON] et al. [3] spanned the state-of-the-art technology in significant fields of self-driving systems, such as perception, mapping and localization, prediction, planning and control, simulation, etc. [PERSON] et al. [4] attempt to provide a structured and comprehensive overview of state-of-the-art automated driving-related hardware-software practices, high-level systems architectures, present challenges, datasets, and tools to ADS. [PERSON] et al. [5] published survey research on self-driving cars based on the aforementioned DARPA challenges. The authors presented a detailed description of the self-driving vehicle developed at the Universidade Federal do Espirito Santo in Brazil, named Intelligent Autonomous Robotics Automobile (IARA). This paper presents a complete overview of each of the aspects that compose the matured used architecture of self-driving cars nowadays, dividing it into two systems, i.e., perception and decision-making. At the same time, each design is composed of a variety of subsystems. Achieving its objective by describing each of the technologies superficially, this work represents a notable introduction to the interested reader. However, for a lettered person in the subject, this work's scope might fall short because it does not present the state-of-the-art technologies for driverless cars' different paradigms. Furthermore, the study of these techniques has been extended to other application domains beyond AVs on the highway. For instance, [PERSON] et al. [6] presented a comparative study where different deep learning architectures were tested to perform object classification in underwater AVs' video image processing. In this study, the authors evaluated different architectures' performance to classify objects (i.e., fish, other vehicles, divers and obstacles) and detect abandoned munitions' corrosion. Interestingly, they were able to deduct that pre-trained algorithms have higher probabilities of success than tailor-made approaches. This rationale will be explained and supported throughout our work. This paper provides a thorough state-of-the-art investigation on object detection methods for the ADS pipeline. In addition, distributed cloud computing and parallelization techniques are studied to understand how these can be incorporated to deploy these methods. Unlike other reviews, this study is centralized mainly in autonomous driving perception systems, focusing on the neural network (NN) concepts for object detection, and how the concept of a Distributed System (DS) for parallelization can be included within. The paper is structured as follows. Section 2 provides the basic definitions and background required to understand the domain, with concepts such as image acquisition, feature extraction, object detection, DS, and route calculation. Section 3 presents a thorough study of each of these concepts in light of the latest work presented in this regard. Section 4 introduces our proposed methodology to address the most recurrent issues of assisted driving and AVs based on our study and with considerable focus on parallelization for real-time deployment systems. Finally, Section 5 provides our final thoughts and comments on the subject.