# End-to-End Automated Guided Modular Vehicle
###### Abstract  
Autonomous Vehicles (AVs) have caught people's attention in recent years, not only from an academic or developmental viewpoint but also because of the wide range of applications that these vehicles may entail, such as intelligent mobility and logistics, as well as for industrial purposes, among others. The open literature contains a variety of works related to the subject. They employ a diversity of techniques ranging from probabilistic to ones based on Artificial Intelligence. The increase in computing capacity, well known to many, has opened plentiful opportunities for the algorithmic processing needed by these applications, making way for the development of autonomous navigation, in many cases with astounding results. The following paper presents a low-cost but high-performance minimal sensor open architecture implemented in a modular vehicle. It was developed in a short period of time, surpassing many of the currently available solutions found in the literature. Diverse experiments were carried out in the controlled and circumscribed environment of an autonomous circuit that demonstrates the efficiency of the applicability of the developed solution.  
autonomous vehicle; intelligent transportation systems; deep learning; automated guided vehicle; end-to-end learning; self-driving cars +
Footnote †: journal: Appliedcooperative planning, validation, and evaluation. Data uncertainty and reliability represent the biggest challenges for data management. Without reliable data, the representation and modelling of the environment are at stake [5]. The topic of adaptive mobility relates to the introduction of self-driving cars to conventional environments.  
Therefore, the automation of the process is not simple. It is important to continue developing research in this area to improve upon the current technology surrounding autonomous vehicles, from the development of better sensors at a lower cost to the improvement of navigation algorithms with more advantageous designs, leading to the generation of more significant low-cost vehicular platforms.  
This paper presents the results obtained from the navigation automation of a modular vehicular platform, intending to generate a low-cost modular AGV. Considering the minimum instrumentation necessary in addition to the algorithms needed to achieve such automation, this work integrates several of the most popular software and hardware tools used nowadays. The Robot Operating System (ROS) was used as an integrating software for the different instrumentation modules and sensors that allow controlling the vehicle; Behavioral Cloning (BC), an end-to-end algorithm, was used because of its ability to replicate human behavior when navigating the vehicle through an established route.  
Numerous acronyms are frequently used in this work, which can be seen in Appendix A.  
This paper is organized as follows: The background overview section describes the related work and state-of-the-art developments in the field and compares them to the modular vehicle platform presented. The research approach presents the methodology used to develop the solution. The proposed system segment contains a thorough description of how the vehicle was instrumented, the minimum systems required to achieve Society of Automotive Engineers (SAE) Level 1 automation, and the test track. The development section reports how the stages in which the instrumentation, data acquisition, algorithm development, training, testing, and validation modules were developed over four weeks. The results segment presents the achievements of the AGV along with its performance metrics. The conclusion analyzes the problems faced and how they were solved along with the contributions of this work to areas of growth. Additionally, possible system upgrades are stated in the future work section.