applied sciences Article End-to-End Automated Guided Modular Vehicle Luis A. Curiel-Ramirez * , Ricardo A. Ramirez-Mendoza * , Rolando Bautista-Montesano , M. Rogelio Bustamante-Bello, Hugo G. Gonzalez-Hernandez , Jorge A. Reyes-Avedaño and Edgar Cortes Gallardo-Medina School of Engineering and Science, Tecnologico de Monterrey 64849, Mexico; [email protected] (R.B.-M.); [email protected] (M.R.B.-B.); [email protected] (H.G.G.-H.); [email protected] (J.A.R.-A.); [email protected] (E.C.G.-M.) * Correspondence: [email protected] (L.A.C.-R.); [email protected] (R.A.R.-M.) Received: 24 April 2020; Accepted: 19 June 2020; Published: 26 June 2020 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. Keywords: autonomous vehicle; intelligent transportation systems; deep learning; automated guided vehicle; end-to-end learning; self-driving cars 1. Introduction Advancements in computing power have allowed the development and implementation of more complex systems in vehicle development. The automotive industry has taken advantage of these advancements by creating and continuously improving advanced driver-assistance systems, such as automatic parking systems, road-assisted driving, and sometimes fully autonomous driving (AD) capacity [1]. Urban, commercial, and industrial vehicles have been enhanced with these systems and are commonly known as Automated Guided Vehicles (AGV). Many automotive manufacturers have self-driving car development programs, including Tesla and Toyota. Additionally, other companies, such as Uber and Waymo, focus on the Transport as a Service (TaaS) industry. Finally, automated vehicles are also being employed inside warehouses or factories to automate distribution and optimize processes—e.g., Amazon [2,3]. There is still a long way to go before seeing this technology incorporated into all industries and transport services. The task of controlling a vehicle is not an easy one; it is a task that sometimes even the human being himself finds complicated to perform. The developed prototypes of self-driving car systems are required to fit industry and academic standards in terms of performance, safety, and trust [2,3]. The stand-alone navigation problem seemed to be solved after the Defense Advanced Research Projects Agency (DARPA) Grand and Urban Challenge [4], however new problems popped up. They generated interest in the following research topics: data management, adaptive mobility, Appl. Sci. 2020, 10, 4400; doi:10.3390/app10124400 www.mdpi.com/journal/applsci Appl. Sci. 2020, 10, 4400 2 of 27 cooperative 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 AppendixA. 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. 2. Background Overview The development of technologies that automate driving in the last decade has structured specific definitions and methodologies that allow the effective implementation of vehicles with the capacity to navigate over complex environments. These definitions and methodologies are the basis of the current development of autonomous vehicles, and it is the same basis used for the development of the solution presented in this work. These concepts are levels of driving automation, approaches to achieving navigation, and learning methods for navigation. 2.1. Levels of Driving Automation Since 2014, the Society of Automotive Engineers (SAE) has published yearly the “SAE J3016 Levels of Driving Automation”, which defines six levels of automation ranging from 0, no automation, to 5, full vehicle autonomy [6–8]. Table1 briefly summarizes it. Appl. Sci. 2020, 10, 4400 3 of 27 Table 1. Levels of driving automation based on the Society of Automotive Engineers (SAE) J3016 [6–8]. SAE Levels Driver Tasks Vehicle Tasks Examples The driver has the main control The systems work only Blindspot warnings, lane 0 even though some of the as momentary assistance departure warnings, automated systems are engaged. providing warnings. parking alerts assistance. The vehicle has partial The driver must supervise the control and assistance in Lane centering OR 1 environment and the system to the steering OR adaptive cruise control. interfere as he requires it. brake/acceleration. The vehicle controls the Lane centering AND steering and 2 adaptive cruise control at brake/acceleration in the same time. specific circumstances. Passive passengers when automated driving is engaged. The vehicle has Traffic jam and highway 3 The driver needs to intervene autonomous driving in chauffeur. upon the system request. limited conditions and will not operate unless all Local driverless taxi 4 Passive passengers when the conditions are met. (pedals or steering wheel automated driving is engaged. is optional). Robo taxi or full The passenger never takes over The vehicle has driving autonomous car (pedals 5 control. control under all and steering wheel are conditions. not required). 2.2. Navigation Approaches By defining the levels of driving automation, it is possible to know the scope and limitations that a navigation system must have when driving the vehicle. The generation of navigation systems for AVs has been achieved in recent years through various approaches. Two main scopes aim to solve the navigation problem: Traditional approach: It focuses on the individual and specialized tasks for perception, navigation, • control, and actuation. The environment and the ego vehicle are modeled with the gathered data. Thus, for highly dynamic environments this approach is limited by the computing power, model complexity, and sensor quality. Robotic systems have used this modular solution for many years. Its complexity tends to be directly proportional to the model’s accuracy [2,3,9]. End-to-end approach (E2E): This scope allows users to generate a direct correlation of the • input data of a system with certain output data through the use of Deep Learning (DL). Artificial intelligence has been a breakthrough for AGVs. Machine learning (ML) serves to generate intelligent perception and control systems for robot navigation. Deep Learning (DL) is a state-of-the-art technique for creating models that resemble human behavior