This paper presents a systematic review of the perception systems and simulators for autonomous vehicles (AV). This work has been divided into three parts. In the first part, perception systems are categorized as environment perception systems and positioning estimation systems. The paper presents the physical fundamentals, principle functioning, and electromagnetic spectrum used to operate the most common sensors used in perception systems (ultrasonic, RADAR, LiDAR, cameras, IMU, GNSS, RTK, etc.). Furthermore, their strengths and weaknesses are shown, and the quantification of their features using spider charts will allow proper selection of different sensors depending on 11 features. In the second part, the main elements to be taken into account in the simulation of a perception system of an AV are presented. For this purpose, the paper describes simulators for model-based development, the main game engines that can be used for simulation, simulators from the robotics field, and lastly simulators used specifically for AV. Finally, the current state of regulations that are being applied in different countries around the world on issues concerning the implementation of autonomous vehicles is presented.

The complex decision-making systems used for autonomous vehicles or advanced driver-assistance systems (ADAS) are being replaced by end-to-end (e2e) architectures based on deep-neural-networks (DNN). DNNs can learn complex driving actions from datasets containing thousands of images and data obtained from the vehicle perception system. This work presents the classification, design and implementation of six e2e architectures capable of generating the driving actions of speed and steering wheel angle directly on the vehicle control elements. The work details the design stages and optimization process of the convolutional networks to develop six e2e architectures. In the metric analysis the architectures have been tested with different data sources from the vehicle, such as images, XYZ accelerations and XYZ angular speeds. The best results were obtained with a mixed data e2e architecture that used front images from the vehicle and angular speeds to predict the speed and steering wheel angle with a mean error of 1.06%. An exhaustive optimization process of the convolutional blocks has demonstrated that it is possible to design lightweight e2e architectures with high performance more suitable for the final implementation in autonomous driving.

The appearance of model-driven engineering (MDE) has invigorated research on domain-specific languages (DSLs) and automatic code generation. MDE uses models to build software, thereby displacing source code as the development process's main feature. DSLs provide easy, intuitive descriptions of the system using graphic models. In this new context, DSLs facilitate work in the first design stages. In addition, MDE helps reduce DSL development costs. It therefore represents a synergistic union that can significantly improve software development.

This article presents an integrated framework for the development of home automation systems following the model-driven approach. By executing model transformations the environment allows developers to generate executable code for specific platforms. The tools presented in this work help developers to model home automation systems by means of a domain specific language which is later transformed into code for home automation specific platforms. These transformations have been defined by means of graph grammars and template engines extended with traceability capabilities. Our framework also allows the models to be reused for different applications since a catalogue of requirements is provided. This framework enables the development of home automation applications with techniques for improving the quality of both the process and the models obtained. In order to evaluate the benefits of the approach, we conducted a survey among developers that used the framework. The analysis of the outcome of this survey shows which conditions should be fulfilled in order to increase reusability.

Requirements traceability is a technique intended to help determine the impact of changes in software design, support their integration, preserve knowledge, and assure the quality and correctness of the overall system. This paper presents an approach that considers traceability of safety requirements in the context of model-driven development of teleoperated services robots. The combination of the model-driven approach with safety requirements traceability makes it possible to construct systems using techniques for automatically identifying, managing, and mitigating risks so that these systems are safe enough to work in a particular environment. To secure the advantages of these mechanisms, we have developed a tool that provides users with traceability reports after applying model transformations. These reports enable developers to determine whether or not all safety requirements have been considered, the impact of changing a safety requirement, and how they are considered both in architectural decisions and code implementations.

In this paper, an augmented reality mirror application using vision-based human pose detection based on vision-based pose detection called ExerCam is presented. ExerCam does not need any special controllers or sensors for its operation, as it works with a simple RGB camera (webcam type), which makes the application totally accessible and low cost. This application also has a system for managing patients, tasks and games via the web, with which a therapist can manage their patients in a ubiquitous and totally remote way. As a final conclusion of the article, it can be inferred that the application developed is viable as a telerehabilitation tool, as it has the resource of a task mode for the calculation of the range of motion (ROM) and, on the other hand, a game mode to encourage patients to improve their performance during the therapy, with positive results obtained in this aspect.