European road safety has improved greatly in recent decades. However, the current numbers are still far away to reach the European Commission's road safety targets. In this context, Cooperative Intelligent Transport Systems (C-ITS) are expected to significantly improve road safety, traffic efficiency and comfort of driving, by helping the driver to make better decisions and adapt to the traffic situation. This paper puts forward two vision-based applications for traffic sign recognition (TSR) and real-time weather alerts, such as for fog-banks. These modules will support operators in road infrastructure maintenance tasks as well as drivers, giving them valuable information via C-ITS messages. Different state-of-the-art methods are analysed using both publicly available datasets (GTSB) as well as our own image databases (Ceit-TSR and Ceit-Foggy). The selected models for TSR implementation are based on Aggregated Chanel Features (ACF) and Convolutional Neural Networks (CNN) that reach more than 90% accuracy in real time. Regarding fog detection, an image feature extraction method on different colour spaces is proposed to differentiate sunny, cloudy and foggy scenes, as well as its visibility level. Both applications are already running in an onboard probe vehicle system.

Aeronautics, in the context of industry 4.0, is continuously evolving to respond to the market dynamics and has incorporated automation to many stages of aircraft manufacturing. However, most of the final assembly line processes are still done manually and remain a challenge. Virtual Reality (VR) technologies can be leveraged to study the incorporation of automation systems involving Human-Robot Coexistence (HRC) in assembly processes before the physical system is available, which is beneficial for increasing the productivity and identifying issues beforehand, thus, preventing unexpected costs. In this context, a VR simulation environment was developed with two innovative factors: (1) The possibility to evaluate multiple new automated and semi-automated cabin and cargo processes and select the best one in terms of specific Key Performance Indicators (KPIs) for a future implementation in the physical system and (2) the capability to study the ergonomics of the human worker inside the narrow space of the fuselage while assembling the parts and coexisting with robots, without compromising the worker's safety. The results show that most of the new proposed strategies improve the assembly time, worker cost, or ergonomics of the process, with an investment varying between 100 K and 200 K euros and ROI of 1-2 years.

Nowadays, many companies see augmented reality (AR) as an important tool to provide new services related to their products. However, many challenges remain to be solved for the widely adoption of this technology, such as the development of suitable authoring tools and real-time and robust algorithms for the detection and tracking of objects where the virtual annotations will be anchored in the real world. This paper presents a complete framework, called ARgitu, to generate and present virtual and augmented information, including the tools required for the development of new contents. To solve the object detection and tracking in complex industrial environments, we also propose a new monocular method for 3D non-Lambertian object recognition in arbitrary environments. The method is based on the current state-of-the-art chamfer matching approaches with a reduced computational effort while maintaining their accuracy.

Virtual reality (VR)- and augmented reality (AR)-based simulations are key technologies in Industry 4.0 which allow for testing and studying of new processes before their deployment. A simulator of industrial processes needs a flexible way in which to model the activities performed by the worker and other elements involved, such as robots and machinery. This work proposes a framework to model industrial processes for VR and AR simulators. The desk method was used to review previous research and extract the most important features of current approaches. Novel features include interaction among human workers and a variety of automation systems, such as collaborative robots, a broader set of tasks (including assembly and disassembly of components), flexibility of modeling industrial processes for different domains and purposes, a clear separation of process definition and simulator, and independence of specific programming languages or technologies. Three industrial scenarios modeled with this framework are presented: an aircraft assembly scenario, a guidance tool for high-voltage cell security, and an application for the training of machine-tool usage.

Simulators provide significant advantages in training operators of concrete spraying machinery, such as economic savings, the practical absence of safety risks, and environmental and educational benefits. The main challenge in developing a real-time training simulator for concrete spraying machinery lies in the modeling of shotcrete application. This article presents a novel method that models and simulates in real time the three main factors influencing shotcrete sprayability: adhesion, cohesion, and rebound. Furthermore, thanks to the addition of an obstacle model, the method makes it possible to spray onto additional supporting elements, which is a typical shotcrete application. The proposed method considers a wet-mix thick flow spraying process and is based on experiments that were run with a real concrete spraying machine and complemented by expert advice. The method was developed and evaluated using a user-centered methodology, resulting in realistic shotcrete application modeling that meets the needs for training concrete spraying machinery operators.

This chapter describes a surface reconstruction method that mixes interpolating and approximating features and its implementation in graphics hardware. Hybrid methods are useful in areas such sculpting, medicine, and cultural heritage, where details must be preserved. Such cases may also contain noise (due to sampling inaccuracies) or duplicated points (in the case of the scan is done from multiple points of view), where hybrid methods provide an interesting solution. The proposed method makes use of a point projection operator to create a regular distributed and noise free set of points, which is reconstructed using local Delaunay triangulations. Both points projection and triangulation methods are studied in its basic serial version, but aiming to design parallel versions (more specifically GPU implementations) that increase their performance. The adaptations required for the parallel reconstruction are discussed, and several implementation details are given.