Ontology based Scene Creation for the Development of Automated Vehicles

The introduction of automated vehicles without permanent human supervision demands a functional system description, including functional system boundaries and a comprehensive safety analysis. These inputs to the technical development can be identified and analyzed by a scenario-based approach. Furthermore, to establish an economical test and release process, a large number of scenarios must be identified to obtain meaningful test results. Experts are doing well to identify scenarios that are difficult to handle or unlikely to happen. However, experts are unlikely to identify all scenarios possible based on the knowledge they have on hand. Expert knowledge modeled for computer aided processing may help for the purpose of providing a wide range of scenarios. This contribution reviews ontologies as knowledge-based systems in the field of automated vehicles, and proposes a generation of traffic scenes in natural language as a basis for a scenario creation.Comment: Accepted at the 2018 IEEE Intelligent Vehicles Symposium, 8 pages, 10 figure

SANTO: Social Aerial NavigaTion in Outdoors

In recent years, the advances in remote connectivity, miniaturization of electronic components and computing power has led to the integration of these technologies in daily devices like cars or aerial vehicles. From these, a consumer-grade option that has gained popularity are the drones or unmanned aerial vehicles, namely quadrotors. Although until recently they have not been used for commercial applications, their inherent potential for a number of tasks where small and intelligent devices are needed is huge. However, although the integrated hardware has advanced exponentially, the refinement of software used for these applications has not beet yet exploited enough. Recently, this shift is visible in the improvement of common tasks in the field of robotics, such as object tracking or autonomous navigation. Moreover, these challenges can become bigger when taking into account the dynamic nature of the real world, where the insight about the current environment is constantly changing. These settings are considered in the improvement of robot-human interaction, where the potential use of these devices is clear, and algorithms are being developed to improve this situation. By the use of the latest advances in artificial intelligence, the human brain behavior is simulated by the so-called neural networks, in such a way that computing system performs as similar as possible as the human behavior. To this end, the system does learn by error which, in an akin way to the human learning, requires a set of previous experiences quite considerable, in order for the algorithm to retain the manners. Applying these technologies to robot-human interaction do narrow the gap. Even so, from a bird's eye, a noticeable time slot used for the application of these technologies is required for the curation of a high-quality dataset, in order to ensure that the learning process is optimal and no wrong actions are retained. Therefore, it is essential to have a development platform in place to ensure these principles are enforced throughout the whole process of creation and optimization of the algorithm. In this work, multiple already-existing handicaps found in pipelines of this computational gauge are exposed, approaching each of them in a independent and simple manner, in such a way that the solutions proposed can be leveraged by the maximum number of workflows. On one side, this project concentrates on reducing the number of bugs introduced by flawed data, as to help the researchers to focus on developing more sophisticated models. On the other side, the shortage of integrated development systems for this kind of pipelines is envisaged, and with special care those using simulated or controlled environments, with the goal of easing the continuous iteration of these pipelines.Thanks to the increasing popularity of drones, the research and development of autonomous capibilities has become easier. However, due to the challenge of integrating multiple technologies, the available software stack to engage this task is restricted. In this thesis, we accent the divergencies among unmanned-aerial-vehicle simulators and propose a platform to allow faster and in-depth prototyping of machine learning algorithms for this drones

Artificial Intelligence, International Competition, and the Balance of Power (May 2018)

World leaders, CEOs, and academics have suggested that a revolution in artificial intelligence is upon us. Are they right, and what will advances in artificial intelligence mean for international competition and the balance of power? This article evaluates how developments in artificial intelligence (AI) — advanced, narrow applications in particular — are poised to influence military power and international politics. It describes how AI more closely resembles “enabling” technologies such as the combustion engine or electricity than a specific weapon. AI’s still-emerging developments make it harder to assess than many technological changes, especially since many of the organizational decisions about the adoption and uses of new technology that generally shape the impact of that technology are in their infancy. The article then explores the possibility that key drivers of AI development in the private sector could cause the rapid diffusion of military applications of AI, limiting first-mover advantages for innovators. Alternatively, given uncertainty about the technological trajectory of AI, it is also possible that military uses of AI will be harder to develop based on private-sector AI technologies than many expect, generating more potential first-mover advantages for existing powers such as China and the United States, as well as larger consequences for relative power if a country fails to adapt. Finally, the article discusses the extent to which U.S. military rhetoric about the importance of AI matches the reality of U.S. investments.LBJ School of Public Affair

A Hierarchical Task Analysis of Commercial Distribution Driving in the UK

At the heart of distribution operations is an essential influence in the success or failure of achieving the triple bottom line of safety, efficiency, and environmental friendliness: commercial vehicle drivers, and the increasingly complex technology with which they interact. To the authors’ knowledge, no hierarchical task analysis exists for commercial distribution driving, and this gap suggests that the first step in clarifying these functional relationships is to fulfill the evident need for a HTA of the commercial driving task. Thus, relevant literature (e.g. the UK Driving Standards Agency; existing hierarchical task analysis of private vehicle driving) is consulted to review procedure and construct a hierarchical task analysis of commercial distribution driving, in accordance with UK standards for C, CE, C+1 and CE+1 licensed driving activities. Preliminary analysis indicates that successful completion of the commercial driving task is subject to a far more complex set of factors than that of private vehicle driving, many of which require input from actors across various contexts, and rely heavily on automated vehicle technology. At present there exists no comprehensive, standardized measure against which to evaluate the quality of content in commercial driver training, and much is left to the expertise and discretion of individual companies to determine content which will create and support an ‘effective’ driver. This hierarchical task analysis provides a normative characterization of commercial driving which informs driver training needs and course content, and supports industry expertise with a functional structure. Furthermore, this analysis may also serve as an input to a wide range of human factors analyses for effective system design

Aplicación de técnicas de aprendizaje colaborativo en el grado de ingeniero agrónomo (agricultura de precisión) y máster en agroingeniería (robótica en la agricultura)

Se han empleado de técnicas de aprendizaje colaborativo y evaluación formativa en tres actividades docentes correspondientes a una asignatura de grado (Agricultura de precisión), otra de postgrado (Robótica aplicada) y a un viaje de estudios. En este estudio se revisa bibliografía docente relevante para esta cuestión y se muestran ejemplos de los modelos conceptuales, bitácoras y ensayos de reflexión generados en este contexto. La aplicación sistemática de estas técnicas empleando cada año como punto de partida los resultados de los cursos previos puede redundar en una mejora significativa del aprendizaje profundo en las distintas materias