The Role of Structural Reflection in Distributed Virtual Reality

The emergence of collaborative virtual world applications that run over the Internet has presented Virtual Reality (VR) application designers with new challenges. In an environment where the public internet streams multimedia data and is constantly under pressure to deliver over widely heterogeneous user-platforms, there has been a growing need that distributed virtual world applications be aware of and adapt to frequent variations in their context of execution. In this paper, we argue that in contrast to research efforts targeted at improvement of scalability, persistence and responsiveness capabilities, much less attempts have been aimed at addressing the flexibility, maintainability and extensibility requirements in contemporary Distributed VR applications. We propose the use of structural reflection as an approach that not only addresses these requirements but also offers added value in the form of providing a framework for scalability, persistence and responsiveness that is itself flexible, maintainable and extensible

Evolutionary robotics and neuroscience

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Decision-Making for Automated Vehicles Using a Hierarchical Behavior-Based Arbitration Scheme

Behavior planning and decision-making are some of the biggest challenges for highly automated systems. A fully automated vehicle (AV) is confronted with numerous tactical and strategical choices. Most state-of-the-art AV platforms implement tactical and strategical behavior generation using finite state machines. However, these usually result in poor explainability, maintainability and scalability. Research in robotics has raised many architectures to mitigate these problems, most interestingly behavior-based systems and hybrid derivatives. Inspired by these approaches, we propose a hierarchical behavior-based architecture for tactical and strategical behavior generation in automated driving. It is a generalizing and scalable decision-making framework, utilizing modular behavior blocks to compose more complex behaviors in a bottom-up approach. The system is capable of combining a variety of scenario- and methodology-specific solutions, like POMDPs, RRT* or learning-based behavior, into one understandable and traceable architecture. We extend the hierarchical behavior-based arbitration concept to address scenarios where multiple behavior options are applicable but have no clear priority against each other. Then, we formulate the behavior generation stack for automated driving in urban and highway environments, incorporating parking and emergency behaviors as well. Finally, we illustrate our design in an explanatory evaluation