Economic and monetary integration of the new Member States - helping to chart the route

This paper examines diverse aspects of the monetary integration of the ten new Member States (NMS) which joined the EU on 1 May 2004 into the euro area. Most NMS have undergone a rapid and deep transformation in all areas with considerable progress in their processes of reform and convergence, and more is underway. While trade integration with the other 15 EU Member States (EU15) has progressed quickly, convergence in output specialisation to EU standards has been slow, especially if measured in real terms. This may influence negatively the pace of real convergence. Most NMS lag significantly behind in building up and deepening their financial systems. There is also evidence that exchange rate flexibility may still be serving as a useful shock absorber for some NMS, and so far the evidence indicates that real exchange rates have moved, broadly speaking, in line with long term fundamental equilibria. On the positive side, many NMS are quite advanced relative to the euro area in the process of labour market and institutional reform (their labour market structures are more flexible than those of the euro area countries). There is also some evidence that a few NMS have a significant degree of business-cycle synchronisation with the euro area: hence, they may become less likely to be affected by different economic shocks. This, however, is not true for all NMS. The monetary policy institutions of the NMS have also converged to some degree - goals and institutional settings of central banks are now much more similar than before. A case-by-case approach to adopting the euro, based on country-specific conditions, seems natural due to the differences between the countries.Optimum Currency Area, Economic and Monetary Integration, EMU.

Kooperative Regelungskonzepte auf Basis der Spieltheorie und deren Anwendung auf Fahrerassistenzsysteme

Diese Arbeit betrachtet Regelkreise, in denen die Regelaufgabe von Menschen und maschinellen Reglern gemeinsam ausgeführt wird. Für diese maschinellen Regler wird systematisch ein formalisiertes Regelkonzept abgeleitet. Ein wesentlicher Teil der Arbeit besteht in der Entwicklung von Algorithmen für die Implementierung. Als Anwendungsbeispiel werden zwei kooperative Fahrerassistenzsysteme vorgestellt. Am Fahrsimulator durchgeführte Studien zeigen eine deutliche Verbesserung des Fahrverhaltens aber auch des Kraftstoffverbrauchs

Kooperative Regelungskonzepte auf Basis der Spieltheorie und deren Anwendung auf Fahrerassistenzsysteme

Cooperative control loops in which human and a technical automation system perform a control task in close cooperation are investigated. A control framework is proposed which is based on a formal description of the cooperative control problem. The main idea of the control algorithm is to solve a differential game on a sliding horizon. The concept has been applied to design two cooperative advanced driver-assistance systems. One for the longitudinal driving task, one for the lateral driving task

A Continuous and Quantitative Metric for the Levels of Automation

The standard SAE J3016 provides a definition for levels of driving automation; however, shared control algorithms that have successfully been applied to vehicle automation, do not reasonably fit into the levels proposed by the standard. In this paper, the rich literature about levels of automation taxonomies is reviewed regarding the applicability to all automation concepts. Most definitions provide qualitative rankings of different levels of automation for specific applications. However, from an engineering perspective, a quantitative and generic approach would generate great benefit for the design, analysis and evaluation of human-machine systems. Thus, criteria for a desirable metric of levels of automation are discussed and a continuous and quantitative measure based on the options available to the human is proposed. The application to an advanced driver assistant system example is demonstrated and future ways to leverage the potential of the metric within and beyond automated driving are examined

Individual Human Behavior Identification Using an Inverse Reinforcement Learning Method

Shared control techniques have a great potential to create synergies in human-machine interaction for efficient and safe applications. However, an optimal interaction requires the machine to consider the individual behavior of the human partner. A widespread approach for modeling human behavior is given by optimal control theory, where the movement trajectories of a human arise from an optimized cost function. The aim of the identification is thus to determine parameters of a cost function which explains observed human motion. The central thesis of this paper is that individual cost function parameters which describe specific behavior can be determined by means of Inverse Reinforcement Learning. We show the applicability of the approach with a tracking control task example. The experiment consists in following a reference trajectory by means of a steering wheel. The study confirms that optimal control is suitable for modeling individual human behavior and demonstrates the suitability of Inverse Reinforcement Learning in order to determine the cost function parameters which explain measured data