Autonomous systems are gaining momentum in various application domains, such as autonomous vehicles, autonomous transport robotics and self-adaptation in smart homes. Product liability regulations impose high standards on manufacturers of such systems with respect to dependability (safety, security and privacy). Today's conventional engineering methods are not adequate for providing guarantees with respect to dependability requirements in a costefficient manner, e.g. road tests in the automotive industry sum up millions of miles before a system can be considered sufficiently safe. System engineers will no longer be able to test and respectively formally verify autonomous systems during development time in order to guarantee the dependability requirements in advance. In this vision paper, we introduce a new holistic software systems engineering approach for autonomous systems, which integrates development time methods as well as operation time techniques. With this approach, we aim to give the users a transparent view of the confidence level of the autonomous system under use with respect to the dependability requirements. We present already obtained results and point out research goals to be addressed in the future.
Most people have trouble assessing artificial intelligence. They cannot decide without a sound education what are the applicabilities and limitations of this technology and how to use it. In this publication we present a workshop program, which allows even novices to understand and implement the most fundamental concepts of supervised learning within a short time. The participants of the workshop are motivated by a combination of lecture, configuration work on a computer, and rapid testing on real model vehicles. The workshop presented here has already been held four times and was rated as very positive and instructive by all 48 participants.
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