Bücher von Nico Schick

Nach einem Wohnungsbrand im Dorf Lormannsberg macht der Zeitungsreporter Johan Kattler Bekanntschaft mit dem charmanten Derrick, der ihm und seiner Familie ein unglaubliches Geheimnis offenbart:Vor vielen Jahren trat Johans längst verstorbener Vater einer kleinen Gruppe von Okkultisten namens Proknome bei, mit deren einzigartigem Zauber er sich von seinen schlechten Emotionen freizusprechen verhoffte. Außerdem ist Derrick kein echter Mensch, sondern ein aus guten Gefühlen bestehendes Wesen, erzeugt von Aaron Falkmann, dem Mann, der einst in dem abgebrannten Haus lebte.Nun befindet sich dieser, zum Schutz vor seiner ebenfalls frei existierenden bösen Gefühlshälfte, in einer von Derrick erzeugten Fantasiewelt.Derrick bittet Johan, nach verschollenen Hinweisen seines Vaters zu suchen, um die Proknome ausfindig zu machen, da diese als Einzige wissen, wie man dem feindseligen Gefühlswesen ein Ende bereitet.

Autonomous driving is one of the key disciplines in the automotive field and currently under intensive development, especially with the objective of saving more people¿s lives on the roads due to significant reductions in the number of traffic accidents. Therefore, the software components within autonomous cars must be tested efficient and precisely. One of the most challenging aspects of autonomous cars are the safety-critical driving scenarios. Their criticality has seldom been measured in terms of further forensic analysis or software solutions in the field of artificial intelligence. Therefore, data related to safety-critical driving scenarios must be obtained another way. In this context, kinematic models can be used to represent these scenes by describing the vehicle¿s movements based on defined boundary constraints as well as providing synthesized data through the simulation of a model for the training and validation of the underlying machine learning algorithms, such as neural networks or generative algorithms. In this paper, three of the most significant safety-critical driving scenarios, namely emergency braking, turning, and overtaking, are modeled accordingly.

About 3700 people die in traffic accidents every day. Human error is the number one cause of accidents. Autonomous driving can greatly reduce the occurrence of traffic accidents. To release self-driving cars for road traffic, the system including software must be validated and tested efficiently. However, due to their criticality, the amount of data corresponding to safety-critical driving scenarios are limited. These driving scenes can be expressed as a time series. They represent the corresponding movement of the vehicle, including time vector, position coordinates, speed and acceleration. Such data can be provided on different ways. For example, in the form of a kinematic model. Alternatively, artificial intelligence or machine learning methods can be used. They have been widely used in the development of autonomous vehicles. For example, generative algorithms can be used to generate such safety-critical driving data. However, the validation of generative algorithms is a challenge in general. In most cases, their quality is assessed by means of expert knowledge (qualitative). In order to achieve a higher degree of automation, a quantitative validation approach is necessary. Generative algorithms are based on probability distributions or probability density functions. Accordingly, similarity measures can be used to evaluate generative algorithms. In this publication, such similarity measures are described and compared on the basis of defined evaluation criteria. With respect to the use case mentioned, a recommended similarity measure is implemented and validated for an example of a typical safety-critical driving scenario.