ZDIN-Zahlen im Detail

Eine zentrale Herausforderung bei der Umsetzung von Model-based Systems Engineering ist die Sicherstellung der Modellkonsistenz. Um dieser Herausforderung zu begegnen, schlägt dieser Beitrag die Anwendung von Konsistenzregeln vor. Hierzu werden einerseits notwendige Grundlagen, wie die Bewertung der Modellkonsistenz und andererseits die konkrete Formulierung von Konsistenzregeln betrachtet. Um eine strukturierte Formulierung zu ermöglichen, wurde eine Satzschablone entwickelt, welche insbesondere die Bewertung von Architekturmustern beinhaltet. Außerdem wurden entsprechend der Satzschablone konkrete Konsistenzregeln an einem heterogenen Modell beispielhaft angewendet.

Autonomous driving and interconnected cyber-physical traffic systems pose ever-increasing challenges for the development and validation of advanced driver assistance systems and autonomous driving functions. In particular, real-time optimization and testing are related to enormous effort and risk. A holistic, flexibly configurable, real-time capable test bench for the entire vehicle remedy this situation. This test system must replicate both the road excitation and the networked traffic environment with sufficient accuracy. In addition, the sensory perceptions of the vehicle must be stimulated. The following paper therefore describes the concept of the holistic, highly flexibly configurable real-time test system for intelligent vehicles in cooperating cyber-physical traffic systems, called ERAGON.

In this paper, a localization function for an autonomous vehicles (AV) is implemented in a cyber-physical laboratory test field. This function is adapted to the properties of the cyber-physical system and can be used in variety of projects of different domains, such as indoor road traffic simulation as well as in the context of Industry 4.0 applications. The great advantage of this feature is that the position of the AV platform can be determined in parallel by two different methods: a self-localization algorithm using sensors on the vehicle and through the sensor in the environment. These two data sources are transmitted via UDP in the CPS laboratory test field, which is used as the basis for the subsequent application (navigation).

Autonomous driving and interconnected cyber-physical traffic systems pose ever-increasing challenges for the development and validation of advanced driver assistance systems and autonomous driving functions. In particular, real-time optimization and testing are related to enormous effort and risk. A holistic, flexibly configurable, real-time capable test bench for the entire vehicle remedy this situation. This test system must replicate both the road excitation and the networked traffic environment with sufficient accuracy. In addition, the sensory perceptions of the vehicle must be stimulated. The following paper therefore describes the concept of the holistic, highly flexibly configurable real-time test system for intelligent vehicles in cooperating cyber-physical traffic systems, called ERAGON.

Safety is critical for autonomous driving, and one aspect of improving safety is to accurately capture the uncertainties of the perception system, especially knowing the unknown. Different from only providing deterministic or probabilistic results, e.g., probabilistic object detection, that only provide partial information for the perception scenario, we propose a complete probabilistic model named GevBEV. It interprets the 2D driving space as a probabilistic Bird’s Eye View (BEV) map with point-based spatial Gaussian distributions, from which one can draw evidence as the parameters for the categorical Dirichlet distribution of any new sample point in the continuous driving space. The experimental results show that GevBEV not only provides more reliable uncertainty quantification but also outperforms the previous works on the benchmarks OPV2V and V2V4Real of BEV map interpretation for cooperative perception in simulated and real-world driving scenarios, respectively. A critical factor in cooperative perception is the data transmission size through the communication channels. GevBEV helps reduce communication overhead by selecting only the most important information to share from the learned uncertainty, reducing the average information communicated by 87% with only a slight performance drop.