Scenario-based testing has become a promising approach to overcome the complexity of real-world traffic for safety assurance of automated vehicles. Within scenario-based testing, a system under test is confronted with a set of predefined scenarios. This set shall ensure more efficient testing of an automated vehicle operating in an open context compared to real-world testing. However, the question arises if a scenario catalog can cover the open context sufficiently to allow an argumentation for sufficiently safe driving functions and how this can be proven. Within this paper, a methodology is proposed to argue a sufficient completeness of a scenario concept using a goal structured notation. Thereby, the distinction between completeness and coverage is discussed. For both, methods are proposed for a streamlined argumentation and regarding evidence. These methods are applied to a scenario concept and the inD dataset to prove the usability.

Scenario-based approaches are important for verifying and validating automated driving systems due to the complexity of traffic, which cannot be covered by on-road tests. A challenge is the definition of a scenario catalog that is not too abstract to address this complexity, but still keeps a manageable number of driving scenarios. This paper presents a semi-formalized approach to derive a scenario catalog, resulting in less than 300 base-scenarios, for use in a driving scenario database for safety validation of automated driving. The approach defines abstract concepts as collections of characteristics to distinguish different driving scenarios. Within high-level categories, certain concepts are combined, either as full factorial of characteristics, or by considering, that the combination of characteristics implies other characteristics. Superclasses are also defined to group scenarios with common characteristics. The approach efficiently represents real-world interactions and can cover many existing catalogs within predefined constraints.

A possible objective in analyzing trajectories of multiple simultaneously moving objects, such as football players during a game, is to extract and understand the general patterns of coordinated movement in different classes of situations as they develop. For achieving this objective, we propose an approach that includes a combination of query techniques for flexible selection of episodes of situation development, a method for dynamic aggregation of data from selected groups of episodes, and a data structure for representing the aggregates that enables their exploration and use in further analysis. The aggregation, which is meant to abstract general movement patterns, involves construction of new time-homomorphic reference systems owing to iterative application of aggregation operators to a sequence of data selections. As similar patterns may occur at different spatial locations, we also propose constructing new spatial reference systems for aligning and matching movements irrespective of their absolute locations. The approach was tested in application to tracking data from two Bundesliga games of the 2018/2019 season. It enabled detection of interesting and meaningful general patterns of team behaviors in three classes of situations defined by football experts. The experts found the approach and the underlying concepts worth implementing in tools for football analysts.

From1 a set of trajectories of the players and the ball in a football (soccer) game, we computationally estimate, for each time frame, the pressure of the defending players upon the ball and the opponents. The extracted pressure relationships are visualized in detailed and summarized forms. Interactive filtering enables exploration of the pressure relationships in selected game episodes or in game situations satisfying specific query conditions..