Trajectory data analysis is an essential component for highly automated driving. Complex models developed with these data predict other road users' movement and behavior patterns. Based on these predictions -and additional contextual information such as the course of the road, (traffic) rules, and interaction with other road users -the highly automated vehicle (HAV) must be able to reliably and safely perform the task assigned to it, e.g., moving from point A to B. Ideally, the HAV moves safely through its environment, just as we would expect a human driver to do. However, if unusual trajectories occur, so-called trajectory corner cases, a human driver can usually cope well, but an HAV can quickly get into trouble. In the definition of trajectory corner cases, which we provide in this work, we will consider the relevance of unusual trajectories with respect to the task at hand. Based on this, we will also present a taxonomy of different trajectory corner cases. The categorization of corner cases into the taxonomy will be shown with examples and is done by cause and required data sources. To illustrate the complexity between the machine learning (ML) model and the corner case cause, we present a general processing chain underlying the taxonomy.

Before Automated Vehicles (AVs) can enter the market, academic and industrial partners must tackle system homologation to ensure the functional safety of AVs. For that task, a scenario-driven approach has gained acceptance in recent years since it allows facing the overall system’s complexity. Real-world drivings are a valuable source of scenarios. However, their extraction and analysis are still the subjects of research. This work addresses this issue and proposes a novel methodology to extract routing scenarios on urban intersections. Therefore, trajectories are annotated with routing maneuvers using a semantically enriched road network and map matching for route backtracking. The accuracy of the approach and consistency of the methodology is evaluated with Brunswick’s inner-city ring and by extracting u-turn scenarios on the Application Platform for Intelligent Mobility (AIM) Research Intersection of the German Aerospace Center (DLR).

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Before Automated Vehicles (AVs) can enter the market, academic and industrial partners must tackle system homologation to ensure the functional safety of AVs. For that task, a scenario-driven approach has gained acceptance in recent years since it allows facing the overall system’s complexity. Real-world drivings are a valuable source of scenarios. However, their extraction and analysis are still the subjects of research. This work addresses this issue and proposes a novel methodology to extract routing scenarios on urban intersections. Therefore, trajectories are annotated with routing maneuvers using a semantically enriched road network and map matching for route backtracking. The accuracy of the approach and consistency of the methodology is evaluated with Brunswick’s inner-city ring and by extracting u-turn scenarios on the Application Platform for Intelligent Mobility (AIM) Research Intersection of the German Aerospace Center (DLR).