Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving
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.