@inproceedings{rosch2022space,
  abstract = {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.},
  author = {Rösch, Kevin and Heidecker, Florian and Truetsch, Julian and Kowol, Kamil and Schicktanz, Clemens and Bieshaar, Maarten and Sick, Bernhard and Stiller, Christoph},
  booktitle = {IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (IEEE CIVTS), IEEE SSCI},
  doi = {10.1109/SSCI51031.2022.10022241},
  interhash = {447a8bdb3e6b8ebdea50efbda167a1be},
  intrahash = {5ca46d6b3c06fe80cb8af0d17ce5770f},
  pages = {86--93},
  publisher = {IEEE},
  title = {Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving},
  url = {https://ieeexplore.ieee.org/document/10022241},
  year = 2022
}

@inproceedings{RLR+21,
  author = {Reichert, Hannes and Lang, Lukas and Rösch, Kevin and Bogdoll, Daniel and Doll, Konrad and Sick, Bernhard and Reuss, Hans-Christian and Stiller, Christoph and Zöllner, J. Marius},
  booktitle = {IEEE International Smart Cities Conference},
  interhash = {a857997bed8e249415ac09f1b0eda865},
  intrahash = {dfbae233f1877ba740c092309b489071},
  note = {(accepted)},
  title = {Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems},
  year = 2021
}

@inproceedings{HBR+21,
  address = {Nagoya, Japan},
  author = {Heidecker, Florian and Breitenstein, Jasmin and Rösch, Kevin and Löhdefink, Jonas and Bieshaar, Maarten and Stiller, Christoph and Fingscheidt, Tim and Sick, Bernhard},
  booktitle = {2021 IEEE Intelligent Vehicles Symposium (IV)},
  interhash = {dd6cfea9002af1f9cfa98e078b419116},
  intrahash = {5f6dbffae40d7656285e93d01cd9a961},
  note = {(accepted)},
  title = {{An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving}},
  year = 2021
}