@inproceedings{heidecker2023context,
  abstract = {Context information provided along with a dataset can be very helpful for solving a problem because the additional knowledge is already available and does not need to be extracted. Moreover, the context indicates how diverse a dataset is, i.e., how many samples per context category are available to train and test machine learning (ML) models. In this article, we present context annotations for the BDD100k image dataset. The annotations comprise, for instance, information about daytime, road condition (dry/wet), and dirt on the windshield. Sometimes, no or only little data are available for unique or rare combinations of these context attributes. However, data that matches these context conditions is crucial when discussing corner cases: Firstly, most ML models, e.g., object detectors, are not trained on such data, which leads to the assumption that they will perform poorly in those situations. Secondly, data containing corner cases are required for validating ML models. With this in mind, separate ML models dedicated to context detection are useful for expanding the training set with additional data of special interest, such as corner cases.},
  author = {Heidecker, Florian and Susetzky, Tobias and Fuchs, Erich and Sick, Bernhard},
  booktitle = {IEEE International Conference on Intelligent Transportation Systems (ITSC)},
  doi = {10.1109/ITSC57777.2023.10422414},
  interhash = {176a956134ff5471596cb400e7df61af},
  intrahash = {6a9cc7dc53de472e969176e9fa8a4f32},
  pages = {1522--1529},
  publisher = {IEEE},
  title = {Context Information for Corner Case Detection in Highly Automated Driving},
  year = 2023
}