@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
}

@inproceedings{hetzel2023imptc,
  abstract = {Inner-city intersections are among the most critical
traffic areas for injury and fatal accidents. Automated vehicles
struggle with the complex and hectic everyday life within those
areas. Sensor-equipped smart infrastructures, which can cooperate with vehicles, can benefit automated traffic by extending the
perception capabilities of drivers and vehicle perception systems.
Additionally, they offer the opportunity to gather reproducible
and precise data of a holistic scene understanding, including
context information as a basis for training algorithms for various
applications in automated traffic. Therefore, we introduce the
Infrastructural Multi-Person Trajectory and Context Dataset
(IMPTC). We use an intelligent public inner-city intersection in
Germany with visual sensor technology. A multi-view camera
and LiDAR system perceives traffic situations and road users’
behavior. Additional sensors monitor contextual information like
weather, lighting, and traffic light signal status. The data acquisition system focuses on Vulnerable Road Users (VRUs) and multiagent interaction. The resulting dataset consists of eight hours
of measurement data. It contains over 2,500 VRU trajectories,
including pedestrians, cyclists, e-scooter riders, strollers, and
wheelchair users, and over 20,000 vehicle trajectories at different
day times, weather conditions, and seasons. In addition, to enable
the entire stack of research capabilities, the dataset includes all
data, starting from the sensor-, calibration- and detection data
until trajectory and context data. The dataset is continuously
expanded and is available online for non-commercial research at
https://github.com/kav-institute/imptc-dataset},
  author = {Hetzel, Manuel and Reichert, Hannes and Reitberger, Günther and Doll, Konrad and Sick, Bernhard and Fuchs, Erich},
  booktitle = {IEEE Intelligent Vehicles Symposium (IV)},
  doi = {10.1109/IV55152.2023.10186776},
  interhash = {97206f4cdb3b8d0520bc44f26973a607},
  intrahash = {b92cac988599318593aecc30644af572},
  pages = {1--7},
  publisher = {IEEE},
  title = {The {IMPTC} Dataset: An Infrastructural Multi-Person Trajectory and Context Dataset},
  year = 2023
}

@article{Fuchs2010379,
  abstract = {Subspace representations that preserve essential information of high-dimensional data may be advantageous for many reasons such as improved interpretability, overfitting avoidance, acceleration of machine learning techniques. In this article, we describe a new subspace representation of time series which we call polynomial shape space representation. This representation consists of optimal (in a least-squares sense) estimators of trend aspects of a time series such as average, slope, curve, change of curve, etc. The shape space representation of time series allows for a definition of a novel similarity measure for time series which we call shape space distance measure. Depending on the application, time series segmentation techniques can be applied to obtain a piecewise shape space representation of the time series in subsequent segments. In this article, we investigate the properties of the polynomial shape space representation and the shape space distance measure by means of some benchmark time series and discuss possible application scenarios in the field of temporal data mining.},
  author = {Fuchs, Erich and Gruber, Thiemo and Pree, Helmuth and Sick, Bernhard},
  doi = {10.1016/j.neucom.2010.03.022},
  interhash = {88c499ac1dc9e9708e70187967494219},
  intrahash = {fdf6865c1bece3f77cc3e29365a2c6b3},
  issn = {0925-2312},
  journal = {Neurocomputing},
  note = {Artificial Brains},
  number = {1–3},
  pages = {379 - 393},
  title = {Temporal data mining using shape space representations of time series},
  url = {http://www.sciencedirect.com/science/article/pii/S0925231210002237},
  volume = 74,
  year = 2010
}