The article titled "Context Information for Corner Case Detection in Highly Automated Driving" by Florian Heidecker, Tobias Susetzky, Erich Fuchs and Bernhard Sick highlights the importance of context information in datasets for machine learning models and presents context annotations for the BDD100k image dataset. Emphasizing the lack of data for unique context combinations, the research proposes specific ML models for context recognition to address the lack of training data for critical scenarios such as corner cases and improve model performance and validation.

The article titled "Context Information for Corner Case Detection in Highly Automated Driving" by Florian Heidecker, Tobias Susetzky, Erich Fuchs and Bernhard Sick highlights the importance of context information in datasets for machine learning models and presents context annotations for the BDD100k image dataset. Emphasizing the lack of data for unique context combinations, the research proposes specific ML models for context recognition to address the lack of training data for critical scenarios such as corner cases and improve model performance and validation.