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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Context Information for Corner Case Detection in Highly Automated Driving


    Contributors:


    Publication date :

    2023-09-24


    Size :

    2005924 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Context Aware Data Reduction for Highly Automated Driving

    Storms, Kai | TIBKAT | 2024

    Free access

    Free Space Detection: A Corner Stone of Automated Driving

    Neumann, Leo / Vanholme, Benoit / Gressmann, Markus et al. | IEEE | 2015


    Towards Corner Case Detection for Autonomous Driving

    Bolte, Jan-Aike / Bar, Andreas / Lipinski, Daniel et al. | IEEE | 2019


    An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving

    Heidecker, Florian / Breitenstein, Jasmin / Rosch, Kevin et al. | IEEE | 2021


    Real Time Recognition of Non-driving Related Tasks in the Context of Highly Automated Driving

    Pech, Timo / Enhuber, Stephan / Wandtner, Bernhard et al. | TIBKAT | 2019