The field of validation and artificial intelligence (AI) for automated driving has been a rapidly emerging field of research and development in the last few years. Despite the enormous success of machine learning (ML) in perception and robotics, the capability of ML-supported automated driving functions remains to be proven in complex real-world scenarios. Due to stringent regulations and safety concerns, it is crucial to not only be able to identify critical driving events, the corner cases, but also to eliminate them in advance by systematic and provable processes. In contrast to previous work, we analyze and systematize the causes of corner cases from the perspective of neural network interpretation, and consider the network’s performance and robustness in relation to the availability of data points used during development and validation. Moreover, we demonstrate the proposed taxonomy of corner cases on real data from multiple sensor input sources, including images and LiDAR point clouds, showing relevant properties of various corner cases. Furthermore, we discuss the possible solutions dealing with previously unknown classes and driving environments as required in future automated driving use cases.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Corner Cases in Data-Driven Automated Driving: Definitions, Properties and Solutions


    Contributors:


    Publication date :

    2023-06-04


    Size :

    4070083 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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

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


    AN APPLICATION-DRIVEN CONCEPTUALIZATION OF CORNER CASES FOR PERCEPTION IN HIGHLY AUTOMATED DRIVING

    Heidecker, Florian / Breitenstein, Jasmin / Rösch, Kevin et al. | British Library Conference Proceedings | 2021


    Systematization of Corner Cases for Visual Perception in Automated Driving

    Breitenstein, Jasmin / Termohlen, Jan-Aike / Lipinski, Daniel et al. | IEEE | 2020


    SYSTEMATIZATION OF CORNER CASES FOR VISUAL PERCEPTION IN AUTOMATED DRIVING

    Breitenstein, Jasmin / Termöhlen, Jan-Aike / Lipinski, Daniel et al. | British Library Conference Proceedings | 2020


    Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving

    Rosch, Kevin / Heidecker, Florian / Truetsch, Julian et al. | IEEE | 2022