Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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  • Publication
    Sensor system for development of perception systems for ATO
    ( 2023)
    Tagiew, Rustam
    ;
    Leinhos, Dirk
    ;
    Haar, Henrik von der
    ;
    Klotz, Christian
    ;
    Sprute, Dennis orcid-logo
    ;
    Ziehn, Jens
    ;
    Schmelter, Andreas
    ;
    Witte, Stefan
    ;
    Klasek, Pavel
    Developing AI systems for automatic train operation (ATO) requires developers to have a deep understanding of the human tasks they are trying to replace. This paper fills this gap and translates the regulatory requirements from the context of German railways for the AI developer community. As a result, tasks such as train’s path monitoring for collision prediction, signal detection, door operation, etc. are identified. Based on this analysis, a functionally justified sensor setup with detailed configuration requirements is presented. This setup was also evaluated by a survey within the railway industry. The evaluated sensors include RGB/IR cameras, LIDARs, radars and ultrasonic sensors. Calculations and estimates for the evaluated sensors are presented graphically and included in this paper. However, the ultimate sensor setup is still a subject of research. The results of this paper also address the lack of training and test datasets for railway AI systems. It is proposed to acquire research datasets that will allow the training of domain adaptation algorithms to transform other datasets, thus increasing the number of available datasets. The sensor setup is also recommended for such research datasets.
  • Publication
    Onboard Sensor Systems for Automatic Train Operation
    ( 2022)
    Tagiew, Rustam
    ;
    Leinhos, Dirk
    ;
    Haar, Henrik von der
    ;
    Klotz, Christian
    ;
    Sprute, Dennis orcid-logo
    ;
    Ziehn, Jens
    ;
    Schmelter, Andreas
    ;
    Witte, Stefan
    ;
    Klasek, Pavel
    This paper introduces the specific requirements of the domain of train operation and its regulatory framework to the AI community. It assesses sensor sets for driverless and unattended train operation. It lists functionally justified ranges of technical specifications for sensors of different types, which will generate input for AI perception algorithms (i.e. for signal and obstacle detection). Since an optimal sensor set is the subject of research, this paper provides the specification of a generic data acquisition platform as a crucial step. Some particular results are recommendations for the minimal resolution and shutter type for image sensors, as well as beam steering methods and resolutions for LiDARs.