@InProceedings{Mersmann-etAl_ITSC2024_Towards-Application-ODD,
  author       = {Mersmann, Till and Betz, Friedrich and Eichenbaum, Julian and Hampel, Fabian and Klamt, Simon and Otten, Yannick and Scholl, Ingrid and Schindler, Christian},
  booktitle    = {2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)}, 
  title        = {Towards the Application of Operational Design Domain Based Scene Generation for Artificial Intelligence Training in Railway Automation},
  funding      = {The project “Rail Automation with Artificial
                  Intelligence for Detection of Exceptional
                  Situations” (RailAIxs) received funding in the mFUND
                  conveyor line by the German Federal Ministry for
                  Digital and Transport under the funding code
                  19FS2031A-D.}, 
  year         = {2024},
  month        = {Sep.},
  pages        = {3181--3188},
  abstract     = {For automated, driverless rail transportation
                  applications in open environments, Artificial
                  Intelligence (AI)-based methods are gaining
                  importance, especially in computer vision and
                  perception tasks. The safe operation of complex
                  automated systems requires validation processes. For
                  this purpose, the concept of Operational Design
                  Domains (ODDs), driven by recent developments in the
                  automotive industry, is gaining momentum, allowing
                  to describe different aspects of operating
                  conditions as scenes and scenarios. With regard to
                  safety and authorization using AI-based vision
                  systems, data coverage is needed, which can be
                  enhanced by employing virtual reality in different
                  forms. The creation of virtual scenes and sensor
                  models allows the generation of synthetic sensor
                  data and metadata that can be used as a database for
                  the training of the vision system.},
  keywords     = {Training; Solid modeling; Automation;Machine vision;
                  Virtual reality; Rail transportation; Safety;
                  Artificial intelligence; Standards; Synthetic data;
                  RailAIxs},
  doi          = {10.1109/ITSC58415.2024.10919732},
  ISSN         = {2153-0017},
}

{"_id":"vezAhKJJNayRXprFA","bibbaseid":"mersmann-betz-eichenbaum-hampel-klamt-otten-scholl-schindler-towardstheapplicationofoperationaldesigndomainbasedscenegenerationforartificialintelligencetraininginrailwayautomation-2024","author_short":["Mersmann, T.","Betz, F.","Eichenbaum, J.","Hampel, F.","Klamt, S.","Otten, Y.","Scholl, I.","Schindler, C."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"propositions":[],"lastnames":["Mersmann"],"firstnames":["Till"],"suffixes":[]},{"propositions":[],"lastnames":["Betz"],"firstnames":["Friedrich"],"suffixes":[]},{"propositions":[],"lastnames":["Eichenbaum"],"firstnames":["Julian"],"suffixes":[]},{"propositions":[],"lastnames":["Hampel"],"firstnames":["Fabian"],"suffixes":[]},{"propositions":[],"lastnames":["Klamt"],"firstnames":["Simon"],"suffixes":[]},{"propositions":[],"lastnames":["Otten"],"firstnames":["Yannick"],"suffixes":[]},{"propositions":[],"lastnames":["Scholl"],"firstnames":["Ingrid"],"suffixes":[]},{"propositions":[],"lastnames":["Schindler"],"firstnames":["Christian"],"suffixes":[]}],"booktitle":"2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)","title":"Towards the Application of Operational Design Domain Based Scene Generation for Artificial Intelligence Training in Railway Automation","funding":"The project “Rail Automation with Artificial Intelligence for Detection of Exceptional Situations” (RailAIxs) received funding in the mFUND conveyor line by the German Federal Ministry for Digital and Transport under the funding code 19FS2031A-D.","year":"2024","month":"Sep.","pages":"3181–3188","abstract":"For automated, driverless rail transportation applications in open environments, Artificial Intelligence (AI)-based methods are gaining importance, especially in computer vision and perception tasks. The safe operation of complex automated systems requires validation processes. For this purpose, the concept of Operational Design Domains (ODDs), driven by recent developments in the automotive industry, is gaining momentum, allowing to describe different aspects of operating conditions as scenes and scenarios. With regard to safety and authorization using AI-based vision systems, data coverage is needed, which can be enhanced by employing virtual reality in different forms. The creation of virtual scenes and sensor models allows the generation of synthetic sensor data and metadata that can be used as a database for the training of the vision system.","keywords":"Training; Solid modeling; Automation;Machine vision; Virtual reality; Rail transportation; Safety; Artificial intelligence; Standards; Synthetic data; RailAIxs","doi":"10.1109/ITSC58415.2024.10919732","issn":"2153-0017","bibtex":"@InProceedings{Mersmann-etAl_ITSC2024_Towards-Application-ODD,\n author = {Mersmann, Till and Betz, Friedrich and Eichenbaum, Julian and Hampel, Fabian and Klamt, Simon and Otten, Yannick and Scholl, Ingrid and Schindler, Christian},\n booktitle = {2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)}, \n title = {Towards the Application of Operational Design Domain Based Scene Generation for Artificial Intelligence Training in Railway Automation},\n funding = {The project “Rail Automation with Artificial\n Intelligence for Detection of Exceptional\n Situations” (RailAIxs) received funding in the mFUND\n conveyor line by the German Federal Ministry for\n Digital and Transport under the funding code\n 19FS2031A-D.}, \n year = {2024},\n month = {Sep.},\n pages = {3181--3188},\n abstract = {For automated, driverless rail transportation\n applications in open environments, Artificial\n Intelligence (AI)-based methods are gaining\n importance, especially in computer vision and\n perception tasks. The safe operation of complex\n automated systems requires validation processes. For\n this purpose, the concept of Operational Design\n Domains (ODDs), driven by recent developments in the\n automotive industry, is gaining momentum, allowing\n to describe different aspects of operating\n conditions as scenes and scenarios. With regard to\n safety and authorization using AI-based vision\n systems, data coverage is needed, which can be\n enhanced by employing virtual reality in different\n forms. The creation of virtual scenes and sensor\n models allows the generation of synthetic sensor\n data and metadata that can be used as a database for\n the training of the vision system.},\n keywords = {Training; Solid modeling; Automation;Machine vision;\n Virtual reality; Rail transportation; Safety;\n Artificial intelligence; Standards; Synthetic data;\n RailAIxs},\n doi = {10.1109/ITSC58415.2024.10919732},\n ISSN = {2153-0017},\n}\n","author_short":["Mersmann, T.","Betz, F.","Eichenbaum, J.","Hampel, F.","Klamt, S.","Otten, Y.","Scholl, I.","Schindler, C."],"key":"Mersmann-etAl_ITSC2024_Towards-Application-ODD","id":"Mersmann-etAl_ITSC2024_Towards-Application-ODD","bibbaseid":"mersmann-betz-eichenbaum-hampel-klamt-otten-scholl-schindler-towardstheapplicationofoperationaldesigndomainbasedscenegenerationforartificialintelligencetraininginrailwayautomation-2024","role":"author","urls":{},"keyword":["Training; Solid modeling; Automation;Machine vision; Virtual reality; Rail transportation; Safety; Artificial intelligence; Standards; Synthetic data; RailAIxs"],"metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"http://maskor.fh-aachen.de/biblio/MASKOR.bib","dataSources":["i8ftsMK5wMbiNqmtB","pBDNm3knLemYTNMHw"],"keywords":["training; solid modeling; automation;machine vision; virtual reality; rail transportation; safety; artificial intelligence; standards; synthetic data; railaixs"],"search_terms":["towards","application","operational","design","domain","based","scene","generation","artificial","intelligence","training","railway","automation","mersmann","betz","eichenbaum","hampel","klamt","otten","scholl","schindler"],"title":"Towards the Application of Operational Design Domain Based Scene Generation for Artificial Intelligence Training in Railway Automation","year":2024}