Dynamic environment modelling and prediction for autonomous systems

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review


This work describes a method to extend classical maps in terms of additional information and a prediction about objects within the environment. The prediction system is based on the behaviour of the observed objects and influences accordingly the updating of the map. First all objects are classified due to their size and further parameters characterizing the ability to move. Furthermore the velocity and orientation of objects within the visible area of the autonomous system are extracted from a vision sensor. In case of a mobile autonomous system they help to adjust its path in real time. Additionally all objects are tracked. In order to generate the statistical map a statistical indicator is introduced describing the possible future positions of objects. Thus the conventional maps can be improved by adding information about the status of the considered space. Furthermore the status of objects can be predicted even when they are not visible anymore. In the case of a mobile system, it will improve the awareness drastically enabling it to act pre-emptively and improve the human-machine interaction in e.g. a production environment.
Original languageEnglish
Title of host publicationProceedings of the 2016 13th Workshop on Positioning, Navigation and Communication, WPNC 2016 : WPNC 2016
Number of pages6
PublisherIEEE - Institute of Electrical and Electronics Engineers Inc.
Publication date17.01.2017
Article number7822847
ISBN (Print)978-1-5090-5441-1
ISBN (Electronic)978-1-5090-5440-4
Publication statusPublished - 17.01.2017
Event13th Workshop on Positioning, Navigation and Communications (WPNC) - Bremen, Germany
Duration: 19.10.201620.10.2016
Conference number: 13

    Research areas

  • Engineering - Maps, Autonomous systems, Dynamic environments , Human machine interaction, Mobile autonomous, Parameters characterizing, Prediction systems, Production Environments, Statistical indicators