Mid-term status report on KISSaF: AI-based situation interpretation for automated driving
Conference: AmE 2022 – Automotive meets Electronics - 13. GMM-Symposium
09/29/2022 - 09/30/2022 at Dortmund, Germany
Proceedings: GMM-Fb. 104: AmE 2022
Pages: 5Language: englishTyp: PDF
Authors:
Stockem Novo, Anne (ZF Automotive Germany GmbH, Gelsenkirchen, Germany & Institute of Computer Science, Ruhr West University of Applied Sciences, Mülheim an der Ruhr, Germany)
Stolpe, Marco (ZF Automotive Germany GmbH, Düsseldorf, Germany)
Diehl, Christopher; Osterburg, Timo; Bertram, Torsten (Institute of Control Theory and Systems Engineering, TU Dortmund University, Dortmund, Germany)
Parsi, Vijay; Murzyn, Nils; Mualla, Firas; Schneider, Georg (ZF Friedrichshafen AG, Uni-Campus Nord D5 2, Saarbrücken, Germany)
Toews, Philipp (INGgreen GmbH, Koblenz, Germany)
Abstract:
KISSaF is a publicly funded project with four project partners from industry and academia. The aim of project KISSaF is the development of a robust scene prediction model for automated driving. State-of-the-art Deep Learning methods are used for a complete and reliable forecasting of the traffic scene with large time horizons. The underlying environment modeling uses a graph-based representation of the scene. A prototype vehicle has been built-up for data recording. This data is the central part for model development, improvement and testing. A framework is currently setup for a scenariobased test approach and performance can be judged under realistic conditions with integrated maneuver planning.