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RNN-based Prediction of Pedestrian Turning Maneuvers

: Becker, Stefan

Fulltext urn:nbn:de:0011-n-5521936 (8.7 MByte PDF)
MD5 Fingerprint: 9d279a999fa354b867f0b88170670bc5
Created on: 19.7.2019

Beyerer, Jürgen (Ed.); Taphanel, Miro (Ed.); Taphanel, Miro (Ed.):
Proceedings of the 2018 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory
Karlsruhe: KIT Scientific Publishing, 2019 (Karlsruher Schriften zur Anthropomatik 40)
ISBN: 978-3-7315-0936-3
ISBN: 3-7315-0936-9
DOI: 10.5445/KSP/1000094782
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation and Institute for Anthropomatics, Vision and Fusion Laboratory (Joint Workshop) <2018, Triberg-Nussbach>
Conference Paper, Electronic Publication
Fraunhofer IOSB ()

The dynamics of objects, such as pedestrians, varies over time. Commonly
this problem is tackled with traditional approaches like the Interacting Multiple Model (IMM) filter using a Bayesian formulation. Following the current trend towards using deep neural networks, in this paper an RNN-based alternative solution for pedestrian maneuver prediction is presented. Similar to an IMM filter solution, the presented model assigns a confidence value to a performed dynamic and, based on them, puts out a multi-modal distribution over future pedestrian trajectories. The qualitative evaluation is done on synthetic data, reflecting prototypical pedestrian maneuvers.