Entropy-Based Intention Change Detection with a Multi-Hypotheses Filter

Conference contribution
(Conference Contribution)


Publication Details

Author(s): Particke F, Hofmann C, Hiller M, Feist C, Thielecke J
Editor(s): IEEE
Publisher: IEEE
Publication year: 2018
Conference Proceedings Title: 2018 21st International Conference on Information Fusion (FUSION)
Pages range: 610-616
ISBN: 978-0-9964527-6-2
Language: English


Abstract

In the future, pedestrians and fully automated vehicles have to operate
in an environment they share. To minimize the risk for pedestrians, it
is very important to predict precisely their future movement. One
important information source is the intention of the pedestrian. For the
integration of the intention information, a Multi-Hypotheses filter is
used, where different hypotheses for the intention of the pedestrian are
considered. An intention change detector based on the Multi-Hypotheses
filter utilizing an entropy-based confidence score is developed. With
this contribution, critical real-world situations like a pedestrian
crossing the street instead of following the sidewalk are tackled. The
evaluation of the intention change detector is performed in simulation
and for real-world data. Firstly, the proposed approach is evaluated
using simulated trajectory data, where trajectories with intention
changes are generated by a self-made trajectory generator (open source).
Secondly, the course of the confidence score is evaluated for a
real-world scenario, where the detection of the pedestrians is performed
by the combination of a deep learning network (Tiny YOLO) and
background subtraction. It is shown that the mean distance into the road
from the sidewalk edge at the detection of the intention change is
below 1.5 m, even in the case of high sensor noise. For lower sensor
noise level, the intention change of the pedestrian is even detected
before entering the street. Key contributions are the proposal of the
Multi-Hypotheses filter, the derivation of the confidence score, the
proposal of the intention detector based on the confidence score and the
detection of the pedestrians and other obstacles by the fusion of
background subtraction and a deep learning network.


FAU Authors / FAU Editors

Hiller, Markus
Lehrstuhl für Informationstechnik mit dem Schwerpunkt Kommunikationselektronik (Stiftungslehrstuhl)
Hofmann, Christian
Lehrstuhl für Informationstechnik mit dem Schwerpunkt Kommunikationselektronik (Stiftungslehrstuhl)
Particke, Florian
Lehrstuhl für Informationstechnik mit dem Schwerpunkt Kommunikationselektronik (Stiftungslehrstuhl)
Thielecke, Jörn Prof. Dr.
Professur für Informationstechnik (Schwerpunkt Ortsbestimmung und Navigation)


External institutions with authors

Audi Electronics Venture GmbH


How to cite

APA:
Particke, F., Hofmann, C., Hiller, M., Feist, C., & Thielecke, J. (2018). Entropy-Based Intention Change Detection with a Multi-Hypotheses Filter. In IEEE (Eds.), 2018 21st International Conference on Information Fusion (FUSION) (pp. 610-616). Cambridge, UK, GB: IEEE.

MLA:
Particke, Florian, et al. "Entropy-Based Intention Change Detection with a Multi-Hypotheses Filter." Proceedings of the 2018 21st International Conference on Information Fusion (FUSION), Cambridge, UK Ed. IEEE, IEEE, 2018. 610-616.

BibTeX: 

Last updated on 2019-14-01 at 15:08