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InfoSalGAIL: Visual Attention-empowered Imitation Learning of Pedestrian Behavior in Critical Traffic Scenarios

Topics: Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World Applications, Financial Applications, Neural Prostheses and Medical Applications, Neural Based Data Mining and Complex Information Process; Convolutional Neural Networks; Deep Learning

Authors: Igor Vozniak ; Matthias Klusch ; André Antakli and Christian Müller

Affiliation: German Research Center for Artificial Intelligence (DFKI), Stuhlsatzenhausweg 3, 66123 Saarbruecken, Germany

Keyword(s): Visual Attention-empowered Imitation Learning, End-to-End Human-like Data-driven Simulation, Critical Scenario Generation.

Abstract: The imitation learning of complex pedestrian behavior based on visual input is a challenge due to the underlying large state space and variations. In this paper, we present a novel visual attention-based imitation learning framework, named InfoSalGAIL, for end-to-end imitation learning of (safe, unsafe) pedestrian navigation policies through visual expert demonstrations empowered by eye fixation sequence and augmented reward function. This work shows the relation in latent space between the policy estimated trajectories and visual-attention map. Moreover, the conducted experiments revealed that InfoSalGAIL can significantly outperform the state-of-the-art baseline InfoGAIL. In fact, its visual attention-empowered imitation learning tends to much better generalize the overall policy of pedestrian behavior leveraging apprenticeship learning to generate more human-like pedestrian trajectories in virtual traffic scenes with the open source driving simulator OpenDS. InfoSalGAIL can be uti lized in the process of generating and validating critical scenarios for adaptive driving assistance systems. (More)

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Paper citation in several formats:
Vozniak, I.; Klusch, M.; Antakli, A. and Müller, C. (2020). InfoSalGAIL: Visual Attention-empowered Imitation Learning of Pedestrian Behavior in Critical Traffic Scenarios. In Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - NCTA; ISBN 978-989-758-475-6; ISSN 2184-3236, SciTePress, pages 325-337. DOI: 10.5220/0010020003250337

@conference{ncta20,
author={Igor Vozniak. and Matthias Klusch. and André Antakli. and Christian Müller.},
title={InfoSalGAIL: Visual Attention-empowered Imitation Learning of Pedestrian Behavior in Critical Traffic Scenarios},
booktitle={Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - NCTA},
year={2020},
pages={325-337},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010020003250337},
isbn={978-989-758-475-6},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - NCTA
TI - InfoSalGAIL: Visual Attention-empowered Imitation Learning of Pedestrian Behavior in Critical Traffic Scenarios
SN - 978-989-758-475-6
IS - 2184-3236
AU - Vozniak, I.
AU - Klusch, M.
AU - Antakli, A.
AU - Müller, C.
PY - 2020
SP - 325
EP - 337
DO - 10.5220/0010020003250337
PB - SciTePress

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