Abstract
In the area of autonomous driving there is a need to flexibly configure and simulate more complex individual pedestrian behavior in critical traffic scenes which goes beyond predefined behavior simulation. This paper presents a novel human-oriented, agent-based pedestrian simulation framework, named HAIL, that addresses this challenge. HAIL allows to simulate human pedestrian behavior through means of imitation learning by virtual agents. For this purpose, HAIL combines the 3D traffic simulation environment OpenDS with an integrated imitation learning environment and hybrid agents with AJAN. For predictive behavior planning on the tactical and strategical level, AJAN is extended with Answer Set Programming. For pedestrian behavior imitation learning on the operational level, HAIL utilizes the module InfoSalGAIL for generation of pedestrian paths learned from demonstration by its human counterpart as expert. Among others, an application example has been demonstrated that HAIL can be applied to solve a common challenge in the Neural Network domain, namely the out-of-distribution (OOD), e.g. never shown scenarios would raise an uncertainty prediction level, by unison work of the two different behavior generation frameworks.
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Notes
- 1.
Carla: https://carla.org/.
- 2.
LGSVL: https://www.lgsvlsimulator.com/.
- 3.
OpenDS - open source driving simulation: https://opends.dfki.de/.
- 4.
- 5.
VADERE Crowd simulation: http://www.vadere.org/.
- 6.
PDESIM - pedestrian crowd simulation: http://pedsim.silmaril.org/.
- 7.
- 8.
AJAN-service: https://github.com/aantakli/AJAN-service AJAN-editor: https://github.com/aantakli/AJAN-editor.
- 9.
Not like EKs, where only the corresponding agent behavior has access to.
- 10.
AJAN uses LibGDX-BTs: https://github.com/libgdx/gdx-ai/wiki/Behavior-Trees.
- 11.
Upon initialization, an AJAN agent receives RDF descriptions of available InfoSalGAIL models defining trained street configurations.
- 12.
- 13.
In AJAN we use the Potassco clingo solver: https://potassco.org/clingo/.
- 14.
Videos of the FoV of the pedestrian agent in scenario A can be found at: https://cloud.dfki.de/owncloud/index.php/s/HAf5wQtMAx3F9K5.
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Acknowledgements
The work described in this paper has been funded by the German Federal Ministry of Education and Research (BMBF) through the project REACT (grant no. 01/W17003) and in part by Huawei Munich Research Center.
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Antakli, A., Vozniak, I., Lipp, N., Klusch, M., Müller, C. (2021). HAIL: Modular Agent-Based Pedestrian Imitation Learning. In: Dignum, F., Corchado, J.M., De La Prieta, F. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Social Good. The PAAMS Collection. PAAMS 2021. Lecture Notes in Computer Science(), vol 12946. Springer, Cham. https://doi.org/10.1007/978-3-030-85739-4_3
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