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HAIL: Modular Agent-Based Pedestrian Imitation Learning

André Antakl; Igor Vozniak; Nils Lipp; Matthias Klusch; Christian Müller
In: Proceedings of the 19th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS). International Conference on Practical Applications of Intelligent Agents and Multiagents (PAAMS-2021), October 6-8, Salamanca/Hybrid, Spain, Springer, 2021.


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 di erent behavior generation frameworks.