Composable Q- functions for pedestrian car interactions

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We propose a novel algorithm that predicts the interaction of pedestrians with cars within a Markov Decision Process framework. It leverages the fact that Q-functions may be composed in the maximum-entropy framework, thus the solutions of two sub-tasks may be combined to approximate the full interaction problem. Sub-task one is the interaction-free navigation of a pedestrian in an urban environment and sub-task two is the interaction with an approaching car (deceleration, waiting etc.) without accounting for the environmental context (e.g. street layout). We propose a regularization scheme motivated by the soft-Bellman-equations and illustrate its necessity. We then analyze the properties of the algorithm in detail with a toy model. We find that as long as the interaction-free sub-task is modelled well with a Q-function, we can learn a representation of the interaction between a pedestrian and a car.
Original languageEnglish
Title of host publicationProceedings IEEE Symposium Intelligent Vehicles (IV 2019)
Place of PublicationPiscataway, NJ, USA
ISBN (Electronic)978-1-7281-0560-4
Publication statusPublished - 2019
EventIEEE Intelligent Vehicles Symposium 2019 - Paris, France
Duration: 9 Jun 201912 Jun 2019


ConferenceIEEE Intelligent Vehicles Symposium 2019
Abbreviated titleIV 2019


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