Abstract
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 language | English |
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Title of host publication | Proceedings IEEE Symposium Intelligent Vehicles (IV 2019) |
Place of Publication | Piscataway, NJ, USA |
Publisher | IEEE |
Pages | 905-912 |
ISBN (Electronic) | 978-1-7281-0560-4 |
DOIs | |
Publication status | Published - 2019 |
Event | IEEE Intelligent Vehicles Symposium 2019 - Paris, France Duration: 9 Jun 2019 → 12 Jun 2019 |
Conference
Conference | IEEE Intelligent Vehicles Symposium 2019 |
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Abbreviated title | IV 2019 |
Country/Territory | France |
City | Paris |
Period | 9/06/19 → 12/06/19 |