Composable Q- functions for pedestrian car interactions
Christian Muench (Daimler AG, TU Delft - Mechanical Engineering)
Dariu Gavrila (TU Delft - Mechanical Engineering)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
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.