Learning an Explainable Trajectory Generator Using the Automaton Generative Network (AGN)
Xiao Li, Guy Rosman, Igor Gilitschenski, Brandon Araki, Cristian-Ioan Vasile, Sertac Karaman, Daniela Rus
Abstract
Symbolic reasoning is a key component for enabling practical use of data-driven planners in autonomous driving. In that context, deterministic finite state automata (DFA) are often used to formalize the underlying high-level decision-making process. Manual design of an effective DFA can be tedious. In combination with deep learning pipelines, DFA can serve as an effective representation to learn and process complex behavioral patterns. The goal of this work is to leverage that potential. We propose the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">automaton generative network</i> (AGN), a differentiable representation of DFAs. The resulting neural network module can be used standalone or as an embedded component within a larger architecture. In evaluations on deep learning based autonomous vehicle planning tasks, we demonstrate that incorporating AGN improves the explainability, sample efficiency, and generalizability of the model.
BibTeX
@inproceedings{ral2022_learninganexplai,
title = {Learning an Explainable Trajectory Generator Using the Automaton Generative Network (AGN)},
author = {Xiao Li and Guy Rosman and Igor Gilitschenski and Brandon Araki and Cristian-Ioan Vasile and Sertac Karaman and Daniela Rus},
booktitle = {RA-L 2022},
year = {2022}
}