Navigating urban environments represents a complex task for automated vehicles. They must reach their goal safely and efficiently while considering a multitude of traffic participants. We propose a modular decision making algorithm to autonomously navigate intersections, addressing challenges of existing rule-based and reinforcement learning (RL) approaches. We first present a safe RL algorithm relying on a model-checker to ensure safety guarantees. To make the decision strategy robust to perception errors and occlusions, we introduce a belief update technique using a learning based approach. Finally, we use a scene decomposition approach to scale our algorithm to environments with multiple traffic participants. We empirically demonstrate that our algorithm outperforms rule-based methods and reinforcement learning techniques on a complex intersection scenario.
@article{arxiv.1904.11483,
title = {Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environments},
author = {Maxime Bouton and Alireza Nakhaei and Kikuo Fujimura and Mykel J. Kochenderfer},
journal= {arXiv preprint arXiv:1904.11483},
year = {2019}
}