  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.
