  Deep reinforcement learning (DRL) provides a promising way for learning
navigation in complex autonomous driving scenarios. However, identifying the
subtle cues that can indicate drastically different outcomes remains an open
problem with designing autonomous systems that operate in human environments.
In this work, we show that explicitly inferring the latent state and encoding
spatial-temporal relationships in a reinforcement learning framework can help
address this difficulty. We encode prior knowledge on the latent states of
other drivers through a framework that combines the reinforcement learner with
a supervised learner. In addition, we model the influence passing between
different vehicles through graph neural networks (GNNs). The proposed framework
significantly improves performance in the context of navigating T-intersections
compared with state-of-the-art baseline approaches.
