  Applying reinforcement learning to autonomous driving entails particular
challenges, primarily due to dynamically changing traffic flows. To address
such challenges, it is necessary to quickly determine response strategies to
the changing intentions of surrounding vehicles. This paper proposes a new
policy optimization method for safe driving using graph-based interaction-aware
constraints. In this framework, the motion prediction and control modules are
trained simultaneously while sharing a latent representation that contains a
social context. To reflect social interactions, we illustrate the movements of
agents in graph form and filter the features with the graph convolution
networks. This helps preserve the spatiotemporal locality of adjacent nodes.
Furthermore, we create feedback loops to combine these two modules effectively.
As a result, this approach encourages the learned controller to be safe from
dynamic risks and renders the motion prediction robust to abnormal movements.
In the experiment, we set up a navigation scenario comprising various
situations with CARLA, an urban driving simulator. The experiments show
state-of-the-art performance on navigation strategy and motion prediction
compared to the baselines.
