Dynamic Interaction-Aware Scene Understanding for Reinforcement Learning in Autonomous Driving

Dynamic Interaction-A ware Scene Understanding for Reinforcement Learning in Autonomous Driving Maria Huegle 1, Gabriel Kalweit 1, Moritz Werling 2 and Joschka Boedecker 1, 3 Abstract -- The common pipeline in autonomous driving systems is highly modular and includes a perception component which extracts lists of surrounding objects and passes these lists to a high-level decision component. In this case, leveraging the benefits of deep reinforcement learning for high-level decision making requires special architectures to deal with multiple variable-length sequences of different object types, such as vehicles, lanes or traffic signs. At the same time, the architecture has to be able to cover interactions between traffic participants in order to find the optimal action to be taken. In this work, we propose the novel Deep Scenes architecture, that can learn complex interaction-aware scene representations based on extensions of either 1) Deep Sets or 2) Graph Convolutional Networks. We present the Graph-Q and DeepScene-Q off-policy reinforcement learning algorithms, both outperforming state-of- the-art methods in evaluations with the publicly available traffic simulator SUMO. I. INTRODUCTION In autonomous driving scenarios, the number of traffic participants and lanes surrounding the agent can vary considerably over time. Common autonomous driving systems use modular pipelines, where a perception component extracts a list of surrounding objects and passes this list to other modules, including localization, mapping, motion planning and high-level decision making components.