Deep Reinforcement Learning for Autonomous Driving using High-Level Heterogeneous Graph Representations

Abstract

Graph networks have recently been used for decision making in automated driving tasks for their ability to capture a variable number of traffic participants. Current high-level graph-based approaches, however, do not model the entire road network and thus must rely on handcrafted features for vehicle-to-vehicle edges encompassing the road topology indirectly. We propose an entity-relation framework that intuitively models the road network and the traffic participants in a heterogeneous graph, representing all relevant information. Our novel architecture transforms the heterogeneous road-vehicle graph into a simpler graph of homogeneous node and edge types to allow effective training for deep reinforcement learning while introducing minimal prior knowledge. Unlike previous approaches, the vehicle-to-vehicle edges of this reduced graph are fully learnable and can therefore encode traffic rules without explicit feature design, an important step towards a holistic reinforcement learning model for automated driving. We show that our proposed method outperforms precomputed handcrafted features on intersection scenarios while also learning the semantics of right-of-way rules.