Deep Reinforcement Learning-Based Knowledge Graph Reasoning for Autonomous Driving systems

The rapid development of advanced sensing and artificial intelligence technologies, has advanced autonomous driving (AD) systems by providing intelligent route planning decisions. However, how to construct an interpretable and efficient decision-making method that can adapt to various complex driving scenarios has become an important and challenging research topic. In this article, a knowledge graph (KG) for AD systems is constructed based on heterogeneous data such as traffic rules and network information. A deep learning model combining bidirectional long short-term memory and conditional random field is used to achieve joint learning of entity recognition and relationship extraction. In order to make decisions on driving behaviors, this article introduces a deep reinforcement learning framework designed to perform knowledge reasoning over the driving KG, which integrates an integrated reward function and an action dropout mechanism. Experimental comparisons against the other advanced knowledge reasoning algorithms on a practical driving rules dataset validate the effectiveness and advantages of the proposed method, with an overall mean average precision exceeding 94%. The validity of the proposed method has also been verified on the simulation platform in different road scenarios such as multilane, roundabout, intersection and thru-junction.