Knowledge Graphs in Autonomous Driving: Construction, Integration, and Real-Time Reasoning

Autonomous driving systems require the integration of heterogeneous sensor data, distributed V2X communication, and safety-critical decision-making into coherent and interpretable world models. This review provides a systematic analysis of knowledge graph (KG)-based approaches in autonomous driving between 2015 and 2025, following a PRISMA-aligned methodology. The literature is organised along a perception → representation → reasoning → decision taxonomy, covering traffic ontologies, V2X knowledge integration, dynamic KG updates, real-time reasoning architectures, and benchmark datasets. A clear shift from static representational ontologies toward predictive and, in a smaller subset, closed-loop validated neuro-symbolic architectures. Knowledge graphs emerge as semantic integration layers that improve contextual reasoning, explainability, and rule compliance in safety-critical environments. Key challenges include scalable real-time reasoning, standardised evaluation frameworks, and safety-aligned integration of learning-based components.