This paper introduces DSceneKG, a suite of knowledge graphs representing real-world driving scenes from multiple autonomous driving datasets. The researchers argue that traditional benchmark datasets are insufficient for evaluating the capabilities of Neurosymbolic AI, which combines symbolic knowledge representations with sub-symbolic AI techniques. DSceneKG aims to address this gap by providing a more realistic and practical benchmark for evaluating Neurosymbolic AI methods in autonomous driving scenarios. The paper details the development of DSceneKG and showcases its application in seven different tasks, including entity prediction, scene clustering, semantic search, and cross-modal retrieval.