# A Survey on Knowledge Graph-based Methods for Automated Driving arXiv: https://arxiv.org/abs/2210.08119 ยท HTML: https://mlsanity.com/paper/2210.08119/ ## Key facts - Cited by 8 papers in the map (data through 14 Oct 2026). - Builds on 33 of its 118 references that are in the map. - First posted on arXiv 30 Sep 2022. ## Builds on (most cited first, 30 of 33) - [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016) - [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017) - [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019) - [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014) - [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020) - [Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2001.05566/) (2020) - [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020) - [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020) - [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019) - [Deep Learning for Generic Object Detection: A Survey](https://mlsanity.com/paper/1809.02165/) (2018) - [A Review of Relational Machine Learning for Knowledge Graphs](https://mlsanity.com/paper/1503.00759/) (2015) - [A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications](https://mlsanity.com/paper/1709.07604/) (2017) - [A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles](https://mlsanity.com/paper/1604.07446/) (2016) - [Graph Embedding Techniques, Applications, and Performance: A Survey](https://mlsanity.com/paper/1705.02801/) (2017) - [Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art](https://mlsanity.com/paper/1704.05519/) (2017) - [Machine Learning on Graphs: A Model and Comprehensive Taxonomy](https://mlsanity.com/paper/2005.03675/) (2020) - [Self-Driving Cars: A Survey](https://mlsanity.com/paper/1901.04407/) (2019) - [Scene Graph Generation with External Knowledge and Image Reconstruction](https://mlsanity.com/paper/1904.00560/) (2019) - [Bridging Knowledge Graphs to Generate Scene Graphs](https://mlsanity.com/paper/2001.02314/) (2020) - [GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Driving](https://mlsanity.com/paper/1907.07792/) (2019) - [DAGMapper: Learning to Map by Discovering Lane Topology](https://mlsanity.com/paper/2012.12377/) (2020) - [LinkNet: Relational Embedding for Scene Graph](https://mlsanity.com/paper/1811.06410/) (2018) - [Ontology based Scene Creation for the Development of Automated Vehicles](https://mlsanity.com/paper/1704.01006/) (2017) - [Deep Learning-based Vehicle Behaviour Prediction For Autonomous Driving Applications: A Review](https://mlsanity.com/paper/1912.11676/) (2019) - [SOSA: A Lightweight Ontology for Sensors, Observations, Samples, and Actuators](https://mlsanity.com/paper/1805.09979/) (2018) - [6-Layer Model for a Structured Description and Categorization of Urban Traffic and Environment](https://mlsanity.com/paper/2012.06319/) (2020) - [An Evaluation of Knowledge Graph Embeddings for Autonomous Driving Data: Experience and Practice](https://mlsanity.com/paper/2003.00344/) (2020) - [Using Ontologies for the Formalization and Recognition of Criticality for Automated Driving](https://mlsanity.com/paper/2205.01532/) (2022) - [An introduction to Graph Data Management](https://mlsanity.com/paper/1801.00036/) (2017) - [Learning Visual Models using a Knowledge Graph as a Trainer](https://mlsanity.com/paper/2102.08747/) (2021) ## Built on by (most cited first, 8 of 8) - [BEV-TSR: Text-Scene Retrieval in BEV Space for Autonomous Driving](https://mlsanity.com/paper/2401.01065/) (2024) - [SemanticFormer: Holistic and Semantic Traffic Scene Representation for Trajectory Prediction using Knowledge Graphs](https://mlsanity.com/paper/2404.19379/) (2024) - [nuScenes Knowledge Graph -- A comprehensive semantic representation of traffic scenes for trajectory prediction](https://mlsanity.com/paper/2312.09676/) (2023) - [Bridging Large-Model Reasoning and Real-Time Control via Agentic Fast-Slow Planning](https://mlsanity.com/paper/2604.01681/) (2026) - [Predicting the Road Ahead: A Knowledge Graph based Foundation Model for Scene Understanding in Autonomous Driving](https://mlsanity.com/paper/2503.18730/) (2025) - [Edge Case Detection in Automated Driving: Methods, Challenges, and Future Directions](https://mlsanity.com/paper/2410.08491/) (2024) - [Knowledge Graphs: The Future of Data Integration and Insightful Discovery](https://mlsanity.com/paper/2502.15689/) (2024) - [From Virtual Environments to Real-World Trials: Emerging Trends in Autonomous Driving](https://mlsanity.com/paper/2603.17714/) (2026) ## Abstract (arXiv) Automated driving is one of the most active research areas in computer science. Deep learning methods have made remarkable breakthroughs in machine learning in general and in automated driving (AD)in particular. However, there are still unsolved problems to guarantee reliability and safety of automated systems, especially to effectively incorporate all available information and knowledge in the driving task. Knowledge graphs (KG) have recently gained significant attention from both industry and academia for applications that benefit by exploiting structured, dynamic, and relational data. The complexity of graph-structured data with complex relationships and inter-dependencies between objects has posed significant challenges to existing machine learning algorithms. However, recent progress in knowledge graph embeddings and graph neural networks allows to applying machine learning to graph-structured data. Therefore, we motivate and discuss the potential benefit of KGs applied to the main tasks of AD including 1) ontologies 2) perception, 3) scene understanding, 4) motion planning, and 5) validation. Then, we survey, analyze and categorize ontologies and KG-based approaches for AD. We discuss current research challenges and propose promising future research directions for KG-based solutions for AD.