{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T05:12:22Z","timestamp":1779340342614,"version":"3.51.4"},"reference-count":54,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T00:00:00Z","timestamp":1778457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>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 \u2192 representation \u2192 reasoning \u2192 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.<\/jats:p>","DOI":"10.3390\/make8050126","type":"journal-article","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T12:05:22Z","timestamp":1778501122000},"page":"126","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Knowledge Graphs in Autonomous Driving: Construction, Integration, and Real-Time Reasoning"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8689-2753","authenticated-orcid":false,"given":"Patrik","family":"Viktor","sequence":"first","affiliation":[{"name":"Department of Marketing, Management and Methodology, Keleti K\u00e1roly Faculty of Business and Management, Obuda University, 1034 Budapest, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0447-9376","authenticated-orcid":false,"given":"G\u00e1bor","family":"Kiss","sequence":"additional","affiliation":[{"name":"Institute of Safety Science and Cybersecurity, Obuda University, 1034 Budapest, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Luettin, J., Monka, S., Henson, C., and Halilaj, L. (2022, January 21\u201323). A Survey on Knowledge Graph-Based Methods for Automated Driving. Proceedings of the Iberoamerican Knowledge Graphs and Semantic Web Conference, Madrid, Spain.","DOI":"10.1007\/978-3-031-21422-6_2"},{"key":"ref_2","unstructured":"(2018). Road Vehicles Functional Safety (Standard No. ISO 26262)."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1002\/rob.20147","article-title":"Stanley: The Robot That Won the DARPA Grand Challenge","volume":"23","author":"Thrun","year":"2006","journal-title":"J. Field Robot."},{"key":"ref_4","first-page":"589","article-title":"The European Commission\u2019s Proposal for an Artificial Intelligence Act\u2014A Critical Assessment by Members of the Robotics and AI Law Society (RAILS)","volume":"4","author":"Ebers","year":"2021","journal-title":"J"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1002\/rob.21918","article-title":"A Survey of Deep Learning Techniques for Autonomous Driving","volume":"37","author":"Grigorescu","year":"2020","journal-title":"J. Field Robot."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1109\/TITS.2020.2972974","article-title":"Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges","volume":"22","author":"Feng","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Htun, S.N.N., and Fukuda, K. (2024). Integrating Knowledge Graphs into Autonomous Vehicle Technologies: A Survey of Current State and Future Directions. Information, 15.","DOI":"10.3390\/info15100645"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"96331","DOI":"10.1109\/ACCESS.2022.3202545","article-title":"A Survey of Traversability Estimation for Mobile Robots","volume":"10","author":"Sevastopoulos","year":"2022","journal-title":"IEEE Access"},{"key":"ref_10","unstructured":"Guarino, N. (1998). Completeness and Quality of an Ontology for an Information System. Formal Ontology in Information Systems: Proceedings of the First International Conference (FOIS\u201998), IOS Press."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"12878","DOI":"10.1109\/TPAMI.2022.3200245","article-title":"TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving","volume":"45","author":"Chitta","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_12","unstructured":"ASAM (2024). ASAM OpenDRIVE\u00ae Specification, Association for Standardization of Automation and Measuring Systems (ASAM)."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Bagschik, G., Menzel, T., and Maurer, M. (2018). Ontology Based Scene Creation for the Development of Automated Vehicles. Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, China, 26\u201330 June 2018, IEEE.","DOI":"10.1109\/IVS.2018.8500632"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Baader, F., Horrocks, I., Lutz, C., and Sattler, U. (2017). An Introduction to Description Logic, Cambridge University Press.","DOI":"10.1017\/9781139025355"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Belen-Saglam, R., Yuan, H., Heering, M.S., Ashraf, R., and Li, S. (2025). A Systematic Literature Review on Cyber Security and Privacy Risks in MaaS (Mobility-as-a-Service) Systems. Information, 16.","DOI":"10.3390\/info16070514"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Melo Castillo, A.N., Salinas Maldonado, C., Heering, M.S., Ashraf, R., and Li, S. (2025). Towards Explainable Pedestrian Behavior Prediction: A Neuro-Symbolic Framework for Autonomous Driving. Appl. Sci., 15.","DOI":"10.3390\/app15116283"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., and Guestrin, C. (2016). \u201cWhy Should I Trust You?\u201d: Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD \u201916), San Francisco, CA, USA, 13\u201317 August 2016, ACM.","DOI":"10.1145\/2939672.2939778"},{"key":"ref_18","unstructured":"Lundberg, S.M., and Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems 30 (NeurIPS 2017), Curran Associates, Inc."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Qiu, H., Ayara, A., and Glimm, B. (2020). Ontology-Based Processing of Dynamic Maps in Automated Driving. Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020)\u2013KEOD, Virtual Event, 2\u20134 November 2020, SciTePress.","DOI":"10.5220\/0010133900980107"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., and Beijbom, O. (2020). nuScenes: A Multimodal Dataset for Autonomous Driving. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 13\u201319 June 2020, IEEE\/CVF.","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Mlodzian, L., Sun, Z., Berkemeyer, H., Monka, S., Wang, Z., Dietze, S., Halilaj, L., and Luettin, J. (2023). nuScenes Knowledge GraphA Comprehensive Semantic Representation of Traffic Scenes for Trajectory Prediction. Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW), Workshop on Scene Graphs and Graph Representation Learning (SG2RL), Paris, France, 2 October 2023, IEEE.","DOI":"10.1109\/ICCVW60793.2023.00011"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Hartjen, L., Schuldt, F., and Friedrich, B. (2019). Semantic Classification of Pedestrian Traffic Scenarios for the Validation of Automated Driving. Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, 27\u201330 October 2019, IEEE.","DOI":"10.1109\/ITSC.2019.8917485"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1023\/A:1022683529158","article-title":"Extracting Refined Rules from Knowledge-Based Neural Networks","volume":"13","author":"Towell","year":"1993","journal-title":"Mach. Learn."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Codevilla, F., M\u00fcller, M., L\u00f3pez, A., Koltun, V., and Dosovitskiy, A. (2018). End-to-End Driving via Conditional Imitation Learning. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia, 21\u201325 May 2018, IEEE.","DOI":"10.1109\/ICRA.2018.8460487"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/0950-7051(96)81920-4","article-title":"Survey and Critique of Techniques for Extracting Rules from Trained Artificial Neural Networks","volume":"8","author":"Andrews","year":"1995","journal-title":"Knowl.-Based Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1109\/TNNLS.2021.3070843","article-title":"A Survey on Knowledge Graphs: Representation, Acquisition, and Applications","volume":"33","author":"Ji","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_27","first-page":"71","article-title":"Knowledge Graphs","volume":"54","author":"Hogan","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"ref_28","unstructured":"Bordes, A., Usunier, N., Garcia-Dur\u00e1n, A., Weston, J., and Yakhnenko, O. (2013). Translating Embeddings for Modeling Multi-Relational Data. Advances in Neural Information Processing Systems 26 (NeurIPS 2013), Curran Associates, Inc."},{"key":"ref_29","unstructured":"Sun, Z., Deng, Z.-H., Nie, J.-Y., and Tang, J. (2019, January 6\u20139). RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space. Proceedings of the International Conference on Learning Representations (ICLR), New Orleans, LA, USA."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1109\/JPROC.2015.2483592","article-title":"A Review of Relational Machine Learning for Knowledge Graphs","volume":"104","author":"Nickel","year":"2016","journal-title":"Proc. IEEE"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1568","DOI":"10.1109\/TBDATA.2025.3527208","article-title":"Big Data-Driven Advancements and Future Directions in Vehicle Perception Technologies: From Autonomous Driving to Modular Buses","volume":"11","author":"Lin","year":"2025","journal-title":"IEEE Trans. Big Data"},{"key":"ref_32","unstructured":"ASAM (2024). ASAM OpenSCENARIO\u00ae Specification, Association for Standardization of Automation and Measuring Systems (ASAM)."},{"key":"ref_33","unstructured":"ASAM (2024). ASAM OpenLABEL\u00ae Specification, Association for Standardization of Automation and Measuring Systems (ASAM). Version 1.0.0."},{"key":"ref_34","unstructured":"Open Geospatial Consortium (2021). OGC City Geography Markup Language (CityGML) Encoding Standard, Open Geospatial Consortium (OGC). Version 3.0."},{"key":"ref_35","unstructured":"(2023). Intelligent Transport Systems (ITS); Access Layer Specification for Intelligent Transport Systems Operating in the 5 GHz Frequency Band (Standard No. ETSI EN 302 663)."},{"key":"ref_36","unstructured":"European Commission (2023). Cooperative Intelligent Transport Systems (C-ITS): Technical Specifications and Deployment Guidelines, Publications Office of the European Union."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1162","DOI":"10.1109\/JPROC.2011.2132790","article-title":"Dedicated Short-Range Communications (DSRC) Standards in the United States","volume":"99","author":"Kenney","year":"2011","journal-title":"Proc. IEEE"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1109\/MCOM.2014.6979970","article-title":"Cooperative Intelligent Transport Systems Standards in Europe","volume":"52","author":"Festag","year":"2014","journal-title":"IEEE Commun. Mag."},{"key":"ref_39","unstructured":"(2010). IEEE Standard for Information Technology\u2014Telecommunications and Information Exchange between Systems\u2014Local and Metropolitan Area Networks\u2014Specific Requirements\u2014Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications Amendment 6: Wireless Access in Vehicular Environments (Standard No. IEEE Std 802.11p-2010)."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"35368","DOI":"10.1109\/ACCESS.2020.2973706","article-title":"3GPP NR Sidelink Transmissions toward 5G V2X","volume":"8","author":"Lien","year":"2020","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Campolo, C., Molinaro, A., and Berthet, A.O. (2017). Full-Duplex Communications to Improve Platooning Control in Multi-Channel VANETs. Proceedings of the 2017 IEEE International Conference on Communications Workshops (ICC Workshops), Paris, France, 21\u201325 May 2017, IEEE.","DOI":"10.1109\/ICCW.2017.7962779"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MVT.2017.2752798","article-title":"LTE-V for Sidelink 5G V2X Vehicular Communications: A New 5G Technology for Short-Range Vehicle-to-Everything Communications","volume":"12","author":"Gozalvez","year":"2017","journal-title":"IEEE Veh. Technol. Mag."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Yu, H., Luo, Y., Shu, M., Huo, Y., Yang, Z., Shi, Y., Guo, Y., Wang, Z., Li, Y., and Nie, Z. (2022). DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, 19\u201324 June 2022, IEEE\/CVF.","DOI":"10.1109\/CVPR52688.2022.02067"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"30255","DOI":"10.1109\/JSEN.2025.3582040","article-title":"Collaborative Perception Datasets for Autonomous Driving: A Review","volume":"25","author":"Wang","year":"2025","journal-title":"IEEE Sens. J."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Tang, Z., Naphade, M., Liu, M.-Y., Yang, X., Birchfield, S., Wang, S., Kumar, R., Anastasiu, D.C., and Hwang, J.-N. (2019). CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 15\u201320 June 2019, IEEE\/CVF.","DOI":"10.1109\/CVPR.2019.00900"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"e10","DOI":"10.1017\/S0269888921000084","article-title":"A Comprehensive Overview of RDF for Spatial and Spatiotemporal Data Management","volume":"36","author":"Zhang","year":"2021","journal-title":"Knowl. Eng. Rev."},{"key":"ref_47","unstructured":"Marcus, G. (2018). Deep Learning: A Critical Appraisal. arXiv."},{"key":"ref_48","unstructured":"(2023). Dedicated Short Range Communications (DSRC) Message Set Dictionary (Standard No. SAE J2735_202309)."},{"key":"ref_49","unstructured":"(2019). Road Vehicles Safety of the Intended Functionality (SOTIF) (Standard No. ISO 21448)."},{"key":"ref_50","unstructured":"Liang, Y., Du, J., Yang, Z., Huang, Y., and Chen, H. (2026). CURVE: Learning Causality-Inspired Invariant Representations for Robust Scene Understanding via Uncertainty-Guided Regularization. arXiv."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"12809","DOI":"10.1007\/s00521-024-09960-z","article-title":"Neuro-Symbolic Artificial Intelligence: A Survey","volume":"36","author":"Bhuyan","year":"2024","journal-title":"Neural Comput. Appl."},{"key":"ref_52","unstructured":"(2022). Road Vehicles\u2014Safety of the Intended Functionality (SOTIF) (Standard No. ISO 21448:2022)."},{"key":"#cr-split#-ref_53.1","unstructured":"European Union (2024). Regulation"},{"key":"#cr-split#-ref_53.2","unstructured":"(EU) 2024\/1689 (Artificial Intelligence Act), Official Journal of the European Union."}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/8\/5\/126\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T04:54:40Z","timestamp":1779339280000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/8\/5\/126"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,11]]},"references-count":54,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["make8050126"],"URL":"https:\/\/doi.org\/10.3390\/make8050126","relation":{},"ISSN":["2504-4990"],"issn-type":[{"value":"2504-4990","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,11]]}}}