{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T03:52:02Z","timestamp":1769831522906,"version":"3.49.0"},"reference-count":86,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"New Energy and Industrial Technology Development Organization (NEDO)","award":["JPNP20006"],"award-info":[{"award-number":["JPNP20006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Autonomous vehicles (AVs) represent a transformative innovation in transportation, promising enhanced safety, efficiency, and sustainability. Despite these promises, achieving robustness, reliability, and adherence to ethical standards in AV systems remains challenging due to the complexity of integrating diverse technologies. This survey reviews literature from 2017 to 2023, analyzing over 90 papers to explore the integration of knowledge graphs (KGs) into AV technologies. Our findings indicate that KGs significantly enhance AV systems by providing structured semantic understanding, improving real-time decision-making, and ensuring compliance with regulatory standards. The paper identifies that while KGs contribute to better environmental perception and contextual reasoning, challenges remain in their seamless integration with existing systems and in maintaining processing speed. We also address the ethical dimensions of AV decision-making, advocating for frameworks that prioritize safety and transparency. This review underscores the potential of KGs to address critical challenges in AV technologies, offering a hopeful and optimistic outlook for the development of robust, reliable, and socially responsible autonomous transportation solutions.<\/jats:p>","DOI":"10.3390\/info15100645","type":"journal-article","created":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T07:58:32Z","timestamp":1729065512000},"page":"645","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Integrating Knowledge Graphs into Autonomous Vehicle Technologies: A Survey of Current State and Future Directions"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0244-2502","authenticated-orcid":false,"given":"Swe Nwe Nwe","family":"Htun","sequence":"first","affiliation":[{"name":"National Institute of Advanced Industrial Science and Technology, Artificial Intelligence Research Center, Tokyo 135-0064, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7366-1094","authenticated-orcid":false,"given":"Ken","family":"Fukuda","sequence":"additional","affiliation":[{"name":"National Institute of Advanced Industrial Science and Technology, Artificial Intelligence Research Center, Tokyo 135-0064, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Guizilini, V.C., Li, J., Ambrus, R., and Gaidon, A. (2021, January 10\u201317). Geometric Unsupervised Domain Adaptation for Semantic Segmentation. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00842"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hou, R., Li, J., Bhargava, A., Ravent\u00f3s, A., Guizilini, V.C., Fang, C., Lynch, J.P., and Gaidon, A. (2020, January 14\u201319). Real-Time Panoptic Segmentation from Dense Detections. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00855"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"45","DOI":"10.5198\/jtlu.2019.1405","article-title":"Understanding autonomous vehicles: A systematic literature review on capability, impact, planning and policy","volume":"12","author":"Faisal","year":"2019","journal-title":"J. Transp. Land Use"},{"key":"ref_4","unstructured":"(2020). Waymo Safety Report, Waymo LLC."},{"key":"ref_5","unstructured":"(2023). 2023 Sustainability Report, General Motors (GM). Journey to Zero."},{"key":"ref_6","unstructured":"(2017). Automated Driving Systems, U.S. Department of Transportation, National Highway Traffic Safety Administration (NHTSA). A Vision for Safety."},{"key":"ref_7","unstructured":"(2024, September 30). Ministry of Industrial and Information Technology of the People\u2019s Republic of China, Available online: https:\/\/wap.miit.gov.cn\/jgsj\/zbys\/qcgy\/art\/2022\/art_6fae62605ce34907939028daf6021c48.html."},{"key":"ref_8","unstructured":"(2024, September 30). Society 5.0 and SIP Autonomous Driving. Available online: https:\/\/www.sip-adus.go.jp\/exhibition\/a2.html."},{"key":"ref_9","unstructured":"(2022). Automated Driving Safety Evaluation Framework Ver. 3.0, Japan Automobile Manufacturers Association, Inc.. Sectional Committee of AD Safety Evaluation, Automated Driving Subcommittee."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Xu, R., Xia, X., Li, J., Li, H., Zhang, S., Tu, Z., Meng, Z., Xiang, H., Dong, X., and Song, R. (2023, January 17\u201324). V2V4Real: A Real-World Large-Scale Dataset for Vehicle-to-Vehicle Cooperative Perception. Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01318"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ogunrinde, I.O., and Bernadin, S. (2023). Deep Camera\u2013Radar Fusion with an Attention Framework for Autonomous Vehicle Vision in Foggy Weather Conditions. Sensors, 23.","DOI":"10.20944\/preprints202305.2180.v1"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Alaba, S.Y., Gurbuz, A.C., and Ball, J.E. (2024). Emerging Trends in Autonomous Vehicle Perception: Multimodal Fusion for 3D Object Detection. World Electr. Veh. J., 15.","DOI":"10.3390\/wevj15010020"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Baczmanski, M., Synoczek, R., Wasala, M., and Kryjak, T. (2023, January 22\u201325). Detection-segmentation convolutional neural network for autonomous vehicle perception. Proceedings of the 2023 27th International Conference on Methods and Models in Automation and Robotics (MMAR), Miedzyzdroje, Poland.","DOI":"10.1109\/MMAR58394.2023.10242398"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Villaz\u00f3n-Terrazas, B., Ortiz-Rodriguez, F., Tiwari, S., Sicilia, M.A., and Mart\u00edn-Moncunill, D. (2022). A Survey on Knowledge Graph-Based Methods for Automated Driving. Knowledge Graphs and Semantic Web. KGSWC 2022. Communications in Computer and Information Science, Springer.","DOI":"10.1007\/978-3-031-21422-6"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"722","DOI":"10.3390\/futuretransp4030034","article-title":"Interactions and Behaviors of Pedestrians with Autonomous Vehicles: A Synthesis","volume":"4","author":"Rezwana","year":"2024","journal-title":"Future Transp."},{"key":"ref_16","first-page":"1","article-title":"Artificial intelligence-based traffic flow prediction: A comprehensive review","volume":"10","author":"Sayed","year":"2013","journal-title":"J. Electr. Syst. Inf. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liu, Q., Li, X., Tang, Y., Gao, X., Yang, F., and Li, Z. (2023). Graph Reinforcement Learning-Based Decision-Making Technology for Connected and Autonomous Vehicles: Framework, Review, and Future Trends. Sensors, 23.","DOI":"10.3390\/s23198229"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"58443","DOI":"10.1109\/ACCESS.2020.2983149","article-title":"A Survey of Autonomous Driving: Common Practices and Emerging Technologies","volume":"8","author":"Yurtsever","year":"2019","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"113816","DOI":"10.1016\/j.eswa.2020.113816","article-title":"Self-Driving Cars: A Survey","volume":"165","author":"Badue","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"122836","DOI":"10.1016\/j.eswa.2023.122836","article-title":"Autonomous driving system: A comprehensive survey","volume":"242","author":"Zhao","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Guizilini, V.C., Vasiljevic, I., Chen, D., Ambrus, R., and Gaidon, A. (2023, January 1\u20136). Towards Zero-Shot Scale-Aware Monocular Depth Estimation. Proceedings of the 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France.","DOI":"10.1109\/ICCV51070.2023.00847"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Irshad, M., Zakharov, S., Liu, K., Guizilini, V.C., Kollar, T., Gaidon, A., Kira, Z., and Ambrus, R. (2023, January 1\u20136). NeO 360: Neural Fields for Sparse View Synthesis of Outdoor Scenes. Proceedings of the 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France.","DOI":"10.1109\/ICCV51070.2023.00843"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Codevilla, F., Santana, E., L\u00f3pez, A.M., and Gaidon, A. (November, January 27). Exploring the Limitations of Behavior Cloning for Autonomous Driving. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00942"},{"key":"ref_24","unstructured":"DeCastro, J.A., Liebenwein, L., Vasile, C.I., Tedrake, R., Karaman, S., and Rus, D. (2018, January 9\u201311). Counterexample-Guided Safety Contracts for Autonomous Driving. Proceedings of the Workshop on the Algorithmic Foundations of Robotics, M\u00e9rida, Mexico."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1007\/s10514-018-9734-5","article-title":"Street-view change detection with deconvolutional networks","volume":"42","author":"Alcantarilla","year":"2016","journal-title":"Auton. Robot."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Guizilini, V.C., Li, J., Ambrus, R., Pillai, S., and Gaidon, A. (2020, January 16\u201318). Robust Semi-Supervised Monocular Depth Estimation with Reprojected Distances. Proceedings of the Conference on Robot Learning, Virtual.","DOI":"10.1109\/CVPR42600.2020.00256"},{"key":"ref_27","unstructured":"Ambrus, R., Guizilini, V.C., Li, J., Pillai, S., and Gaidon, A. (November, January 30). Two Stream Networks for Self-Supervised Ego-Motion Estimation. Proceedings of the Conference on Robot Learning, Osaka, Japan."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kanai, T., Vasiljevic, I., Guizilini, V.C., Gaidon, A., and Ambrus, R. (2023, January 1\u20135). Robust Self-Supervised Extrinsic Self-Calibration. Proceedings of the 2023 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Detroit, MI, USA.","DOI":"10.1109\/IROS55552.2023.10341367"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lee, K., Kliemann, M., Gaidon, A., Li, J., Fang, C., Pillai, S., and Burgard, W. (2020\u201324, January 24). PillarFlow: End-to-end Birds-eye-view Flow Estimation for Autonomous Driving. Proceedings of the 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA.","DOI":"10.1109\/IROS45743.2020.9340931"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Guizilini, V.C., Ambrus, R., Pillai, S., and Gaidon, A. (2020, January 14\u201319). 3D Packing for Self-Supervised Monocular Depth Estimation. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00256"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Manhardt, F., Kehl, W., and Gaidon, A. (2019, January 15\u201320). ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00217"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chiu, H., Li, J., Ambrus, R., and Bohg, J. (June, January 30). Probabilistic 3D Multi-Modal, Multi-Object Tracking for Autonomous Driving. Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi\u2019an, China.","DOI":"10.1109\/ICRA48506.2021.9561754"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Guizilini, V.C., Senanayake, R., and Ramos, F.T. (2019, January 20\u201324). Dynamic Hilbert Maps: Real-Time Occupancy Predictions in Changing Environments. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8793914"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Amini, A., Rosman, G., Karaman, S., and Rus, D. (2019, January 20\u201324). Variational End-to-End Navigation and Localization. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8793579"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Koide, K., Yokozuka, M., Oishi, S., and Banno, A. (2022, January 23\u201327). Globally Consistent and Tightly Coupled 3D LiDAR Inertial Mapping. Proceedings of the 2022 International Conference on Robotics and Automation (ICRA), Philadelphia, PA, USA.","DOI":"10.1109\/ICRA46639.2022.9812385"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Koide, K., Oishi, S., Yokozuka, M., and Banno, A. (2024, January 13\u201317). Tightly Coupled Range Inertial Localization on a 3D Prior Map Based on Sliding Window Factor Graph Optimization. Proceedings of the 2024 IEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan.","DOI":"10.1109\/ICRA57147.2024.10611195"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"McAllister, R.T., Wulfe, B., Mercat, J., Ellis, L., Levine, S., and Gaidon, A. (2022, January 23\u201327). Control-Aware Prediction Objectives for Autonomous Driving. Proceedings of the 2022 International Conference on Robotics and Automation (ICRA), Philadelphia, PA, USA.","DOI":"10.1109\/ICRA46639.2022.9811884"},{"key":"ref_38","unstructured":"Lee, K., Ros, G., Li, J., and Gaidon, A. (May, January 30). SPIGAN: Privileged Adversarial Learning from Simulation. Proceedings of the International Conference on Learning Representations (ICLR), Vancouver, BC, Canada."},{"key":"ref_39","unstructured":"Guizilini, V.C., Hou, R., Li, J., Ambrus, R., and Gaidon, A. (2020, January 26\u201330). Semantically-Guided Representation Learning for Self-Supervised Monocular Depth. Proceedings of the International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia. Available online: https:\/\/openreview.net\/pdf?id=ByxT7TNFvH."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"DeCastro, J.A., Leung, K., Ar\u00e9chiga, N., and Pavone, M. (2020, January 20\u201323). Interpretable Policies from Formally-Specified Temporal Properties. Proceedings of the 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), Rhodes, Greece.","DOI":"10.1109\/ITSC45102.2020.9294442"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"12459","DOI":"10.1109\/LRA.2022.3219022","article-title":"Generalized LOAM: LiDAR Odometry Estimation With Trainable Local Geometric Features","volume":"7","author":"Honda","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"8591","DOI":"10.1109\/LRA.2021.3113043","article-title":"Globally Consistent 3D LiDAR Mapping With GPU-Accelerated GICP Matching Cost Factors","volume":"6","author":"Koide","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3485","DOI":"10.1109\/LRA.2020.2976305","article-title":"Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction","volume":"5","author":"Liu","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Ghaffari Jadidi, M., Clark, W., Bloch, A.M., Eustice, R.M., and Grizzle, J.W. (2019, January 22\u201326). Continuous Direct Sparse Visual Odometry from RGB-D Images. Proceedings of the Robotics: Science and Systems (RSS), Breisgau, Germany. Available online: https:\/\/www.roboticsproceedings.org\/rss15\/p44.pdf.","DOI":"10.15607\/RSS.2019.XV.044"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Cao, Z., Biyik, E., Wang, W.Z., Ravent\u00f3s, A., Gaidon, A., Rosman, G., and Sadigh, D. (2020, January 12\u201316). Reinforcement Learning based Control of Imitative Policies for Near-Accident Driving. Proceedings of the Robotics: Science and Systems (RSS), Virtually.","DOI":"10.15607\/RSS.2020.XVI.039"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Gideon, J., Stent, S., and Fletcher, L. (2018, January 15\u201320). A Multi-Camera Deep Neural Network for Detecting Elevated Alertness in Drivers. Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada.","DOI":"10.1109\/ICASSP.2018.8461986"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Mangalam, K., Adeli, E., Lee, K., Gaidon, A., and Niebles, J. (2019, January 1\u20135). Disentangling Human Dynamics for Pedestrian Locomotion Forecasting with Noisy Supervision. Proceedings of the 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), Snowmass Village, CO, USA.","DOI":"10.1109\/WACV45572.2020.9093350"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Aldibaja, M., Suganuma, N., Yoneda, K., and Yanase, R. (2022). Challenging Environments for Precise Mapping Using GNSS\/INS-RTK Systems: Reasons and Analysis. Remote Sens., 14.","DOI":"10.3390\/rs14164058"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Aldibaja, M., and Suganuma, N. (2021). Graph SLAM-Based 2.5D LIDAR Mapping Module for Autonomous Vehicles. Remote Sens., 13.","DOI":"10.3390\/rs13245066"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Aldibaja, M., Suganuma, N., and Yanase, R. (2022). 2.5D Layered Sub-Image LIDAR Maps for Autonomous Driving in Multilevel Environments. Remote Sens., 14.","DOI":"10.3390\/rs14225847"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Yoneda, K., Kuramoto, A., Suganuma, N., Asaka, T., Aldibaja, M., and Yanase, R. (2020). Robust Traffic Light and Arrow Detection Using Digital Map with Spatial Prior Information for Automated Driving. Sensors, 20.","DOI":"10.3390\/s20041181"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Yanase, R., Hirano, D., Aldibaja, M., Yoneda, K., and Suganuma, N. (2022). LiDAR- and Radar-Based Robust Vehicle Localization with Confidence Estimation of Matching Results. Sensors, 22.","DOI":"10.3390\/s22093545"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Aldibaja, M., Yanase, R., and Suganuma, N. (2024). Waypoint Transfer Module between Autonomous Driving Maps Based on LiDAR Directional Sub-Images. Sensors, 24.","DOI":"10.3390\/s24030875"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Yan, Z., Yang, B., Wang, Z., and Nakano, K. (2023). A Predictive Model of a Driver\u2019s Target Trajectory Based on Estimated Driving Behaviors. Sensors, 23.","DOI":"10.3390\/s23031405"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Nacpil, E.J., and Nakano, K. (2020). Surface Electromyography-Controlled Automobile Steering Assistance. Sensors, 20.","DOI":"10.3390\/s20030809"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.iatssr.2019.11.005","article-title":"Automated driving recognition technologies for adverse weather conditions","volume":"43","author":"Yoneda","year":"2019","journal-title":"Iatss Res."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Aldibaja, M., Suganuma, N., Yoneda, K., Yanase, R., and Kuramoto, A. (2017, January 16\u201318). Supervised Calibration Method for Improving Contrast and Intensity of LIDAR Laser Beams. Proceedings of the International Conference on Multisensor Fusion and Integration for Intelligent Systems, Daegu, South Korea.","DOI":"10.1007\/978-3-319-90509-9_12"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Yoneda, K., Kuramoto, A., and Suganuma, N. (2017, January 24\u201326). Convolutional neural network based vehicle turn signal recognition. Proceedings of the International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), Okinawa, Japan.","DOI":"10.1109\/ICIIBMS.2017.8279693"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"5938","DOI":"10.1109\/TCYB.2022.3219142","article-title":"Quantitative Evaluation Methodology for Chassis-Domain Dynamics Performance of Automated Vehicles","volume":"53","author":"Cheng","year":"2022","journal-title":"IEEE Trans. Cybern."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"24778","DOI":"10.1109\/TITS.2022.3193665","article-title":"Structural Transformer Improves Speed-Accuracy Trade-Off in Interactive Trajectory Prediction of Multiple Surrounding Vehicles","volume":"23","author":"Hou","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"24866","DOI":"10.1109\/TITS.2022.3195213","article-title":"Spatio-Temporal Image Representation and Deep-Learning-Based Decision Framework for Automated Vehicles","volume":"23","author":"Cheng","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"107018","DOI":"10.1016\/j.aap.2023.107018","article-title":"Impact of duration of monitoring before takeover request on takeover time with insights into eye tracking data","volume":"185","author":"Huang","year":"2023","journal-title":"Accid. Anal. Prev."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Shimizu, T., Koide, K., Oishi, S., Yokozuka, M., Banno, A., and Shino, M. (2021, January 10\u201315). Sensor-independent Pedestrian Detection for Personal Mobility Vehicles in Walking Space Using Dataset Generated by Simulation. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9413329"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1109\/OJITS.2022.3222442","article-title":"Classification of Automated Lane-Change Styles by Modeling and Analyzing Truck Driver Behavior: A Driving Simulator Study","volume":"3","author":"Wang","year":"2022","journal-title":"IEEE Open J. Intell. Transp. Syst."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3447772","article-title":"Knowledge Graphs","volume":"54","author":"Hogan","year":"2020","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1616","DOI":"10.1109\/TKDE.2018.2807452","article-title":"A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications","volume":"30","author":"Cai","year":"2017","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_67","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":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Lilis, Y., Zidianakis, E., Partarakis, N., Antona, M., and Stephanidis, C. (2017). Personalizing HMI Elements in ADAS Using Ontology Meta-Models and Rule Based Reasoning. Universal Access in Human\u2013Computer Interaction. Design and Development Approaches and Methods, Proceedings of the 11th International Conference, UAHCI 2017, Held as Part of HCI International 2017, Vancouver, BC, Canada, 9\u201314 July 2017, Springer.","DOI":"10.1007\/978-3-319-58706-6_31"},{"key":"ref_69","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio\u2019, P., and Bengio, Y. (May, January 30). Graph Attention Networks. Proceedings of the 6th International Conference on Learning Representations (ICLR), Conference Track Proceedings, Vancouver, BC, Canada."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"477","DOI":"10.3233\/SW-212959","article-title":"A Survey on Visual Transfer Learning using Knowledge Graphs","volume":"13","author":"Monka","year":"2022","journal-title":"Semant. Web"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Halilaj, L., Dindorkar, I., L\u00fcttin, J., and Rothermel, S. (2021, January 6\u201310). A Knowledge Graph-Based Approach for Situation Comprehension in Driving Scenarios. Proceedings of the Extended Semantic Web Conference, Heraklion, Greece.","DOI":"10.1007\/978-3-030-77385-4_42"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"108245","DOI":"10.1016\/j.knosys.2022.108245","article-title":"roadscene2vec: A Tool for Extracting and Embedding Road Scene-Graphs","volume":"242","author":"Malawade","year":"2021","journal-title":"Knowl. Based Syst."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Zipfl, M., and Z\u00f6llner, J.M. (2022, January 8\u201312). Towards Traffic Scene Description: The Semantic Scene Graph. Proceedings of the 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), Macau, China.","DOI":"10.1109\/ITSC55140.2022.9922469"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Mlodzian, L., Sun, Z., Berkemeyer, H., Monka, S., Wang, Z., Dietze, S., Halilaj, L., and Luettin, J. (2023, January 2\u20136). nuScenes Knowledge Graph\u2014A comprehensive semantic representation of traffic scenes for trajectory prediction. Proceedings of the 2023 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW), Paris, France.","DOI":"10.1109\/ICCVW60793.2023.00011"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"5103","DOI":"10.1109\/LRA.2022.3145952","article-title":"Learnable Online Graph Representations for 3D Multi-Object Tracking","volume":"7","author":"Zaech","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_76","unstructured":"Arai, K. (2021). Accelerating Road Sign Ground Truth Construction with Knowledge Graph and Machine Learning. Intelligent Computing. Lecture Notes in Networks and Systems, Springer."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"7941","DOI":"10.1109\/TITS.2021.3074854","article-title":"Scene-Graph Augmented Data-Driven Risk Assessment of Autonomous Vehicle Decisions","volume":"23","author":"Yu","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Bagschik, G., Menzel, T., and Maurer, M. (2018, January 26\u201330). Ontology based Scene Creation for the Development of Automated Vehicles. Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), Suzhou, China.","DOI":"10.1109\/IVS.2018.8500632"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Urbieta, I.R., Nieto, M., Garc\u00eda, M., and Otaegui, O. (2021). Design and Implementation of an Ontology for Semantic Labeling and Testing: Automotive Global Ontology (AGO). Appl. Sci., 11.","DOI":"10.3390\/app11177782"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"8615","DOI":"10.1109\/LRA.2021.3111433","article-title":"Lane Graph Estimation for Scene Understanding in Urban Driving","volume":"6","author":"Vertens","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_81","unstructured":"Li, T., Chen, L., Geng, X., Wang, H., Li, Y., Liu, Z., Jiang, S., Wang, Y., Xu, H., and Xu, C. (2023). Graph-based Topology Reasoning for Driving Scenes. arXiv."},{"key":"ref_82","unstructured":"Wickramarachchi, R., Henson, C.A., and Sheth, A. (2020, January 23\u201325). An Evaluation of Knowledge Graph Embeddings for Autonomous Driving Data: Experience and Practice. Proceedings of the AAAI Spring Symposium Combining Machine Learning with Knowledge Engineering, Palo Alto, CA, USA."},{"key":"ref_83","first-page":"1","article-title":"Spatio-Temporal Interaction Aware and Trajectory Distribution Aware Graph Convolution Network for Pedestrian Multimodal Trajectory Prediction","volume":"72","author":"Wang","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"7381","DOI":"10.1109\/LRA.2024.3426386","article-title":"SemanticFormer: Holistic and Semantic Traffic Scene Representation for Trajectory Prediction Using Knowledge Graphs","volume":"9","author":"Sun","year":"2024","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Sun, R., Lingrand, D., and Precioso, F. (2023, January 2\u20136). Exploring the Road Graph in Trajectory Forecasting for Autonomous Driving. Proceedings of the 2023 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW), Paris, France.","DOI":"10.1109\/ICCVW60793.2023.00014"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"2957","DOI":"10.1007\/s11948-020-00252-y","article-title":"The Boeing 737 MAX: Lessons for Engineering Ethics","volume":"26","author":"Herkert","year":"2020","journal-title":"Sci. Eng. Ethics"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/10\/645\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:14:22Z","timestamp":1760112862000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/10\/645"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"references-count":86,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["info15100645"],"URL":"https:\/\/doi.org\/10.3390\/info15100645","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}