{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T16:18:57Z","timestamp":1775837937524,"version":"3.50.1"},"reference-count":72,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,4,7]],"date-time":"2024-04-07T00:00:00Z","timestamp":1712448000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>The advent of autonomous vehicles has heralded a transformative era in transportation, reshaping the landscape of mobility through cutting-edge technologies. Central to this evolution is the integration of artificial intelligence (AI), propelling vehicles into realms of unprecedented autonomy. Commencing with an overview of the current industry landscape with respect to Operational Design Domain (ODD), this paper delves into the fundamental role of AI in shaping the autonomous decision-making capabilities of vehicles. It elucidates the steps involved in the AI-powered development life cycle in vehicles, addressing various challenges such as safety, security, privacy, and ethical considerations in AI-driven software development for autonomous vehicles. The study presents statistical insights into the usage and types of AI algorithms over the years, showcasing the evolving research landscape within the automotive industry. Furthermore, the paper highlights the pivotal role of parameters in refining algorithms for both trucks and cars, facilitating vehicles to adapt, learn, and improve performance over time. It concludes by outlining different levels of autonomy, elucidating the nuanced usage of AI algorithms, and discussing the automation of key tasks and the software package size at each level. Overall, the paper provides a comprehensive analysis of the current industry landscape, focusing on several critical aspects.<\/jats:p>","DOI":"10.3390\/bdcc8040042","type":"journal-article","created":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T10:11:55Z","timestamp":1712571115000},"page":"42","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":174,"title":["Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2171-9623","authenticated-orcid":false,"given":"Divya","family":"Garikapati","sequence":"first","affiliation":[{"name":"Institute of Electrical and Electronics Engineers (IEEE), New York, NY 10016, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1779-5754","authenticated-orcid":false,"given":"Sneha Sudhir","family":"Shetiya","sequence":"additional","affiliation":[{"name":"Torc Robotics, Inc., Blacksburg, VA 24060, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bordoloi, U., Chakraborty, S., Jochim, M., Joshi, P., Raghuraman, A., and Ramesh, S. (2023, January 17\u201319). Autonomy-driven Emerging Directions in Software-defined Vehicles. Proceedings of the 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE), Antwerp, Belgium.","DOI":"10.23919\/DATE56975.2023.10136910"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1007\/s42154-022-00179-z","article-title":"Impact, challenges and prospect of software-defined vehicles","volume":"5","author":"Liu","year":"2022","journal-title":"Automot. Innov."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Nadikattu, R.R. (2019). New ways in artificial intelligence. Int. J. Comput. Trends Technol., 67.","DOI":"10.2139\/ssrn.3629063"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5625","DOI":"10.1109\/TITS.2023.3235774","article-title":"Cooperative Platoon Formation of Connected and Autonomous Vehicles: Toward Efficient Merging Coordination at Unsignalized Intersections","volume":"24","author":"Deng","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.dsm.2021.12.002","article-title":"Machine Learning-based approach: Global trends, research directions, and regulatory standpoints","volume":"4","author":"Pugliese","year":"2021","journal-title":"Data Sci. Manag."},{"key":"ref_6","unstructured":"(2024, January 28). SAE Industry Technologies Consortia\u2019s Automated Vehicle Safety Consortium AVSC Best Practice for Describing an Operational Design Domain: Conceptual Framework and Lexicon, AVSC00002202004, Revised April, 2020. Available online: https:\/\/www.sae.org\/standards\/content\/avsc00002202004\/."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3118","DOI":"10.1016\/j.trpro.2023.11.874","article-title":"Distributed ODD Awareness for Connected and Automated Driving","volume":"72","author":"Khastgir","year":"2023","journal-title":"Transp. Res. Procedia"},{"key":"ref_8","unstructured":"Jack, W., and Jon, B. (2024). Navigating Tomorrow: Advancements and Road Ahead in AI for Autonomous Vehicles, EasyChair. (No. 11955)."},{"key":"ref_9","unstructured":"Lillo, L.D., Gode, T., Zhou, X., Atzei, M., Chen, R., and Victor, T. (2023). Comparative safety performance of autonomous-and human drivers: A real-world case study of the waymo one service. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1101520","DOI":"10.3389\/fpsyg.2023.1101520","article-title":"(Mis-) use of standard Autopilot and Full Self-Driving (FSD) Beta: Results from interviews with users of Tesla\u2019s FSD Beta","volume":"14","author":"Nordhoff","year":"2023","journal-title":"Front. Psychol."},{"key":"ref_11","first-page":"505","article-title":"Regulating Driving Automation Safety","volume":"73","author":"Wansley","year":"2024","journal-title":"Emory Law J."},{"key":"ref_12","first-page":"100","article-title":"MBSE and Safety Lifecycle of AI-enabled systems in transportation","volume":"11","author":"Anton","year":"2023","journal-title":"Int. J. Open Inf. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4640069","DOI":"10.1155\/2023\/4640069","article-title":"Applying the operational design domain concept to vehicles equipped with advanced driver assistance systems for enhanced safety","volume":"2023","author":"Kang","year":"2023","journal-title":"J. Adv. Transp."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6206","DOI":"10.1109\/TITS.2021.3084396","article-title":"A Taxonomy and Survey of Edge Cloud Computing for Intelligent Transportation Systems and Connected Vehicles","volume":"23","author":"Arthurs","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Murphey, Y.L., Kolmanovsky, I., and Watta, P. (2022). AI-Enabled Technologies for Autonomous and Connected Vehicles, Springer.","DOI":"10.1007\/978-3-031-06780-8"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Vishnukumar, H., Butting, B., M\u00fcller, C., and Sax, E. (2017, January 7\u20138). Machine Learning and deep neural network\u2014Artificial intelligence core for lab and real-world test and validation for ADAS and autonomous vehicles: AI for efficient and quality test and validation. Proceedings of the 2017 Intelligent Systems Conference (IntelliSys), London, UK.","DOI":"10.1109\/IntelliSys.2017.8324372"},{"key":"ref_17","first-page":"00164","article-title":"Autonomous driving architectures: Insights of machine Learning and deep Learning algorithms","volume":"6","author":"Bachute","year":"2021","journal-title":"Mach. Learn. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1109\/JAS.2020.1003021","article-title":"Artificial intelligence applications in the development of autonomous vehicles: A survey","volume":"7","author":"Ma","year":"2020","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3614","DOI":"10.1109\/TITS.2023.3236274","article-title":"Autonomous Vehicles Security: Challenges and Solutions Using Blockchain and Artificial Intelligence","volume":"24","author":"Bendiab","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chu, M., Zong, K., Shu, X., Gong, J., Lu, Z., Guo, K., Dai, X., and Zhou, G. (2023, January 23\u201328). Work with AI and Work for AI: Autonomous Vehicle Safety Drivers\u2019 Lived Experiences. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, Hamburg, Germany.","DOI":"10.1145\/3544548.3581564"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hwang, M.H., Lee, G.S., Kim, E., Kim, H.W., Yoon, S., Talluri, T., and Cha, H.R. (2023). Regenerative braking control strategy based on AI algorithm to improve driving comfort of autonomous vehicles. Appl. Sci., 13.","DOI":"10.3390\/app13020946"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Grigorescu, S., Trasnea, B., Cocias, C., and Macesanu, G. (2020). A Survey of Deep Learning Techniques for Autonomous Driving. arXiv.","DOI":"10.1002\/rob.21918"},{"key":"ref_23","first-page":"21314","article-title":"Coderl: Mastering code generation through pretrained models and deep reinforcement Learning","volume":"35","author":"Le","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Allamanis, M. (2014, January 16\u201321). Learning Natural Coding Conventions. Proceedings of the SIGSOFT\/FSE\u201914: 22nd ACM SIGSOFT Symposium on the Foundations of Software Engineering, Hong Kong, China.","DOI":"10.1145\/2635868.2635883"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"104211","DOI":"10.1016\/j.engappai.2021.104211","article-title":"Recent advances in motion and behavior planning techniques for software architecture of autonomous vehicles: A state-of-the-art survey","volume":"101","author":"Sharma","year":"2021","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_26","unstructured":"Yoo, D.H. (2023). The Ethics of Artificial Intelligence From an Economics Perspective: Logical, Theoretical, and Legal Discussions in Autonomous Vehicle Dilemma. [Ph.D. Thesis, Universit\u00e0 degli Studi di Siena]."},{"key":"ref_27","first-page":"3","article-title":"Autonomous Vehicles for All?","volume":"1","author":"Khan","year":"2023","journal-title":"J. Auton. Transp. Syst."},{"key":"ref_28","unstructured":"Mensah, G.B. (2024, January 26). Artificial Intelligence and Ethics: A Comprehensive Review of Bias Mitigation, Transparency, and Accountability in AI Systems. Available online: https:\/\/www.researchgate.net\/profile\/George-Benneh-Mensah-2\/publication\/375744287_Artificial_Intelligence_and_Ethics_A_Comprehensive_Reviews_of_Bias_Mitigation_Transparency_and_Accountability_in_AI_Systems\/links\/656c8e46b86a1d521b2e2a16\/Artificial-Intelligence-and-Ethics-A-Comprehensive-Reviews-of-Bias-Mitigation-Transparency-and-Accountability-in-AI-Systems.pdf."},{"key":"ref_29","unstructured":"Stine, A.A.K., and Kavak, H. (2023). AI Assurance, Academic Press."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e48009","DOI":"10.2196\/48009","article-title":"Ethical considerations of using ChatGPT in health care","volume":"25","author":"Wang","year":"2023","journal-title":"J. Med. Internet Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1007\/s43681-023-00258-9","article-title":"From ethical AI frameworks to tools: A review of approaches","volume":"3","author":"Prem","year":"2023","journal-title":"AI Ethics"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hase, P., and Bansal, M. (2020). Evaluating explainable AI: Which algorithmic explanations help users predict model behavior?. arXiv.","DOI":"10.18653\/v1\/2020.acl-main.491"},{"key":"ref_33","first-page":"125","article-title":"Exploring Ethical Considerations in AI-driven Autonomous Vehicles: Balancing Safety and Privacy","volume":"2","author":"Kumar","year":"2024","journal-title":"J. Artif. Intell. Gen. Sci. (Jaigs)"},{"key":"ref_34","unstructured":"Vogel, M., Bruckmeier, T., and Cerbo, F.D. (2020). General Data Protection Regulation (GDPR) Infrastructure for Microservices and Programming Model. (10839099), U.S. Patent."},{"key":"ref_35","first-page":"451","article-title":"Operationalising AI governance through ethics-based auditing: An industry case study","volume":"3","author":"Floridi","year":"2023","journal-title":"Ethics"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Biswas, A., and Wang, H.-C. (2023). Autonomous Vehicles Enabled by the Integration of IoT, Edge Intelligence, 5G, and Blockchain. Sensors, 23.","DOI":"10.3390\/s23041963"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1607","DOI":"10.1109\/TSMC.2022.3203176","article-title":"Robust adaptive safety-critical control for unknown systems with finite-time element-wise parameter estimation","volume":"53","author":"Wang","year":"2023","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Madala, R., Vijayakumar, N., Verma, S., Chandvekar, S.D., and Singh, D.P. (2023, January 14\u201316). Automated AI research on cyber attack prediction and security design. Proceedings of the 2023 6th International Conference on Contemporary Computing and Informatics (IC3I), Gautam Buddha Nagar, India.","DOI":"10.1109\/IC3I59117.2023.10397798"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"102104","DOI":"10.1016\/j.ijinfomgt.2020.102104","article-title":"Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda","volume":"53","author":"Nishant","year":"2020","journal-title":"Int. J. Inf. Manag."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"107190","DOI":"10.1016\/j.infsof.2023.107190","article-title":"Detecting multi-type self-admitted technical debt with generative adversarial network-based neural networks","volume":"158","author":"Yu","year":"2023","journal-title":"Inf. Softw. Technol."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhuhadar, L.P., and Lytras, M.D. (2023). The Application of AutoML Techniques in Diabetes Diagnosis: Current Approaches, Performance, and Future Directions. Sustainability, 15.","DOI":"10.3390\/su151813484"},{"key":"ref_42","unstructured":"Marie, L. (2024, January 26). NVIDIA Enters Production with DRIVE Orin, Announces BYD and Lucid Group as New EV Customers, Unveils Next-Gen DRIVE Hyperion AV Platform. Published by Nvidia Newsroom, Press Release, 2022. Available online: https:\/\/nvidianews.nvidia.com\/news\/nvidia-enters-production-with-drive-orin-announces-byd-and-lucid-group-as-new-ev-customers-unveils-next-gen-drive-hyperion-av-platform."},{"key":"ref_43","unstructured":"Tharakram, K. (2024, January 26). Snapdragon Ride SDK: A Premium Platform for Developing Customizable ADAS Applications. Published by Qualcomm OnQ Blog. Available online: https:\/\/www.qualcomm.com\/news\/onq\/2022\/01\/snapdragon-ride-sdk-premium-solution-developing-customizable-adas-and-autonomous."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Garikapati, D., and Liu, Y. (2022, January 8\u201312). Dynamic Control Limits Application Strategy For Safety-Critical Autonomy Features. Proceedings of the IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), Macau, China.","DOI":"10.1109\/ITSC55140.2022.9922214"},{"key":"ref_45","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2610","DOI":"10.1016\/j.procs.2020.04.283","article-title":"Object detection system based on convolution neural networks using single shot multi-box detector","volume":"171","author":"Kumar","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"114660","DOI":"10.1016\/j.eswa.2021.114660","article-title":"Trajectory planning for multi-robot systems: Methods and applications","volume":"173","author":"Madridano","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Guo, F., Wang, S., Yue, B., and Wang, J. (2020). A deformable configuration planning framework for a parallel wheel-legged robot equipped with lidar. Sensors, 20.","DOI":"10.3390\/s20195614"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1007\/s40435-020-00665-4","article-title":"A review of PID control, tuning methods and applications","volume":"9","author":"Borase","year":"2021","journal-title":"Int. J. Dyn. Control."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"103648","DOI":"10.1016\/j.jnca.2023.103648","article-title":"AI augmented Edge and Fog computing: Trends and challenges","volume":"216","author":"Tuli","year":"2023","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_51","first-page":"101892","article-title":"Integration of Artificial Intelligence (AI) with sensor networks: Trends and future research opportunities","volume":"36","author":"Elkhediri","year":"2023","journal-title":"J. King Saud-Univ.-Comput. Inf. Sci."},{"key":"ref_52","unstructured":"Institute of Electrical and Electronics Engineers (IEEE) Explore (2024, January 28). AI\/ML Publications (2014\u20132023). Available online: https:\/\/ieeexplore.ieee.org\/Xplore\/home.jsp."},{"key":"ref_53","unstructured":"Multidisciplinary Digital Publishing Institute (MDPI) (2024, January 28). AI\/ML Publications (2014\u20132023). Available online: https:\/\/www.mdpi.com\/."},{"key":"ref_54","unstructured":"Society of Automotive Engineers (SAE) International (2024, January 28). AI\/ML Publications (2014\u20132023). Available online: https:\/\/www.sae.org\/publications."},{"key":"ref_55","unstructured":"Science Direct (2024, January 28). AI\/ML Publications (2014\u20132023). Available online: https:\/\/www.sciencedirect.com\/."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"119736","DOI":"10.1016\/j.techfore.2019.119736","article-title":"The impact of autonomous trucks on business models in the automotive and logistics industry\u2014A Delphi-based scenario study","volume":"148","author":"Fritschy","year":"2019","journal-title":"Technol. Forecast. Soc. Chang."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.trpro.2020.09.013","article-title":"The emerging technological innovation system of driverless trucks","volume":"49","author":"Engholma","year":"2020","journal-title":"Transp. Res. Procedia"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Parekh, D., Poddar, N., Rajpurkar, A., Chahal, M., Kumar, N., Joshi, G.P., and Cho, W. (2022). A Review on Autonomous Vehicles: Progress, Methods and Challenges. Electronics, 11.","DOI":"10.3390\/electronics11142162"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"125329","DOI":"10.1016\/j.physa.2020.125329","article-title":"Modeling cars and trucks in the heterogeneous traffic based on car\u2013truck combination effect using cellular automata","volume":"562","author":"Kong","year":"2021","journal-title":"Phys. Stat. Mech. Appl."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"368","DOI":"10.3390\/futuretransp3010022","article-title":"HetroTraffSim: A macroscopic heterogeneous traffic flow simulator for road bottlenecks","volume":"3","author":"Zeb","year":"2023","journal-title":"Future Transp."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.trb.2017.01.011","article-title":"Modeling heterogeneous traffic flow: A pragmatic approach","volume":"99","author":"Qian","year":"2017","journal-title":"Transp. Res. Part Methodol."},{"key":"ref_62","first-page":"121","article-title":"Benefits and costs of autonomous trucks and cars","volume":"9","author":"Andersson","year":"2019","journal-title":"J. Transp. Technol."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Lee, S., Cho, K., Park, H., and Cho, D. (2023). Cost-Effectiveness of Introducing Autonomous Trucks: From the Perspective of the Total Cost of Operation in Logistics. Appl. Sci., 13.","DOI":"10.3390\/app131810467"},{"key":"ref_64","unstructured":"SAE International (2024, January 28). J3016_202104: Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles. Revised on April 2021. Available online: https:\/\/www.sae.org\/standards\/content\/j3016_202104\/."},{"key":"ref_65","first-page":"3438","article-title":"Measuring and relieving the over-smoothing problem for graph neural networks from the topological view","volume":"34","author":"Chen","year":"2020","journal-title":"Proc. Aaai Conf. Artif. Intell."},{"key":"ref_66","unstructured":"Bojarski, M., Testa, D.D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, D.L., Monfort, M., Muller, U., and Zhang, J. (2016). End-to-End Deep Learning for Self-Driving Cars. arXiv."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1177\/03611981211035764","article-title":"Review of learning-based longitudinal motion planning for autonomous vehicles: Research gaps between self-driving and traffic congestion","volume":"2676","author":"Zhou","year":"2022","journal-title":"Transp. Res. Rec."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"105878","DOI":"10.1016\/j.engappai.2023.105878","article-title":"Integrating machine learning and model predictive control for automotive applications: A review and future directions","volume":"120","author":"Norouzi","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_69","first-page":"587","article-title":"Analysis of the effects of adaptive cruise control on driver behavior and awareness using a driving simulator","volume":"12","author":"Kummetha","year":"2020","journal-title":"J. Transp. Saf. Secur."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"15","DOI":"10.4271\/2016-01-0128","article-title":"Challenges in Autonomous Vehicle Testing and Validation","volume":"4","author":"Koopman","year":"2016","journal-title":"Sae Int. J. Transp. Safety"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"4541","DOI":"10.1109\/TTE.2023.3244360","article-title":"High-Performance Megawatt-Scale MVDC Zonal Electrical Distribution System Based on Power Electronics Open System Interfaces","volume":"9","author":"Bosich","year":"2023","journal-title":"IEEE Trans. Transp. Electrif."},{"key":"ref_72","unstructured":"Neural Information Processing Systems (NeurIPS) (2024, January 28). AI\/ML Publications (2014\u20132023). Available online: https:\/\/nips.cc\/."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/8\/4\/42\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:24:26Z","timestamp":1760106266000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/8\/4\/42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,7]]},"references-count":72,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["bdcc8040042"],"URL":"https:\/\/doi.org\/10.3390\/bdcc8040042","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,7]]}}}