From Virtual Environments to Real-World Trials: Emerging Trends in Autonomous Driving

Computer Science > Artificial Intelligence [Submitted on 18 Mar 2026] From Virtual Environments to Real-World Trials: Emerging Trends in Autonomous Driving A. Humnabadkar, A. Sikdar, B. Cave, H. Zhang, N. Bessis, A. Behera Autonomous driving technologies have achieved significant advances in recent years, yet their real-world deployment remains constrained by data scarcity, safety requirements, and the need for generalization across diverse environments. In response, synthetic data and virtual environments have emerged as powerful enablers, offering scalable, controllable, and richly annotated scenarios for training and evaluation. This survey presents a comprehensive review of recent developments at the intersection of autonomous driving, simulation technologies, and synthetic datasets. We organize the landscape across three core dimensions: (i) the use of synthetic data for perception and planning, (ii) digital twin-based simulation for system validation, and (iii) domain adaptation strategies bridging synthetic and real-world data. We also highlight the role of vision-language models and simulation realism in enhancing scene understanding and generalization. A detailed taxonomy of datasets, tools, and simulation platforms is provided, alongside an analysis of trends in benchmark design. Finally, we discuss critical challenges and open research directions, including Sim2Real transfer, scalable safety validation, cooperative autonomy, and simulation-driven policy learning, that must be addressed to accelerate the path toward safe, generalizable, and globally deployable autonomous driving systems. Comments: Accepted manuscript - Transactions on Intelligent Transportation Systems Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2603.17714 [cs.AI]   (or arXiv:2603.17714v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2603.17714 Focus to learn more Submission history From: Aditya Humnabadkar Mr. [view email] [v1] Wed, 18 Mar 2026 13:32:26 UTC (10,371 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)