Simulation-to-Real Transfer Learning for End-to-End Autonomous Driving Models
Keywords:
autonomous driving, simulation-to-real transfer, end-to-end learning, domain adaptation, safety assurance, governance, infrastructureAbstract
End-to-end autonomous driving models promise to unify perception, prediction, planning, and control within learned computational pipelines, but their deployment depends on whether capabilities acquired in simulation can be transferred reliably to real roads. This paper examines simulation-to-real transfer learning as a system-level problem rather than a narrow algorithmic challenge. It argues that transferability is shaped by architectural choices, data infrastructures, validation regimes, operational governance, and the socio-technical conditions of deployment. Simulation offers scalable exposure to rare and dangerous scenarios, yet it also introduces visual, dynamic, and semantic discrepancies that can induce brittle behavior when models encounter real-world distribution shifts. The paper analyzes domain randomization, adversarial adaptation, generative translation, and post-training strategies as complementary mechanisms that must be embedded within broader assurance architectures. It further discusses how scenario libraries, digital twins, fleet feedback, and model documentation can support continuous validation and accountability. Because autonomous driving systems operate in public space, questions of fairness, accessibility, liability, and sustainability cannot be treated as post-deployment concerns. The paper concludes that simulation-to-real transfer should be governed as an infrastructural capability, with shared benchmarks, traceable data provenance, safety cases, and policy frameworks that align technical performance with public interest.
References
1. Codevilla, F., Müller, M., López, A., Koltun, V., & Dosovitskiy, A. (2018). End-to-end driving via conditional imitation learning. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 4693–4700). IEEE. https://doi.org/10.1109/ICRA.2018.8460487
2. Dosovitskiy, A., Ros, G., Codevilla, F., López, A., & Koltun, V. (2017). CARLA: An open urban driving simulator. In Proceedings of the 1st Annual Conference on Robot Learning (pp. 1–16). PMLR.
3. Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L. D., Monfort, M., Muller, U., Zhang, J., Zhang, X., Zhao, J., & Zieba, K. (2016). End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316.
4. Kendall, A., Hawke, J., Janz, D., Mazur, P., Reda, D., Allen, J. M., Lam, V. D., Qiao, K., & Shah, A. (2019). Learning to drive in a day. In 2019 International Conference on Robotics and Automation (ICRA) (pp. 8248–8254). IEEE. https://doi.org/10.1109/ICRA.2019.8793742
5. Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., & Abbeel, P. (2017). Domain randomization for transferring deep neural networks from simulation to the real world. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 23–30). IEEE. https://doi.org/10.1109/IROS.2017.8202133
6. Peng, X. B., Andrychowicz, M., Zaremba, W., & Abbeel, P. (2018). Sim-to-real transfer of robotic control with dynamics randomization. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 3803–3810). IEEE. https://doi.org/10.1109/ICRA.2018.8460528
7. Sadeghi, F., & Levine, S. (2017). CAD2RL: Real single-image flight without a single real image. In Robotics: Science and Systems XIII. https://doi.org/10.15607/RSS.2017.XIII.034
8. Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2016). Domain-adversarial training of neural networks. Journal of Machine Learning Research, 17(59), 1–35.
9. Tzeng, E., Hoffman, J., Saenko, K., & Darrell, T. (2017). Adversarial discriminative domain adaptation. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 7167–7176). IEEE. https://doi.org/10.1109/CVPR.2017.316
10. Zhu, J. Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 2223–2232). IEEE. https://doi.org/10.1109/ICCV.2017.244
11. Wang, Z., She, Q., & Ward, T. E. (2021). Generative adversarial networks in computer vision: A survey and taxonomy. ACM Computing Surveys, 54(2), Article 37. https://doi.org/10.1145/3439723
12. Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016). Concrete problems in AI safety. arXiv preprint arXiv:1606.06565.
13. Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., Chaudhary, V., Young, M., Crespo, J. F., & Dennison, D. (2015). Hidden technical debt in machine learning systems. In Advances in Neural Information Processing Systems 28 (pp. 2503–2511). Curran Associates.
14. Shalev-Shwartz, S., Shammah, S., & Shashua, A. (2017). On a formal model of safe and scalable self-driving cars. arXiv preprint arXiv:1708.06374.
15. Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013). Vision meets robotics: The KITTI dataset. The International Journal of Robotics Research, 32(11), 1231–1237. https://doi.org/10.1177/0278364913491297
16. Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., & Beijbom, O. (2020). nuScenes: A multimodal dataset for autonomous driving. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 11621–11631). IEEE. https://doi.org/10.1109/CVPR42600.2020.01164
17. Yang, R., Wang, M., Chen, Y., Guo, T., Xu, Y., Cui, C., ... & Su, L. (2026). Post-Training in End-to-End Autonomous Driving. arXiv preprint arXiv:2607.08072.
18. Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., Caine, B., Vasudevan, V., Han, W., Ngiam, J., Zhao, H., Timofeev, A., Ettinger, S., Krivokon, M., Gao, A., Joshi, A., Zhang, Y., Shlens, J., Chen, Z., & Anguelov, D. (2020). Scalability in perception for autonomous driving: Waymo Open Dataset. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 2446–2454). IEEE. https://doi.org/10.1109/CVPR42600.2020.00252
19. Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019). Model cards for model reporting. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 220–229). ACM. https://doi.org/10.1145/3287560.3287596
20. Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé III, H., & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86–92. https://doi.org/10.1145/3458723
21. Shladover, S. E. (2018). Connected and automated vehicle systems: Introduction and overview. Journal of Intelligent Transportation Systems, 22(3), 190–200. https://doi.org/10.1080/15472450.2017.1421166
22. National Highway Traffic Safety Administration. (2016). Federal automated vehicles policy: Accelerating the next revolution in roadway safety. U.S. Department of Transportation.
23. European Commission. (2021). Proposal for a regulation of the European Parliament and of the Council on laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). COM/2021/206 final.
24. Koopman, P., & Wagner, M. (2017). Autonomous vehicle safety: An interdisciplinary challenge. IEEE Intelligent Transportation Systems Magazine, 9(1), 90–96. https://doi.org/10.1109/MITS.2016.2583491
25. Yurtsever, E., Lambert, J., Carballo, A., & Takeda, K. (2020). A survey of autonomous driving: Common practices and emerging technologies. IEEE Access, 8, 58443–58469. https://doi.org/10.1109/ACCESS.2020.2983149
Downloads
Published
Issue
Section
License
Copyright (c) 2026 International Journal of Artificial Intelligence Engineering and Systems
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
