AI-Driven Unified SysML-RoadRunner Integration Approach: An Intelligent Bridge Between MBSE and 3D Simulation for Autonomous Vehicle

Khalil Aloui

doi:10.64808/engineeringperspective.1760896

AI-Driven Unified SysML-RoadRunner Integration Approach: An Intelligent Bridge Between MBSE and 3D Simulation for Autonomous Vehicle

Abstract

The development of autonomous driving systems requires seamless integration between high-level system models and simulation environments to enable early validation and verification. This paper presents a novel methodology that bridges the gap between Model-Based Systems Engineering (MBSE) and 3D simulation for autonomous driving scenarios. We in-troduce RoadRunnerSysMLProfile, a specialized SysML profile that extends standard modeling capabilities with domain-specific constructs for autonomous driving environments and behaviors. This profile customizes SysML diagrams to create RoadRunner Scene Integration Diagrams for static road elements and RoadRunner Scenario Integration Diagrams for dy-namic vehicle behaviors. Additionally, we present AutoSim Transfer, an AI-enhanced transformation tool that leverages machine learning techniques to automatically detect complex junctions, preserve semantic consistency, and optimize the conversion of SysML XMI files into standardized OpenDRIVE and OpenSCENARIO formats compatible with the Road-Runner simulation environment. Our approach addresses key challenges in current MBSE-to-simulation workflows, includ-ing semantic preservation, automated junction detection, and bidirectional traceability. In a case study on complex urban driving scenarios, the proposed methodology demonstrated a 78% reduction in manual modeling effort and achieved 92% accuracy in detecting multi-lane intersections compared to conventional approaches. This methodology enables automotive engineers to maintain consistency between system specifications and simulation environments throughout the development lifecycle, facilitating more comprehensive validation of autonomous driving functions.

Keywords

References

1. Maurer, M., Gerdes, J. C., Lenz, B., & Winner, H. (2016). Autono-mous driving: technical, legal and social aspects. Springer Nature. https://doi.org/10.1007/978-3-662-48847-8
2. Broy, M., Krüger, I. H., Pretschner, A., & Salzmann, C. (2007). Engineering automotive software. Proceedings of the IEEE, 95(2), 356-373. https://doi.org/10.1109/JPROC.2006.888386
3. French, M. O. (2015). Extending model based systems engineering for complex systems. In 53rd AIAA Aerospace Sciences Meeting (p. 1639). https://doi.org/10.2514/6.2015-1639
4. Behere, S., & Törngren, M. (2016). A functional reference architec-ture for autonomous driving. Information and Software Technology, 73, 136-150. https://doi.org/10.1016/j.infsof.2015.12.008
5. Friedenthal, S., Moore, A., & Steiner, R. (2014). A practical guide to SysML: the systems modeling language. Morgan Kaufmann.
6. Weilkiens, T. (2016). SYSMOD - The Systems Modeling Toolbox - Pragmatic MBSE with SysML. MBSE4U.
7. Aloui, K., Guizani, A., Hammadi, M., Soriano, T., & Haddar, M. (2021). Integrated design methodology of automated guided vehicles based on swarm robotics. Applied Sciences, 11(13), 6187. https://doi.org/10.3390/app11136187
8. Qamar, A., During, C., & Wikander, J. (2009). Designing mecha-tronic systems, a model-based perspective, an attempt to achieve SysML-Matlab/Simulink model integration. In 2009 IEEE/ASME In-ternational Conference on Advanced Intelligent Mechatronics (pp. 1306-1311). IEEE. https://doi.org/10.1109/AIM.2009.5229869

9. Bock, C., Barbau, R., Matei, I., & Dadfarnia, M. (2017). An exten-sion of the systems modeling language for physical interaction and signal flow simulation. Systems Engineering, 20(5), 395-431. https://doi.org/10.1002/sys.21380
10. Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., & Koltun, V. (2017). CARLA: An open urban driving simulator. In Conference on robot learning (pp. 1-16). PMLR.
11. Shah, S., Dey, D., Lovett, C., & Kapoor, A. (2017, November). Air-sim: High-fidelity visual and physical simulation for autonomous ve-hicles. In Field and service robotics: Results of the 11th international conference (pp. 621-635). Cham: Springer International Publishing.
12. MathWorks. (2021). RoadRunner: Design 3D scenes for automated driving simulation.
13. Hallerbach, S., Xia, Y., Eberle, U., & Koester, F. (2018). Simulation-based identification of critical scenarios for cooperative and automated vehicles. SAE International Journal of Connected and Automated Vehicles, 1(2), 93-106. https://doi.org/10.4271/2018-01-1066
14. Dupuis, M., Strobl, M., & Grezlikowski, H. (2010). OpenDRIVE 2010 and beyond–status and future of the de facto standard for the description of road networks. In Proc. of the Driving Simulation Conference Europe (pp. 231-242).
15. Riedmaier, S., Ponn, T., Ludwig, D., Schick, B., & Diermeyer, F. (2020). Survey on scenario-based safety assessment of automated vehicles. IEEE Access, 8, 87456-87477. https://doi.org/10.1109/ACCESS.2020.2993730
16. Rosique, F., Navarro, P. J., Fernández, C., & Padilla, A. (2019). A systematic review of perception system and simulators for autono-mous vehicles research. Sensors, 19(3), 648. https://doi.org/10.3390/s19030648
17. Biggs, G., Juknevicius, T., Armonas, A., & Post, K. (2018). Integrat-ing Safety and Reliability Analysis into MBSE: Overview of the New Feature in SysML 1.5. INCOSE International Symposium, 28(1), 1322-1336. https://doi.org/10.1002/j.2334-5837.2018.00551.x
18. Mütsch, F., Gremmelmaier, H., Becker, N., Bogdoll, D., Zofka, M. R., & Zöllner, J. M. (2023). From model-based to data-driven simula-tion: Challenges and trends in autonomous driving. arXiv preprint arXiv:2305.13960. https://doi.org/10.48550/arXiv.2305.13960
19. Zander-Nowicka, J. (2009). Model-based testing of real-time embed-ded systems in the automotive domain.
20. Giese, H., Hildebrandt, S., & Lambers, L. (2014). Bridging the gap between formal semantics and implementation of triple graph gram-mars. Software & Systems Modeling, 13(1), 273-299. https://doi.org/10.1007/s10270-012-0247-y
21. Aloui, K., Guizani, A., Hammadi, M., Soriano, T., & Haddar, M. (2024). An integrated design methodology for swarm robotics using model-based systems engineering and robot operating systems. Pro-ceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 238(11), 5159-5185. https://doi.org/10.1177/09544062231216853
22. Qamar, A., Wikander, J., & During, C. (2011). Designing mecha-tronic systems: A model-integration approach. In Proceedings of the 18th International Conference on Engineering Design (ICED 11) (pp. 145-156).
23. Grönniger, H., Krahn, H., Pinkernell, C., & Rumpe, B. (2008). Mod-eling variants of automotive systems using views. Modellbasierte Entwicklung von eingebetteten Fahrzeugfunktionen (MBEFF), In-formatik Bericht, 1, 76-89. https://doi.org/10.48550/arXiv.1409.6629
24. Bombieri, N., Di Guglielmo, G., Ferrari, M., Fummi, F., Pravadelli, G., Stefanni, F., & Venturelli, A. (2010). HIFSuite: Tools for HDL code conversion and manipulation. EURASIP Journal on Embedded Systems, 2010(1), 436328. https://doi.org/10.1155/2010/436328
25. Weissnegger, R., Schuss, M., Kreiner, C., Pistauer, M., Römer, K., & Steger, C. (2016). Simulation-based verification of automotive safety-critical systems based on EAST-ADL. Procedia Computer Science, 83, 245-252. https://doi.org/10.1016/j.procs.2016.04.122
26. Jordan, E., Berlec, T., Rihar, L., & Kusar, J. (2020). Simulation of cost driven value stream mapping. International journal of simulation modelling, 19(3), 458-469.
27. Amelunxen, C., Königs, A., Rötschke, T., & Schürr, A. (2006, July). MOFLON: A standard-compliant metamodeling framework with graph transformations. In European Conference on Model Driven Architecture-Foundations and Applications (pp. 361-375). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/11787044_27
28. Zolotas, A., Hoyos Rodriguez, H., Hutchesson, S., Sanchez Pina, B., Grigg, A., Li, M., ... & Paige, R. F. (2020). Bridging proprietary modelling and open-source model management tools: the case of PTC Integrity Modeller and Epsilon. Software and Systems Modeling, 19(1), 17-38. https://doi.org/10.1007/s10270-019-00732-1
29. Nguyen, P. T., Di Rocco, J., Di Ruscio, D., Ochoa, L., Degueule, T., & Di Penta, M. (2019). FOCUS: A recommender system for mining API function calls and usage patterns. In 2019 IEEE/ACM 41st In-ternational Conference on Software Engineering (ICSE) (pp. 1050-1060). IEEE. https://doi.org/10.1109/ICSE.2019.00109
30. Raedler, S., Mangler, J., & Rinderle-Ma, S. (2023). Model-driven engineering method to support the formalization of machine learning using SysML. arXiv preprint arXiv:2307.04495. https://doi.org/10.48550/arXiv.2307.04495
31. Kovalenko, O., Serral, E., Sabou, M., Ekaputra, F. J., Winkler, D., & Biffl, S. (2014, November). Automating cross-disciplinary defect de-tection in multi-disciplinary engineering environments. In Internation-al Conference on Knowledge Engineering and Knowledge Manage-ment (pp. 238-249). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-13704-9_19
32. Bagschik, G., Menzel, T., & Maurer, M. (2018, June). Ontology based scene creation for the development of automated vehicles. In 2018 IEEE intelligent vehicles symposium (IV) (pp. 1813-1820). IEEE. https://doi.org/10.1109/IVS.2018.8500632
33. Bastani, F., He, S., Abbar, S., Alizadeh, M., Balakrishnan, H., Chawla, S., & Madden, S. (2018). Machine-assisted map editing. In Proceedings of the 26th ACM SIGSPATIAL International Confer-ence on Advances in Geographic Information Systems (pp. 23-32). https://doi.org/10.1145/3274895.3274927
34. Mattyus, G., Wang, S., Fidler, S., & Urtasun, R. (2017). HD maps: Fine-grained road segmentation by parsing ground and aerial images. In Proceedings of the IEEE Conference on Computer Vision and Pat-tern Recognition (pp. 3611-3619).
35. Wang, C., Guo, F., Yu, R., Wang, L., & Zhang, Y. (2023). The ap-plication of driver models in the safety assessment of autonomous vehicles: Perspectives, insights, prospects. IEEE Transactions on In-telligent Vehicles, 9(1), 2364-2381. https://doi.org/10.1109/TIV.2023.3333796
36. Neurohr, C., Westhofen, L., Butz, M., Bollmann, M. H., Eberle, U., & Galbas, R. (2021). Criticality analysis for the verification and vali-dation of automated vehicles. IEEE Access, 9, 18016-18041. https://doi.org/10.1109/ACCESS.2021.3053159
37. Ågren, S. M., Knauss, E., Heldal, R., Pelliccione, P., Malmqvist, G., & Bodén, J. (2018, August). The manager perspective on require-ments impact on automotive systems development speed. In 2018 IEEE 26th international requirements engineering conference (RE) (pp. 17-28). IEEE. https://doi.org/10.1109/RE.2018.00-55
38. Elgharbawy, M., Schwarzhaupt, A., Frey, M., & Gauterin, F. (2018). A real-time multisensor fusion verification framework for advanced driver assistance systems. Transportation Research Part F: Traffic Psychology and Behaviour, 61, 259-267. https://doi.org/10.1016/j.trf.2016.12.002
39. ASAM. (2021). OpenDRIVE: Format Specification, Version 1.7.0.
40. ASAM. (2021). OpenSCENARIO: Format Specification, Version 1.1.0.
41. Scholtes, M., Westhofen, L., Turner, L. R., Lotto, K., Schuldes, M., Weber, H., ... & Eckstein, L. (2021). 6-layer model for a structured description and categorization of urban traffic and environment. iEEE Access, 9, 59131-59147. https://doi.org/10.1109/ACCESS.2021.3072739
42. Han, Y., Zhang, H., Li, H., Jin, Y., Lang, C., & Li, Y. (2023). Col-laborative perception in autonomous driving: Methods, datasets, and challenges. IEEE Intelligent Transportation Systems Magazine, 15(6), 131-151. https://doi.org/10.1109/MITS.2023.3298534
43. Schätz, B., Voss, S., & Zverlov, S. (2015, June). Automating design-space exploration: optimal deployment of automotive SW-components in an ISO26262 context. In Proceedings of the 52nd Annual Design Automation Conference (pp. 1-6). https://doi.org/10.1145/2744769.2747912
44. Broy, M., Feilkas, M., Herrmannsdoerfer, M., Merenda, S., & Ratiu, D. (2010). Seamless model-based development: From isolated tools to integrated model engineering environments. Proceedings of the IEEE, 98(4), 526-545. https://doi.org/10.1109/JPROC.2009.2037771
45. Furst, S. (2009). Autosar-a worldwide standard is on the road. In 14th International VDI Congress Electronic Systems for Vehicles, 2009.
46. Haidar, F., Laıb, A., Ajwad, S. A., & Guılbert, G. (2024). Integrated vehicle dynamics modeling, path tracking, and simulation: a MATLAB implementation approach. Engineering Perspective, 3(1), 7-16. https://doi.org/10.29228/eng.pers.75106
47. Karaman, M., & Korucu, S. (2023). Modeling the vehicle movement and braking effect of the hydrostatic regenerative braking system. En-gineering Perspective, 3(2), 18-26. https://doi.org/10.29228/eng.pers.69826
48. Kıyaklı, A. O., & Solmaz, H. (2019). Modeling of an electric vehicle with MATLAB/Simulink. International journal of automotive science and technology, 2(4), 9-15. https://doi.org/10.30939/ijastech..475477
49. Albdairi, M., & Almusawi, A. (2024). Examining the Influence of Autonomous Vehicle Behaviors on Travel Times and Vehicle Arri-vals: A Comparative Study Across Different Simulation Durations on the Kirkuk-Sulaymaniyah Highway. International Journal of Automo-tive Science and Technology, 8(3), 341-353. https://doi.org/10.30939/ijastech..1480916
50. Kazár1a, T. M., Pethő, Z., Vida, G., & Török, Á. (2022). Simulation of Road Traffic Accidents Related to ADAS Systems in PreScan. https://doi.org/10.3311/BMEZalaZONE2022-008
51. Aloui, K., Guizani, A., Hammadi, M., Soriano, T., & Haddar, M. (2021). Integrated design methodology of automated guided vehicles based on swarm robotics. Applied Sciences, 11(13), 6187. https://doi.org/10.3390/app11136187
52. Allagui, A., Belhadj, I., Plateaux, R., Hammadi, M., Penas, O., & Aifaoui, N. (2023). Reinforcement learning for disassembly sequence planning optimization. Computers in Industry, 151, 103992. https://doi.org/10.1016/j.compind.2023.103992

Details

Primary Language

English

Subjects

Automotive Mechatronics and Autonomous Systems

Journal Section

Research Article

Authors

Khalil Aloui ^*
France

Early Pub Date

December 16, 2025

Publication Date

December 30, 2025

Submission Date

August 8, 2025

Acceptance Date

October 10, 2025

Published in Issue

Year 2025 Volume: 5 Number: 4

DOI

https://doi.org/10.64808/engineeringperspective.1760896

IZ

https://izlik.org/JA29JE52GW

Cite

RIS / Bibtex

APA

Aloui, K. (2025). AI-Driven Unified SysML-RoadRunner Integration Approach: An Intelligent Bridge Between MBSE and 3D Simulation for Autonomous Vehicle. Engineering Perspective, 5(4), 129-148. https://doi.org/10.64808/engineeringperspective.1760896

AMA

1.Aloui K. AI-Driven Unified SysML-RoadRunner Integration Approach: An Intelligent Bridge Between MBSE and 3D Simulation for Autonomous Vehicle. engineeringperspective. 2025;5(4):129-148. doi:10.64808/engineeringperspective.1760896

Chicago

Aloui, Khalil. 2025. “AI-Driven Unified SysML-RoadRunner Integration Approach: An Intelligent Bridge Between MBSE and 3D Simulation for Autonomous Vehicle”. Engineering Perspective 5 (4): 129-48. https://doi.org/10.64808/engineeringperspective.1760896.

EndNote

Aloui K (December 1, 2025) AI-Driven Unified SysML-RoadRunner Integration Approach: An Intelligent Bridge Between MBSE and 3D Simulation for Autonomous Vehicle. Engineering Perspective 5 4 129–148.

IEEE

[1]K. Aloui, “AI-Driven Unified SysML-RoadRunner Integration Approach: An Intelligent Bridge Between MBSE and 3D Simulation for Autonomous Vehicle”, engineeringperspective, vol. 5, no. 4, pp. 129–148, Dec. 2025, doi: 10.64808/engineeringperspective.1760896.

ISNAD

Aloui, Khalil. “AI-Driven Unified SysML-RoadRunner Integration Approach: An Intelligent Bridge Between MBSE and 3D Simulation for Autonomous Vehicle”. Engineering Perspective 5/4 (December 1, 2025): 129-148. https://doi.org/10.64808/engineeringperspective.1760896.

JAMA

1.Aloui K. AI-Driven Unified SysML-RoadRunner Integration Approach: An Intelligent Bridge Between MBSE and 3D Simulation for Autonomous Vehicle. engineeringperspective. 2025;5:129–148.

MLA

Aloui, Khalil. “AI-Driven Unified SysML-RoadRunner Integration Approach: An Intelligent Bridge Between MBSE and 3D Simulation for Autonomous Vehicle”. Engineering Perspective, vol. 5, no. 4, Dec. 2025, pp. 129-48, doi:10.64808/engineeringperspective.1760896.

Vancouver

1.Khalil Aloui. AI-Driven Unified SysML-RoadRunner Integration Approach: An Intelligent Bridge Between MBSE and 3D Simulation for Autonomous Vehicle. engineeringperspective. 2025 Dec. 1;5(4):129-48. doi:10.64808/engineeringperspective.1760896

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