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

Year 2025, Volume: 5 Issue: 4, 129 - 148

Khalil Aloui

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

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

Autonomous Driving , Artificial Intelligence Model-Based Systems Engineering (MBSE) , Model Transformation , RoadRun-ner , Systems Modeling Language (SysML) , XMI Processing

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