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

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.