Closing Sim2Real Gaps: A Versatile Development and Validation Platform for Autonomous Driving Stacks

The successful transfer of autonomous driving stacks (ADS) from simulation to the real world faces two main challenges: the <b>Reality Gap (RG)</b>-mismatches between simulated and real behaviors-and the <b>Performance Gap (PG)</b>-differences between expected and achieved performance across domains. We propose a <b>Methodology for Closing Reality and Performance Gaps (MCRPG)</b>, a structured and iterative approach that jointly reduces RG and PG through parameter tuning, cross-domain metrics, and staged validation. MCRPG comprises three stages-<b>Digital Twin</b>, <b>Parallel Execution</b>, and <b>Real-World</b>-to progressively align ADS behavior and performance. To ground and validate the method, we present an open-source, cost-effective <b>Development and Validation Platform (DVP)</b> that integrates an ROS-based modular ADS with the CARLA simulator and a custom autonomous electric vehicle. We also introduce a two-level metric suite: (i) <b>Reality Alignment</b> via Maximum Normalized Cross-Correlation (MNCC) over multi-modal signals (e.g., ego kinematics, detections), and (ii) <b>Ego-Vehicle Performance</b> covering safety, comfort, and driving efficiency. Experiments in an urban scenario show convergence between simulated and real behavior and increasingly consistent performance across stages. Overall, MCRPG and DVP provide a replicable framework for robust, scalable, and accessible <b>Sim2Real</b> research in autonomous navigation techniques.