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

## TL;DR

This paper introduces a methodology and platform to bridge the gaps between simulated and real-world performance in autonomous driving systems.

## Contribution

A structured methodology and open-source platform for closing Sim2Real gaps in autonomous driving stacks.

## Key findings

- Experiments showed convergence between simulated and real behavior in an urban scenario.
- The proposed metric suite effectively aligns reality and evaluates ego-vehicle performance.
- The platform enables scalable and accessible Sim2Real research for autonomous navigation.

## Abstract

The successful transfer of autonomous driving stacks (ADS) from simulation to the real world faces two main challenges: the Reality Gap (RG)—mismatches between simulated and real behaviors—and the Performance Gap (PG)—differences between expected and achieved performance across domains. We propose a Methodology for Closing Reality and Performance Gaps (MCRPG), a structured and iterative approach that jointly reduces RG and PG through parameter tuning, cross-domain metrics, and staged validation. MCRPG comprises three stages—Digital Twin, Parallel Execution, and Real-World—to progressively align ADS behavior and performance. To ground and validate the method, we present an open-source, cost-effective Development and Validation Platform (DVP) 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) Reality Alignment via Maximum Normalized Cross-Correlation (MNCC) over multi-modal signals (e.g., ego kinematics, detections), and (ii) Ego-Vehicle Performance 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 Sim2Real research in autonomous navigation techniques.