Work

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

Year: 2026

Type: article

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.

Source: Sensors

DOI: 10.3390/s26041338

Authors: J. Felipe Arango, Rodrigo Gutiérrez-Moreno, Pedro A. Revenga, Ángel Llamazares, Elena López-Guillén, Luis M. Bergasa

Institution: Universidad de Alcalá

Language: English

Topic: Autonomous Vehicle Technology and Safety

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $3,137