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

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  • 1Electronics Department, University of Alcalá, 28805 Alcalá de Henares, Spain.

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Summary
This summary is machine-generated.

Bridging the simulation-to-real gap in autonomous driving requires addressing reality and performance discrepancies. Our Methodology for Closing Reality and Performance Gaps (MCRPG) and Development and Validation Platform (DVP) offer a structured approach for robust Sim2Real transfer.

Keywords:
CARLA simulatorSim2Real transferautonomous driving stackdevelopment and validation platform (DVP)digital twinparallel executionperformance gapreal-world testingreality gaprobot operating system (ROS)

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Area of Science:

  • Robotics and Autonomous Systems
  • Computer Science
  • Artificial Intelligence

Background:

  • Autonomous Driving Stacks (ADS) face significant challenges transferring from simulation to the real world.
  • The Reality Gap (RG) and Performance Gap (PG) hinder seamless Sim2Real integration.
  • Existing validation methods often fail to address both RG and PG concurrently.

Purpose of the Study:

  • To introduce a novel Methodology for Closing Reality and Performance Gaps (MCRPG) for autonomous driving.
  • To present an open-source Development and Validation Platform (DVP) for Sim2Real research.
  • To establish a structured, iterative framework for aligning simulated and real-world ADS behavior and performance.

Main Methods:

  • MCRPG employs a three-stage approach: Digital Twin, Parallel Execution, and Real-World validation.
  • Joint reduction of RG and PG is achieved through parameter tuning and cross-domain metrics.
  • A two-level metric suite, including Reality Alignment (MNCC) and Ego-Vehicle Performance, is utilized for validation.

Main Results:

  • Experiments demonstrated convergence between simulated and real-world ADS behavior.
  • Consistent performance improvements were observed across the staged validation process.
  • The MCRPG and DVP framework facilitated robust and scalable Sim2Real transfer.

Conclusions:

  • MCRPG provides an effective framework for jointly minimizing Reality and Performance Gaps in autonomous driving.
  • The open-source DVP enables accessible and replicable Sim2Real research.
  • The proposed methodology enhances the reliability and safety of autonomous navigation techniques.