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Related Experiment Video

Updated: Jun 27, 2026

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

A Curriculum Approach to Reduce the Dynamics-Related Reality Gap in Autonomous Driving Decision-Making.

Rodrigo Gutiérrez-Moreno1, Rafael Barea1, Elena López-Guillén1

  • 1Electronics Department, University of Alcalá (UAH), 28801 Alcalá de Henares, Spain.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a curriculum learning method for autonomous driving decision-making. It effectively bridges the sim-to-real gap by progressively refining policies, enhancing safety and efficiency in complex urban scenarios.

Keywords:
autonomous drivingcurriculum learningdigital twinshybrid decision-makingparallel executionreality gap

Related Experiment Videos

Last Updated: Jun 27, 2026

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

Area of Science:

  • Robotics and Control Systems
  • Artificial Intelligence and Machine Learning
  • Autonomous Vehicle Navigation

Background:

  • Autonomous driving decision-making in complex urban environments demands safe, robust, and adaptable behaviors.
  • A significant challenge is the reality gap between simulation and real-world vehicle dynamics and actuation.
  • Existing methods often struggle to bridge this sim-to-real gap effectively for tactical decision-making.

Purpose of the Study:

  • To present a curriculum learning approach for autonomous driving decision-making to reduce the dynamics-related reality gap.
  • To develop a hybrid architecture combining learning-based tactical decisions with classical planning and control.
  • To validate the proposed approach through a staged sim-to-real process, culminating in real-world testing.

Main Methods:

  • A staged sim-to-real process involving training in a lightweight simulator, refinement in CARLA, fine-tuning with a digital twin, and real-world validation.
  • Focus on vehicle dynamics, actuation response, and scenario geometry rather than the complete sim-to-real problem.
  • Evaluation across diverse urban driving scenarios (lane changing, roundabouts, merging, crossroads) in simulation and a controlled merge scenario in reality.

Main Results:

  • The curriculum learning approach improved training efficiency and final performance, achieving over 91% success rates in SMARTS.
  • In CARLA, the proposed architecture was up to 50% faster than the Autopilot baseline, with enhanced comfort and safety metrics (acceleration, jerk).
  • Real-world parallel execution demonstrated the feasibility of transferring the decision-making architecture to a physical vehicle.

Conclusions:

  • The proposed curriculum learning strategy effectively reduces the dynamics-related reality gap in autonomous driving decision-making.
  • The hybrid architecture demonstrates superior performance, efficiency, and safety compared to baselines in simulated and real-world conditions.
  • The staged sim-to-real process is a viable method for developing and deploying robust autonomous driving systems.