mlsanityData to 14 Oct 2026

DR2L: Surfacing Corner Cases to Robustify Autonomous Driving via Domain Randomization Reinforcement Learning

arXiv 2107.11762 ↗First posted 25 Jul 2021cs.RO cs.AI cs.LGcited by 11

On the map: End-to-end autonomous driving → Adversarial scenarios and safety testing

Abstract (arXiv)

How to explore corner cases as efficiently and thoroughly as possible has long been one of the top concerns in the context of deep reinforcement learning (DeepRL) autonomous driving. Training with simulated data is less costly and dangerous than utilizing real-world data, but the inconsistency of parameter distribution and the incorrect system modeling in simulators always lead to an inevitable Sim2real gap, which probably accounts for the underperformance in novel, anomalous and risky cases that simulators can hardly generate. Domain Randomization(DR) is a methodology that can bridge this gap with little or no real-world data. Consequently, in this research, an adversarial model is put forward to robustify DeepRL-based autonomous vehicles trained in simulation to gradually surfacing harder events, so that the models could readily transfer to the real world.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Placed in Adversarial scenarios and safety testing, one of 9 directions in End-to-end autonomous driving. Builds on 16 of its 29 references that are in the map. First posted on arXiv 25 Jul 2021.

Builds on

16 of 29 references.

References · 16

  1. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,498
  2. 2015Prioritized Experience Replaycited by 1,099
  3. 2019Solving Rubik's Cube with a Robot Handcited by 734
  4. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 721
  5. 2018Learning Dexterous In-Hand Manipulationcited by 687
  6. 2017Sim-to-Real Transfer of Robotic Control with Dynamics Randomizationcited by 672
  7. 2018Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experiencecited by 217
  8. 2018Sim-to-Real via Sim-to-Sim: Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networkscited by 174
  9. 2019Active Domain Randomizationcited by 170
  10. 2017Preparing for the Unknown: Learning a Universal Policy with Online System Identificationcited by 141
  11. 2019Meta-Sim: Learning to Generate Synthetic Datasetscited by 128
  12. 2017Stein Variational Policy Gradientcited by 44
4 more
  1. 2018Learning To Simulatecited by 44
  2. 2019From GAN to WGANcited by 34
  3. 2020Tactical Decision Making for Emergency Vehicles Based on A Combinational Learning Methodcited by 1
  4. 2021Learning to falsify automated driving vehicles with prior knowledgecited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 0; 2023: 3; 2024: 2; 2025: 4; 2026: 1.

11 papers.

  1. 2021Understanding Domain Randomization for Sim-to-real Transfercited by 80
  2. 2023(Re)2H2O: Autonomous Driving Scenario Generation via Reversely Regularized Hybrid Offline-and-Online Reinforcement Learningcited by 6
  3. 2023Continual Driving Policy Optimization with Closed-Loop Individualized Curriculacited by 4
  4. 2023Stackelberg Driver Model for Continual Policy Improvement in Scenario-Based Closed-Loop Autonomous Drivingcited by 3
  5. 2024GENESIS-RL: GEnerating Natural Edge-cases with Systematic Integration of Safety considerations and Reinforcement Learningcited by 3
  6. 2025Pessimism Principle Can Be Effective: Towards a Framework for Zero-Shot Transfer Reinforcement Learningcited by 2
  7. 2025Navigating the Edge with the State-of-the-Art Insights into Corner Case Identification and Generation for Enhanced Autonomous Vehicle Safetycited by 1
  8. 2025Statistical Guarantees for Offline Domain Randomizationcited by 1
  9. 2024Dynamically Expanding Capacity of Autonomous Driving with Near-Miss Focused Training Frameworkcited by 0
  10. 2025CORTEX-AVD: A Framework for CORner Case Testing and EXploration in Autonomous Vehicle Developmentcited by 0
  11. 2026Grounding Sim-to-Real Generalization in Robotic Manipulation: An Empirical Study with Vision-Language-Action Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Review of Deep Reinforcement Learning for Autonomous Driving
  2. 2024Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies
  3. 2025Robust Driving Control for Autonomous Vehicles: An Intelligent General-sum Constrained Adversarial Reinforcement Learning Approach
  4. 2025Sharpening the Spear: Adaptive Expert-Guided Adversarial Attack Against DRL-based Autonomous Driving Policies
  5. 2025Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach
  6. 2023A Platform-Agnostic Deep Reinforcement Learning Framework for Effective Sim2Real Transfer towards Autonomous Driving
  7. 2021Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi-agent Autonomous Driving Policies
  8. 2024Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey