mlsanityData to 7 Oct 2026

Exiting the Simulation: The Road to Robust and Resilient Autonomous Vehicles at Scale

arXiv 2210.10876 ↗First posted 19 Oct 2022cs.RO cs.AIcited by 0

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Abstract (arXiv)

In the past two decades, autonomous driving has been catalyzed into reality by the growing capabilities of machine learning. This paradigm shift possesses significant potential to transform the future of mobility and reshape our society as a whole. With the recent advances in perception, planning, and control capabilities, autonomous driving technologies are being rolled out for public trials, yet we remain far from being able to rigorously ensure the resilient operations of these systems across the long-tailed nature of the driving environment. Given the limitations of real-world testing, autonomous vehicle simulation stands as the critical component in exploring the edge of autonomous driving capabilities, developing the robust behaviors required for successful real-world operation, and enabling the extraction of hidden risks from these complex systems prior to deployment. This paper presents the current state-of-the-art simulation frameworks and methodologies used in the development of autonomous driving systems, with a focus on outlining how simulation is used to build the resiliency required for real-world operation and the methods developed to bridge the gap between simulation and reality. A synthesis of the key challenges surrounding autonomous driving simulation is presented, specifically highlighting the opportunities to further advance the ability to continuously learn in simulation and effectively transfer the learning into the real-world - enabling autonomous vehicles to exit the guardrails of simulation and deliver robust and resilient operations at scale.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 33 of its 103 references that are in the map. First posted on arXiv 19 Oct 2022.

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33 of 103 references.

References · 33

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2013Intriguing properties of neural networkscited by 5,114
  3. 2016Overcoming catastrophic forgetting in neural networkscited by 4,718
  4. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,427
  5. 2016Learning without Forgettingcited by 2,465
  6. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  7. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,401
  8. 2017Continual Learning with Deep Generative Replaycited by 1,152
  9. 2016End to End Learning for Self-Driving Carscited by 1,000
  10. 2014Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Imagescited by 774
  11. 2017End-to-end Driving via Conditional Imitation Learningcited by 479
  12. 2019A Survey of Deep Learning Techniques for Autonomous Drivingcited by 401
21 more
  1. 2018ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worstcited by 391
  2. 2019A Survey of Autonomous Driving: Common Practices and Emerging Technologiescited by 373
  3. 2016A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehiclescited by 251
  4. 2016CAD2RL: Real Single-Image Flight without a Single Real Imagecited by 227
  5. 2017On a Formal Model of Safe and Scalable Self-driving Carscited by 129
  6. 2019A Survey of Deep Learning Applications to Autonomous Vehicle Controlcited by 101
  7. 2018Driving Policy Transfer via Modularity and Abstractioncited by 91
  8. 2016Sim-to-Real Robot Learning from Pixels with Progressive Netscited by 89
  9. 2017Virtual to Real Reinforcement Learning for Autonomous Drivingcited by 79
  10. 2018Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulationcited by 76
  11. 2016Accelerated Evaluation of Automated Vehicles Safety in Lane Change Scenarios Based on Importance Sampling Techniquescited by 44
  12. 2017A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detectioncited by 43
  13. 2018Learning to Drive from Simulation without Real World Labelscited by 39
  14. 2016Game-Theoretic Modeling of Driver and Vehicle Interactions for Verification and Validation of Autonomous Vehicle Control Systemscited by 24
  15. 2019Efficient Black-box Assessment of Autonomous Vehicle Safetycited by 23
  16. 2020Waymo's Safety Methodologies and Safety Readiness Determinationscited by 16
  17. 2019Yes, we GAN: Applying Adversarial Techniques for Autonomous Drivingcited by 7
  18. 2019Zero-Shot Autonomous Vehicle Policy Transfer: From Simulation to Real-World via Adversarial Learningcited by 7
  19. 2017From the Lab to the Street: Solving the Challenge of Accelerating Automated Vehicle Testingcited by 3
  20. 2019Demonstration of a Time-Efficient Mobility System Using a Scaled Smart Citycited by 3
  21. 2019Let's Get Dirty: GAN Based Data Augmentation for Camera Lens Soiling Detection in Autonomous Drivingcited by 3

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