mlsanityData to 7 Oct 2026

Improved Robustness and Safety for Autonomous Vehicle Control with Adversarial Reinforcement Learning

arXiv 1903.03642 ↗First posted 8 Mar 2019cs.LG cs.RO stat.MLcited by 23

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Abstract (arXiv)

To improve efficiency and reduce failures in autonomous vehicles, research has focused on developing robust and safe learning methods that take into account disturbances in the environment. Existing literature in robust reinforcement learning poses the learning problem as a two player game between the autonomous system and disturbances. This paper examines two different algorithms to solve the game, Robust Adversarial Reinforcement Learning and Neural Fictitious Self Play, and compares performance on an autonomous driving scenario. We extend the game formulation to a semi-competitive setting and demonstrate that the resulting adversary better captures meaningful disturbances that lead to better overall performance. The resulting robust policy exhibits improved driving efficiency while effectively reducing collision rates compared to baseline control policies produced by traditional reinforcement learning methods.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Mar 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 11; 2023: 3; 2024: 0; 2025: 2; 2026: 2.

23 papers.

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  1. 2023CAT: Closed-loop Adversarial Training for Safe End-to-End Drivingcited by 52
  2. 2022Large-scale Robustness Analysis of Video Action Recognition Modelscited by 29
  3. 2022Robustness Analysis of Video-Language Models Against Visual and Language Perturbationscited by 23
  4. 2025Reevaluating Policy Gradient Methods for Imperfect-Information Gamescited by 13
  5. 2021A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehiclescited by 10
  6. 2022Robust Imitation Learning from Corrupted Demonstrationscited by 10
  7. 2022Performance Analysis of Out-of-Distribution Detection on Trained Neural Networkscited by 7
  8. 2022Feasible Adversarial Robust Reinforcement Learning for Underspecified Environmentscited by 4
  9. 2022Traffic-Aware Autonomous Driving with Differentiable Traffic Simulationcited by 4
  10. 2021Improving Robustness of Deep Reinforcement Learning Agents: Environment Attack based on the Critic Networkcited by 3
  11. 2021ARC: Adversarially Robust Control Policies for Autonomous Vehiclescited by 3
  12. 2026ADV-0: Closed-Loop Min-Max Adversarial Training for Long-Tail Robustness in Autonomous Drivingcited by 3
11 more
  1. 2021Off Environment Evaluation Using Convex Risk Minimizationcited by 2
  2. 2021Evaluating Robustness over High Level Driving Instruction for Autonomous Drivingcited by 1
  3. 2022Understanding the Impact of Edge Cases from Occluded Pedestrians for ML Systemscited by 1
  4. 2022Robust AI Driving Strategy for Autonomous Vehiclescited by 1
  5. 2022Training Robots to Evaluate Robots: Example-Based Interactive Reward Functions for Policy Learningcited by 1
  6. 2023Deep incremental learning models for financial temporal tabular datasets with distribution shiftscited by 1
  7. 2025Robust Driving Control for Autonomous Vehicles: An Intelligent General-sum Constrained Adversarial Reinforcement Learning Approachcited by 1
  8. 2022Measuring Interventional Robustness in Reinforcement Learningcited by 0
  9. 2022Smoothing Policy Iteration for Zero-sum Markov Gamescited by 0
  10. 2023Where2Start: Leveraging initial States for Robust and Sample-Efficient Reinforcement Learningcited by 0
  11. 2026Reward-Preserving Attacks For Robust Reinforcement Learningcited by 0