mlsanityData to 7 Oct 2026

Optimal Actuator Attacks on Autonomous Vehicles Using Reinforcement Learning

arXiv 2502.07839 ↗First posted 11 Feb 2025cs.RO cs.LGcited by 0

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

Abstract (arXiv)

With the increasing prevalence of autonomous vehicles (AVs), their vulnerability to various types of attacks has grown, presenting significant security challenges. In this paper, we propose a reinforcement learning (RL)-based approach for designing optimal stealthy integrity attacks on AV actuators. We also analyze the limitations of state-of-the-art RL-based secure controllers developed to counter such attacks. Through extensive simulation experiments, we demonstrate the effectiveness and efficiency of our proposed method.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Adversarial scenarios and safety testing, one of 9 directions in End-to-end autonomous driving. Builds on 3 of its 13 references that are in the map. First posted on arXiv 11 Feb 2025.

Builds on

3 of 13 references.

References · 3

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,179
  3. 2024QUADFormer: Learning-based Detection of Cyber Attacks in Quadrotor UAVscited by 1

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Not cited yet.

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  1. 2022Security of Deep Reinforcement Learning for Autonomous Driving: A Survey
  2. 2025AED: Automatic Discovery of Effective and Diverse Vulnerabilities for Autonomous Driving Policy with Large Language Models
  3. 2021Minimizing Safety Interference for Safe and Comfortable Automated Driving with Distributional Reinforcement Learning
  4. 2024Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies
  5. 2026Securing Autonomous Vehicle Systems via Twin-Aware Federated Reinforcement Learning
  6. 2024SECRM-2D: RL-Based Efficient and Comfortable Route-Following Autonomous Driving with Analytic Safety Guarantees
  7. 2021A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles
  8. 2025Robust Driving Control for Autonomous Vehicles: An Intelligent General-sum Constrained Adversarial Reinforcement Learning Approach