Robust Driving Control for Autonomous Vehicles: An Intelligent General-sum Constrained Adversarial Reinforcement Learning Approach

• URL: http://arxiv.org/abs/2510.09041v2
• Date: Sat, 08 Nov 2025 06:11:19 GMT
• Title: Robust Driving Control for Autonomous Vehicles: An Intelligent General-sum Constrained Adversarial Reinforcement Learning Approach
• Authors: Junchao Fan, Qi Wei, Ruichen Zhang, Dusit Niyato, Yang Lu, Jianhua Wang, Xiaolin Chang, Bo Ai,
• Abstract summary: We propose a novel robust autonomous driving approach that consists of a strategic targeted adversary and a robust driving agent.<n>IGCARL improves the success rate by at least 27.9% over state-of-the-art methods, demonstrating superior robustness to adversarial attacks.
• Score: 56.34189898996741
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep reinforcement learning (DRL) has demonstrated remarkable success in developing autonomous driving policies. However, its vulnerability to adversarial attacks remains a critical barrier to real-world deployment. Although existing robust methods have achieved success, they still suffer from three key issues: (i) these methods are trained against myopic adversarial attacks, limiting their abilities to respond to more strategic threats, (ii) they have trouble causing truly safety-critical events (e.g., collisions), but instead often result in minor consequences, and (iii) these methods can introduce learning instability and policy drift during training due to the lack of robust constraints. To address these issues, we propose Intelligent General-sum Constrained Adversarial Reinforcement Learning (IGCARL), a novel robust autonomous driving approach that consists of a strategic targeted adversary and a robust driving agent. The strategic targeted adversary is designed to leverage the temporal decision-making capabilities of DRL to execute strategically coordinated multi-step attacks. In addition, it explicitly focuses on inducing safety-critical events by adopting a general-sum objective. The robust driving agent learns by interacting with the adversary to develop a robust autonomous driving policy against adversarial attacks. To ensure stable learning in adversarial environments and to mitigate policy drift caused by attacks, the agent is optimized under a constrained formulation. Extensive experiments show that IGCARL improves the success rate by at least 27.9% over state-of-the-art methods, demonstrating superior robustness to adversarial attacks and enhancing the safety and reliability of DRL-based autonomous driving.

Related papers

• Sparse Threats, Focused Defense: Criticality-Aware Robust Reinforcement Learning for Safe Autonomous Driving [11.62520853262219]
  We introduce criticality-aware robust RL (CARRL) for handling sparse, safety-critical risks in autonomous driving.<n>CARRL consists of two interacting components: a risk exposure adversary (REA) and a risk-targeted robust agent (RTRA)<n>We show that our approach reduces the collision rate by at least 22.66% across all cases compared to state-of-the-art baseline methods.
  arXiv  Detail & Related papers  (2026-01-05T05:20:16Z)
• Reinforcement Learning for Decision-Level Interception Prioritization in Drone Swarm Defense [51.736723807086385]
  We present a case study demonstrating the practical advantages of reinforcement learning in addressing this challenge.<n>We introduce a high-fidelity simulation environment that captures realistic operational constraints.<n>Agent learns to coordinate multiple effectors for optimal interception prioritization.<n>We evaluate the learned policy against a handcrafted rule-based baseline across hundreds of simulated attack scenarios.
  arXiv  Detail & Related papers  (2025-08-01T13:55:39Z)
• Sharpening the Spear: Adaptive Expert-Guided Adversarial Attack Against DRL-based Autonomous Driving Policies [3.5120264792560993]
  Deep reinforcement learning (DRL) has emerged as a promising paradigm for autonomous driving.<n>DRL-based policies remain highly vulnerable to adversarial attacks, posing serious safety risks in real-world deployments.<n>We propose an adaptive expert-guided adversarial attack method that enhances both the stability and efficiency of attack policy training.
  arXiv  Detail & Related papers  (2025-06-23T05:42:49Z)
• TeLL-Drive: Enhancing Autonomous Driving with Teacher LLM-Guided Deep Reinforcement Learning [61.33599727106222]
  TeLL-Drive is a hybrid framework that integrates a Teacher LLM to guide an attention-based Student DRL policy.<n>A self-attention mechanism then fuses these strategies with the DRL agent's exploration, accelerating policy convergence and boosting robustness.
  arXiv  Detail & Related papers  (2025-02-03T14:22:03Z)
• Auto-RT: Automatic Jailbreak Strategy Exploration for Red-Teaming Large Language Models [62.12822290276912]
  Auto-RT is a reinforcement learning framework that automatically explores and optimize complex attack strategies.<n>By significantly improving exploration efficiency and automatically optimizing attack strategies, Auto-RT detects a boarder range of vulnerabilities, achieving a faster detection speed and 16.63% higher success rates compared to existing methods.
  arXiv  Detail & Related papers  (2025-01-03T14:30:14Z)
• Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies [2.9965913883475137]
  We present a stealthy and efficient adversarial attack method for DRL-based autonomous driving policies.<n>We train the adversary to learn the optimal policy for attacking at critical moments without domain knowledge.<n>Our method achieves more than 90% collision rate within three attacks in most cases.
  arXiv  Detail & Related papers  (2024-12-04T06:11:09Z)
• RACER: Epistemic Risk-Sensitive RL Enables Fast Driving with Fewer Crashes [57.319845580050924]
  We propose a reinforcement learning framework that combines risk-sensitive control with an adaptive action space curriculum. We show that our algorithm is capable of learning high-speed policies for a real-world off-road driving task.
  arXiv  Detail & Related papers  (2024-05-07T23:32:36Z)
• Robust Driving Policy Learning with Guided Meta Reinforcement Learning [49.860391298275616]
  We introduce an efficient method to train diverse driving policies for social vehicles as a single meta-policy. By randomizing the interaction-based reward functions of social vehicles, we can generate diverse objectives and efficiently train the meta-policy. We propose a training strategy to enhance the robustness of the ego vehicle's driving policy using the environment where social vehicles are controlled by the learned meta-policy.
  arXiv  Detail & Related papers  (2023-07-19T17:42:36Z)
• Security of Deep Reinforcement Learning for Autonomous Driving: A Survey [20.703911484411737]
  Reinforcement learning (RL) enables agents to learn optimal behaviors through interaction with their environment.<n>Despite its promise, RL is susceptible to attacks designed to compromise policy learning or to induce erroneous decisions by trained agents.
  arXiv  Detail & Related papers  (2022-12-12T18:50:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.