MARL-OT: Multi-Agent Reinforcement Learning Guided Online Fuzzing to Detect Safety Violation in Autonomous Driving Systems

• URL: http://arxiv.org/abs/2501.14451v1
• Date: Fri, 24 Jan 2025 12:34:04 GMT
• Title: MARL-OT: Multi-Agent Reinforcement Learning Guided Online Fuzzing to Detect Safety Violation in Autonomous Driving Systems
• Authors: Linfeng Liang, Xi Zheng,
• Abstract summary: This paper introduces MARL-OT, a scalable framework that leverages MARL to detect safety violations of Autonomous Driving Systems (ADSs) MARL-OT employs MARL for high-level guidance, triggering various dangerous scenarios for the rule-based online fuzzer to explore potential safety violations of ADSs. Our approach improves the detected safety violation rate by up to 136.2% compared to the state-of-the-art (SOTA) testing technique.
• Score: 1.1677228160050082
• License:
• Abstract: Autonomous Driving Systems (ADSs) are safety-critical, as real-world safety violations can result in significant losses. Rigorous testing is essential before deployment, with simulation testing playing a key role. However, ADSs are typically complex, consisting of multiple modules such as perception and planning, or well-trained end-to-end autonomous driving systems. Offline methods, such as the Genetic Algorithm (GA), can only generate predefined trajectories for dynamics, which struggle to cause safety violations for ADSs rapidly and efficiently in different scenarios due to their evolutionary nature. Online methods, such as single-agent reinforcement learning (RL), can quickly adjust the dynamics' trajectory online to adapt to different scenarios, but they struggle to capture complex corner cases of ADS arising from the intricate interplay among multiple vehicles. Multi-agent reinforcement learning (MARL) has a strong ability in cooperative tasks. On the other hand, it faces its own challenges, particularly with convergence. This paper introduces MARL-OT, a scalable framework that leverages MARL to detect safety violations of ADS resulting from surrounding vehicles' cooperation. MARL-OT employs MARL for high-level guidance, triggering various dangerous scenarios for the rule-based online fuzzer to explore potential safety violations of ADS, thereby generating dynamic, realistic safety violation scenarios. Our approach improves the detected safety violation rate by up to 136.2% compared to the state-of-the-art (SOTA) testing technique.

Related papers

• 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. 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)
• LLM-attacker: Enhancing Closed-loop Adversarial Scenario Generation for Autonomous Driving with Large Language Models [39.139025989575686]
  AClosed-loop adversarial scenario generation framework leveraging large language models (LLMs) adversarial scenario generation methods are developed, in which behaviors of traffic participants are manipulated to induce safety-critical events. LLMs-attacker can create more dangerous scenarios than other methods, and the ADS trained with it achieves a collision rate half that of training with normal scenarios.
  arXiv  Detail & Related papers  (2025-01-27T08:18:52Z)
• Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving [65.61999354218628]
  We take the first step toward designing black-box adversarial attacks specifically targeting vision-language models (VLMs) in autonomous driving systems. We propose Cascading Adversarial Disruption (CAD), which targets low-level reasoning breakdown by generating and injecting semantics. We present Risky Scene Induction, which addresses dynamic adaptation by leveraging a surrogate VLM to understand and construct high-level risky scenarios.
  arXiv  Detail & Related papers  (2025-01-23T11:10:02Z)
• CRASH: Challenging Reinforcement-Learning Based Adversarial Scenarios For Safety Hardening [16.305837225117607]
  This paper introduces CRASH - Challenging Reinforcement-learning based Adversarial scenarios for Safety Hardening. First CRASH can control adversarial Non Player Character (NPC) agents in an AV simulator to automatically induce collisions with the Ego vehicle. We also propose a novel approach, that we term safety hardening, which iteratively refines the motion planner by simulating improvement scenarios against adversarial agents.
  arXiv  Detail & Related papers  (2024-11-26T00:00:27Z)
• LMM-enhanced Safety-Critical Scenario Generation for Autonomous Driving System Testing From Non-Accident Traffic Videos [22.638869562921133]
  It is paramount to generate a diverse range of safety-critical test scenarios for autonomous driving systems. Some accident-free real-world scenarios can not only lead to misbehaviors in ADSs but also be leveraged for the generation of ADS violations. It is of significant importance to discover safety violations of ADSs from routine traffic scenarios.
  arXiv  Detail & Related papers  (2024-06-16T09:05:56Z)
• Safe Multi-Agent Reinforcement Learning with Bilevel Optimization in Autonomous Driving [3.5293763645151404]
  We propose a safe MARL method grounded in a Stackelberg model with bi-level optimization. We develop two practical algorithms, namely Constrained Stackelberg Q-learning (CSQ) and Constrained Stackelberg Multi-Agent Deep Deterministic Policy Gradient (CS-MADDPG) Our algorithms, CSQ and CS-MADDPG, outperform several strong MARL baselines, such as Bi-AC, MACPO, and MAPPO-L, regarding reward and safety performance.
  arXiv  Detail & Related papers  (2024-05-28T14:15:18Z)
• PAFOT: A Position-Based Approach for Finding Optimal Tests of Autonomous Vehicles [4.243926243206826]
  This paper proposes PAFOT, a position-based approach testing framework. PAFOT generates adversarial driving scenarios to expose safety violations of Automated Driving Systems. Experiments show PAFOT can effectively generate safety-critical scenarios to crash ADSs and is able to find collisions in a short simulation time.
  arXiv  Detail & Related papers  (2024-05-06T10:04:40Z)
• Empowering Autonomous Driving with Large Language Models: A Safety Perspective [82.90376711290808]
  This paper explores the integration of Large Language Models (LLMs) into Autonomous Driving systems. LLMs are intelligent decision-makers in behavioral planning, augmented with a safety verifier shield for contextual safety learning. We present two key studies in a simulated environment: an adaptive LLM-conditioned Model Predictive Control (MPC) and an LLM-enabled interactive behavior planning scheme with a state machine.
  arXiv  Detail & Related papers  (2023-11-28T03:13:09Z)
• Learned Risk Metric Maps for Kinodynamic Systems [54.49871675894546]
  We present Learned Risk Metric Maps for real-time estimation of coherent risk metrics of high dimensional dynamical systems. LRMM models are simple to design and train, requiring only procedural generation of obstacle sets, state and control sampling, and supervised training of a function approximator.
  arXiv  Detail & Related papers  (2023-02-28T17:51:43Z)
• Learning energy-efficient driving behaviors by imitating experts [75.12960180185105]
  This paper examines the role of imitation learning in bridging the gap between control strategies and realistic limitations in communication and sensing. We show that imitation learning can succeed in deriving policies that, if adopted by 5% of vehicles, may boost the energy-efficiency of networks with varying traffic conditions by 15% using only local observations.
  arXiv  Detail & Related papers  (2022-06-28T17:08:31Z)
• Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks [62.923992740383966]
  We present the first study of security issues of MSF-based perception in AD systems. We generate a physically-realizable, adversarial 3D-printed object that misleads an AD system to fail in detecting it and thus crash into it. Our results show that the attack achieves over 90% success rate across different object types and MSF.
  arXiv  Detail & Related papers  (2021-06-17T05:11:07Z)

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.