Interactive Adversarial Testing of Autonomous Vehicles with Adjustable Confrontation Intensity

2025-07-29

AI Summary

Current autonomous driving testing methods suffer from reliance on high-quality data, limited interactivity, and insufficient adversarial robustness. To address these limitations, this paper proposes an interactive adversarial testing framework with tunable attack intensity. It employs context-aware intelligent agent vehicles to exert persistent, realistic interference, thereby constructing dynamic and scalable interactive test scenarios. A scalar adversarial factor enables continuous, fine-grained control over adversarial strength. Furthermore, the framework integrates a multi-head attention–enhanced policy network with structured safety evaluation metrics, facilitating adaptive, cross-scenario and cross-policy testing. Experimental results demonstrate that the framework significantly improves fault excitation capability, test coverage, and controllability of adversarial intensity. It exhibits strong environmental generalizability and reproducibility across diverse autonomous driving policies and complex interactive scenarios.

Technology Category

• Multiagent Systems: Adversarial Agents
• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements

Abstract

Scientific testing techniques are essential for ensuring the safe operation of autonomous vehicles (AVs), with high-risk, highly interactive scenarios being a primary focus. To address the limitations of existing testing methods, such as their heavy reliance on high-quality test data, weak interaction capabilities, and low adversarial robustness, this paper proposes ExamPPO, an interactive adversarial testing framework that enables scenario-adaptive and intensity-controllable evaluation of autonomous vehicles. The framework models the Surrounding Vehicle (SV) as an intelligent examiner, equipped with a multi-head attention-enhanced policy network, enabling context-sensitive and sustained behavioral interventions. A scalar confrontation factor is introduced to modulate the intensity of adversarial behaviors, allowing continuous, fine-grained adjustment of test difficulty. Coupled with structured evaluation metrics, ExamPPO systematically probes AV's robustness across diverse scenarios and strategies. Extensive experiments across multiple scenarios and AV strategies demonstrate that ExamPPO can effectively modulate adversarial behavior, expose decision-making weaknesses in tested AVs, and generalize across heterogeneous environments, thereby offering a unified and reproducible solution for evaluating the safety and intelligence of autonomous decision-making systems.

Problem

• Develops interactive adversarial testing for autonomous vehicles safety
• Enables adjustable confrontation intensity in AV testing scenarios
• Addresses limitations in current AV testing methods

Innovation

• Interactive adversarial testing with adjustable intensity
• Multi-head attention-enhanced policy network
• Scalar confrontation factor for test difficulty