Interactive Adversarial Testing of Autonomous Vehicles with Adjustable Confrontation Intensity

--Scientific testing techniques are essential for ensuring the safe operation of autonomous vehicles (A Vs), with high-risk, highly interactive scenarios being a primary focus. T o 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 V ehicle (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 A V's robustness across diverse scenarios and strategies. Extensive experiments across multiple scenarios and A V strategies demonstrate that ExamPPO can effectively modulate adversarial behavior, expose decision-making weaknesses in tested A Vs, and generalize across heterogeneous environments, thereby offering a unified and reproducible solution for evaluating the safety and intelligence of autonomous decision-making systems. UTONOMOUS driving technologies have achieved substantial progress in recent years, driven by advances in perception, planning, and control systems [1], [2], [3]. These innovations have accelerated the development and deployment of intelligent vehicles in structured and semi-structured environments. This work is supported in part by the National Natural Science Foundation of China (52472451, 62433014), the Shanghai Scientific Innovation Foundation (No.23DZ1203400), and the Fundamental Research Funds for the Central Universities. Yicheng Guo, Chengkai Xu, Peng Hang, and Jian Sun are with the College of Transportation, Tongji University, Shanghai 201804, China.