Sufficient testing under corner cases is critical for the long-term operation of vehicle-infrastructure cooperation systems (VICS). However, existing corner-case generation methods are primarily AI-driven, and VICS testing under corner cases is typically limited to simulation. In this paper, we introduce an L5 ''Interactable'' level to the VICS digital twin (VICS-DT) taxonomy, extending beyond the conventional L4 ''Optimizable'' level. We further propose an L5-level VICS testing framework, IMPACT (Interactive Mixed-digital-twin Paradigm for Advanced Cooperative vehicle-infrastructure Testing). By enabling direct human interactions with VICS entities, IMPACT incorporates highly uncertain and unpredictable human behaviors into the testing loop, naturally generating high-quality corner cases that complement AI-based methods. Furthermore, the mixedDT-enabled ''Physical-Virtual Action Interaction'' facilitates safe VICS testing under corner cases, incorporating real-world environments and entities rather than purely in simulation. Finally, we implement IMPACT on the I-VIT (Interactive Vehicle-Infrastructure Testbed), and experiments demonstrate its effectiveness. The experimental videos are available at our project website: https://dongjh20.github.io/IMPACT.

翻译：在极端工况下进行充分测试对车辆-基础设施协同系统的长期运行至关重要。然而，现有的极端工况生成方法主要依赖人工智能驱动，且相关测试通常局限于仿真环境。本文在VICS数字孪生分类体系中引入超越传统L4"可优化"层级的L5"可交互"层级，并提出L5级VICS测试框架IMPACT（面向先进车路协同测试的交互式混合数字孪生范式）。该框架通过支持人类与VICS实体直接交互，将高度不确定且不可预测的人类行为纳入测试闭环，自然生成可补充人工智能方法的高质量极端工况。此外，基于混合数字孪生的"物理-虚拟动作交互"机制，能够在融合真实环境与实体的条件下安全开展极端工况测试，而非局限于纯仿真环境。最后，我们在I-VIT交互式车路协同测试平台上实现了IMPACT框架，实验验证了其有效性。实验视频详见项目网站：https://dongjh20.github.io/IMPACT。