提出可交互式数字孪生测试框架，融合真实与虚拟环境提升车路协同系统测试效果
From Optimizable to Interactable: Mixed Digital Twin-Empowered Testing of Vehicle-Infrastructure Cooperation Systems
- 构建人机共控的混合数字孪生测试架构，引入真实人类行为
- 在真实测试平台I-VIT上验证，实现安全高效的极端场景生成
- 适合车路协同系统研发与测试人员使用
车路协同系统（VICS）的长期稳定运行亟需在极端场景下充分测试。然而，现有极端场景生成方法多依赖人工智能，且测试通常局限于纯仿真环境。本文在传统L4级‘可优化’数字孪生基础上，首次引入L5级‘可交互’概念，提出IMPACT（基于混合数字孪生的先进车路协同测试范式）。通过允许用户直接与系统实体交互，将高度不确定的人类行为自然融入测试流程，有效生成高质量极端场景，补充纯AI生成的不足。同时，基于混合数字孪生的‘物理-虚拟动作交互’机制，使测试可在真实环境和实体中进行，而非完全依赖仿真，显著提升测试真实性与安全性。我们在I-VIT（交互式车路协同测试平台）上实现了该框架，并通过实验验证了其有效性。相关演示视频见项目主页：https://dongjh20.github.io/IMPACT。
原文摘要 · Abstract (English)
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.
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