In the emerging mixed traffic environments, Connected and Autonomous Vehicles (CAVs) have to interact with surrounding human-driven vehicles (HDVs). This paper introduces MSH-MCCT (Multi-Source Human-in-the-Loop Mixed Cloud Control Testbed), a novel CAV testbed that captures complex interactions between various CAVs and HDVs. Utilizing the Mixed Digital Twin concept, which combines Mixed Reality with Digital Twin, MSH-MCCT integrates physical, virtual, and mixed platforms, along with multi-source control inputs. Bridged by the mixed platform, MSH-MCCT allows human drivers and CAV algorithms to operate both physical and virtual vehicles within multiple fields of view. Particularly, this testbed facilitates the coexistence and real-time interaction of physical and virtual CAVs \& HDVs, significantly enhancing the experimental flexibility and scalability. Experiments on vehicle platooning in mixed traffic showcase the potential of MSH-MCCT to conduct CAV testing with multi-source real human drivers in the loop through driving simulators of diverse fidelity. The videos for the experiments are available at our project website: https://dongjh20.github.io/MSH-MCCT.

翻译：在日益兴起的混合交通环境中，网联自动驾驶车辆（CAV）需与周围的人类驾驶车辆（HDV）进行交互。本文提出MSH-MCCT（多源人在环混合云控测试平台）——一种新型CAV测试平台，能够捕捉不同CAV与HDV之间的复杂交互作用。该平台基于混合数字孪生概念（融合混合现实与数字孪生技术），整合了物理平台、虚拟平台及混合平台，并结合多源控制输入。通过混合平台的桥接作用，MSH-MCCT允许人类驾驶员与CAV算法在多个视场中操控物理与虚拟车辆。尤为重要的是，该测试平台可实现物理与虚拟CAV及HDV的共存与实时交互，显著增强了实验灵活性与可扩展性。针对混合交通中车辆编队开展的实验表明，MSH-MCCT能够通过不同保真度的驾驶模拟器，将多源真实人类驾驶员纳入测试环路，开展CAV性能评估。实验演示视频可访问项目官网：https://dongjh20.github.io/MSH-MCCT。