构建人车共控的混合交通仿真测试平台，提升自动驾驶测试灵活性。
Multi-Source Human-in-the-Loop Digital Twin Testbed for Connected and Autonomous Vehicles in Mixed Traffic Flow
- 融合物理、虚拟与混合平台，支持多源人类驾驶员实时参与
- 实现真实与虚拟车辆在混合交通中的协同运行与交互
- 适用于高保真驾驶模拟器下的自动驾驶算法测试
在新兴的混合交通环境中，联网与自动驾驶汽车（CAVs）需与人类驾驶车辆（HDVs）互动。本文提出一种新型CAV测试平台MSH-MCCT（多源人机协同混合云控测试平台），通过混合数字孪生技术，结合现实与虚拟平台，集成物理、虚拟及混合环境，并支持多源控制输入。该平台通过混合界面，使人类驾驶员与自动驾驶算法能同步操控真实与虚拟车辆，在多个视场中协同作业。特别地，该测试平台实现了真实与虚拟CAVs与HDVs的共存与实时交互，显著提升了实验的灵活性与可扩展性。在混合交通中的车队编队实验表明，该平台可通过不同保真度的驾驶模拟器，实现多源真人驾驶员在环的CAV测试。实验视频可在项目网站获取：https://dongjh20.github.io/MSH-MCCT。
原文摘要 · Abstract (English)
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.
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