多源人机协同数字孪生测试平台用于混合交通流中的连接与自动驾驶车辆
Multisource human-in-the-loop digital twin testbed for connected and autonomous vehicles in mixed traffic flow
AI总结：
本文提出了一种多源人机协同混合云控制测试平台（MSH-MCCT），用于在混合交通环境中测试连接与自动驾驶车辆（CAVs）与人类驾驶车辆（HDVs）之间的复杂交互，通过混合数字孪生概念结合混合现实与数字孪生，提升实验灵活性和可扩展性。
摘要：
在新兴的混合交通环境中，连接与自动驾驶车辆（CAVs）必须与周围的人类驾驶车辆（HDVs）进行交互。本文介绍MSH-MCCT（多源人机协同混合云控制测试平台），一种新的CAV测试平台，能够捕捉各种CAVs和HDVs之间的复杂交互。利用混合数字孪生概念，该概念结合了混合现实与数字孪生，MSH-MCCT整合了物理、虚拟和混合平台，以及多源控制输入。通过混合平台的连接，MSH-MCCT允许人类驾驶员和CAV算法在多个视野范围内同时操作物理和虚拟车辆。特别地，该测试平台促进了物理和虚拟CAVs与HDVs的共存和实时交互，显著提高了实验的灵活性和可扩展性。在混合交通中的车辆编队实验展示了MSH-MCCT通过不同保真度的驾驶模拟器进行多源真实人类驾驶员闭环CAV测试的潜力。实验视频可在我们的项目网站上获得：https://dongjh20.github.io/MSH-MCCT。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
