@article{Dong2026, 
author = {Jianghong Dong and Chunying Yang and Mengchi Cai and Chaoyi Chen and Qing Xu and Jianqiang Wang and Jiawei Wang and Keqiang Li},
title = {Multisource human-in-the-loop digital twin testbed for connected and autonomous vehicles in mixed traffic flow},
year = {2026},
journal = {Journal of Intelligent and Connected Vehicles},
volume = {9},
number = {2},
pages = {9210084},
keywords = {digital twin, connected and autonomous vehicle (CAV) testing, mixed reality, mixed traffic},
url = {https://www.sciopen.com/article/10.26599/JICV.2026.9210084},
doi = {10.26599/JICV.2026.9210084},
abstract = {In emerging mixed traffic environments, connected and autonomous vehicles (CAVs) must interact with surrounding human-driven vehicles (HDVs). This study introduces multisource human-in-the-loop mixed cloud control testbed (MSH-MCCT), 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 twins, MSH-MCCT integrates physical, virtual, and mixed platforms, along with multisource 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. In particular, this testbed facilitates the coexistence and real-time interaction of physical and virtual CAVs and 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 multisource real human drivers in the loop through driving simulators of diverse fidelity.}
}