Demonstration for MSH-MCCT

MSH-MCCT is a multi-source human-in-the-loop experimental platform for connected and autonomous vehicles in mixed traffic flow.

Jianghong Dong1, Jiawei Wang2,*, Chunying Yang1 Mengchi Cai1, Chaoyi Chen1,
Qing Xu1, Jianqiang Wang1, Keqiang Li1,*
1Tsinghua University 2University of Michigan

Experiment A: traffic wave scenario

In Experiment A, the CACC controllers enable the CAVs to dampen the velocity fluctuation of the head vehicle, preventing disturbances from amplifying within the platoon, while the human drivers are amplifying these fluctuations, especially the last one.

Experiment B: safety-critical scenario

In Experiment B, the CAVs distributed in the platoon mitigate the amplification of fluctuations caused by the head vehicle's sudden braking. In particular, since all the collisions involve at least one virtual vehicle, no actual damage is incurred by any vehicle.

Abstract

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 https://dongjh20.github.io/MSH-MCCT.

Detailed schematic


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Detailed mode of operation


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MSH-MCCT-based work


1. Data-enabled predictive leading cruise control in mixed traffic


Wang J, Zheng Y, Dong J, et al. Implementation and experimental validation of data-driven predictive control for dissipating stop-and-go waves in mixed traffic[J]. IEEE Internet of Things Journal, 2023. [paper link] [video link]


2. Mixed platoon control under noise and attacks


Li S, Chen C, Zheng H, et al. Mixed Platoon Control Under Noise and Attacks: Robust Data-Driven Predictive Control and Human-in-the-Loop Validation[J]. IEEE Transactions on Intelligent Transportation Systems, 2025. [paper link]


3. Robust explicit data-driven predictive control for mixed platoons


Li S, Zhou J, Wang J, et al. Robust explicit data-driven predictive control for mixed vehicle platoons[J]. IEEE Internet of Things Journal, 2025.


4. Multi-vehicle coordinated formation control in multiple lanes


Cai M, Xu Q, Yang C, et al. Experimental Validation of Multi-Lane Formation Control for Connected and Automated Vehicles[C]//2023 IEEE International Conference on Unmanned Systems (ICUS). IEEE, 2023: 1267-1273. [paper link] [video link]