Multi-vehicle experiment platform: A Digital Twin Realization Method

Yang, Chunying; Dong, Jianghong; Xu, Qing; Cai, Mengchi; Qin, Hongmao; Wang, Jianqiang; Li, Keqiang

doi:10.1109/sii52469.2022.9708890

Cited by 15 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yang et al [83] proposed a DT method for executing multivehicle experiments, using a combination of physical and virtual vehicles to perform coordination tasks. For this, a sand table testbed was developed with its DT in the cloud, where devices enable human-machine interaction, all visualized in a mixed-reality solution.…”

Section: A Connected and Autonomous Vehiclesmentioning

confidence: 99%

Sustainable Intelligent Transportation Systems via Digital Twins: A Contextualized Survey

Martínez,

Campelo,

Ribeiro

2024

Preprint

View full text Add to dashboard Cite

show abstract

Section: A Connected and Autonomous Vehiclesmentioning

confidence: 99%

Sustainable Intelligent Transportation Systems via Digital Twins: A Contextualized Survey

Martínez,

Campelo,

Ribeiro

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…We test DeeP-LCC in commercially available electric mini-vehicles running on a specially designed platform at Tsinghua University [24]. The platform architecture is shown in Fig.…”

Section: A Experiments Platformmentioning

confidence: 99%

Experimental validation of DeeP-LCC for dissipating stop-and-go waves in mixed traffic

Wang¹,

Zheng²,

Dong³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

We present results on the experimental validation of leading cruise control (LCC) for connected and autonomous vehicles (CAVs). In a mixed traffic situation that is dominated by human-driven vehicles, LCC strategies are promising to smooth undesirable stop-and-go waves. Our experiments are carried out on a mini-scale traffic platform. We first reproduce stop-and-go traffic waves in a miniature scale, and then show that these traffic instabilities can be dissipated by one or a few CAVs that utilize Data-EnablEd Predicted Leading Cruise Control (DeeP-LCC). Rather than identifying a parametric traffic model, DeeP-LCC relies on a data-driven non-parametric behavior representation for traffic prediction and CAV control. DeeP-LCC also incorporates input and output constraints to achieve collision-free guarantees for CAVs. We experimentally demonstrate that DeeP-LCC is able to dissipate traffic waves caused by car-following behavior and significantly improve both driving safety and travel efficiency. CAVs utilizing DeeP-LCC may bring additional societal benefits by mitigating stop-and-go waves in practical traffic.

show abstract

ViSE: Digital Twin Exploration for Automotive Functional Safety and Cybersecurity

Kabir,

Ray

2024

J Hardw Syst Secur

View full text Add to dashboard Cite

Multi-vehicle experiment platform: A Digital Twin Realization Method

Cited by 15 publications

References 10 publications

Sustainable Intelligent Transportation Systems via Digital Twins: A Contextualized Survey

Sustainable Intelligent Transportation Systems via Digital Twins: A Contextualized Survey

Experimental validation of DeeP-LCC for dissipating stop-and-go waves in mixed traffic

ViSE: Digital Twin Exploration for Automotive Functional Safety and Cybersecurity

Contact Info

Product

Resources

About