Zhaobin Mo

According to our database, Zhaobin Mo authored at least 16 papers between 2018 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Physics-Informed Deep Learning for Traffic State Estimation: A Survey and the Outlook.

Xuan Di, Rongye Shi, Zhaobin Mo, Yongjie Fu

Algorithms, June, 2023

Robust Data Sampling in Machine Learning: A Game-Theoretic Framework for Training and Validation Data Selection.

Zhaobin Mo, Xuan Di, Rongye Shi

Games, February, 2023

Detecting mild cognitive impairment and dementia in older adults using naturalistic driving data and interaction-based classification from influence score.

Xuan Di, Yiqiao Yin, Yongjie Fu, Zhaobin Mo, Shaw-Hwa Lo, Carolyn DiGuiseppi, David W. Eby, Linda L. Hill, Thelma J. Mielenz, David Strogatz, Minjae Kim, Guohua Li

Artif. Intell. Medicine, 2023

2022

A Physics-Informed Deep Learning Paradigm for Traffic State and Fundamental Diagram Estimation.

Rongye Shi, Zhaobin Mo, Kuang Huang, Xuan Di, Qiang Du

IEEE Trans. Intell. Transp. Syst., 2022

TrafficFlowGAN: Physics-Informed Flow Based Generative Adversarial Network for Uncertainty Quantification.

Zhaobin Mo, Yongjie Fu, Daran Xu, Xuan Di

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

Quantifying Uncertainty In Traffic State Estimation Using Generative Adversarial Networks.

Zhaobin Mo, Yongjie Fu, Xuan Di

Proceedings of the 25th IEEE International Conference on Intelligent Transportation Systems, 2022

2021

A Physics-Informed Deep Learning Paradigm for Traffic State Estimation and Fundamental Diagram Discovery.

Rongye Shi, Zhaobin Mo, Kuang Huang, Xuan Di, Qiang Du

CoRR, 2021

Physics-Informed Deep Learning for Traffic State Estimation.

Rongye Shi, Zhaobin Mo, Kuang Huang, Xuan Di, Qiang Du

CoRR, 2021

Physics-Informed Deep Learning for Traffic State Estimation: A Hybrid Paradigm Informed By Second-Order Traffic Models.

Rongye Shi, Zhaobin Mo, Xuan Di

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

A Physics-Informed Deep Learning Paradigm for Car-Following Models.

Zhaobin Mo, Xuan Di, Rongye Shi

CoRR, 2020

When Do Drivers Concentrate? Attention-based Driver Behavior Modeling With Deep Reinforcement Learning.

Xingbo Fu, Xuan Di, Zhaobin Mo

CoRR, 2020

2018

Interpenetrating Cooperative Localization in Dynamic Connected Vehicle Networks.

Huajing Zhao, Zhaobin Mo, Macheng Shen, Jing Sun, Ding Zhao

CoRR, 2018

Clustering of Naturalistic Driving Encounters Using Unsupervised Learning.

Sisi Li, Wenshuo Wang, Zhaobin Mo, Ding Zhao

CoRR, 2018

Extracting V2V Encountering Scenarios from Naturalistic Driving Database.

Zhaobin Mo, Sisi Li, Diange Yang, Ding Zhao

CoRR, 2018

Cluster Naturalistic Driving Encounters Using Deep Unsupervised Learning.

Sisi Li, Wenshuo Wang, Zhaobin Mo, Ding Zhao

Proceedings of the 2018 IEEE Intelligent Vehicles Symposium, 2018

Multimedia Fusion at Semantic Level in Vehicle Cooperactive Perception.

Zhongyang Xiao, Zhaobin Mo, Kun Jiang, Diange Yang

Proceedings of the 2018 IEEE International Conference on Multimedia & Expo Workshops, 2018