Zhaobin Mo

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 20

Top Topics

Unsupervised Learning

Ordinary Differential Equations

State Estimation

Top Venues

Artif. Intell. Medicine

Publications

Zhaobin Mo, Yongjie Fu, Xuan Di
PI-NeuGODE: Physics-Informed Graph Neural Ordinary Differential Equations for Spatiotemporal Trajectory Prediction. AAMAS (2024)
Zhaobin Mo, Xuan Di, Rongye Shi
Robust Data Sampling in Machine Learning: A Game-Theoretic Framework for Training and Validation Data Selection. Games 14 (1) (2023)
Xuan Di, Rongye Shi, Zhaobin Mo, Yongjie Fu
Physics-Informed Deep Learning for Traffic State Estimation: A Survey and the Outlook. Algorithms 16 (6) (2023)
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
Detecting mild cognitive impairment and dementia in older adults using naturalistic driving data and interaction-based classification from influence score. Artif. Intell. Medicine 138 (2023)
Xuan Di, Rongye Shi, Zhaobin Mo, Yongjie Fu
Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook. CoRR (2023)
Zhaobin Mo, Yongjie Fu, Daran Xu, Xuan Di
TrafficFlowGAN: Physics-Informed Flow Based Generative Adversarial Network for Uncertainty Quantification. ECML/PKDD (3) (2022)
Zhaobin Mo, Yongjie Fu, Xuan Di
Quantifying Uncertainty In Traffic State Estimation Using Generative Adversarial Networks. ITSC (2022)
Zhaobin Mo, Yongjie Fu, Daran Xu, Xuan Di
TrafficFlowGAN: Physics-informed Flow based Generative Adversarial Network for Uncertainty Quantification. CoRR (2022)
Zhaobin Mo, Yongjie Fu, Xuan Di
Quantifying Uncertainty In Traffic State Estimation Using Generative Adversarial Networks. CoRR (2022)
Rongye Shi, Zhaobin Mo, Kuang Huang, Xuan Di, Qiang Du
A Physics-Informed Deep Learning Paradigm for Traffic State and Fundamental Diagram Estimation. IEEE Trans. Intell. Transp. Syst. 23 (8) (2022)
Rongye Shi, Zhaobin Mo, Kuang Huang, Xuan Di, Qiang Du
A Physics-Informed Deep Learning Paradigm for Traffic State Estimation and Fundamental Diagram Discovery. CoRR (2021)
Rongye Shi, Zhaobin Mo, Kuang Huang, Xuan Di, Qiang Du
Physics-Informed Deep Learning for Traffic State Estimation. CoRR (2021)
Rongye Shi, Zhaobin Mo, Xuan Di
Physics-Informed Deep Learning for Traffic State Estimation: A Hybrid Paradigm Informed By Second-Order Traffic Models. AAAI (2021)
Zhaobin Mo, Xuan Di, Rongye Shi
A Physics-Informed Deep Learning Paradigm for Car-Following Models. CoRR (2020)
Xingbo Fu, Xuan Di, Zhaobin Mo
When Do Drivers Concentrate? Attention-based Driver Behavior Modeling With Deep Reinforcement Learning. CoRR (2020)
Zhongyang Xiao, Zhaobin Mo, Kun Jiang, Diange Yang
Multimedia Fusion at Semantic Level in Vehicle Cooperactive Perception. ICME Workshops (2018)
Sisi Li, Wenshuo Wang, Zhaobin Mo, Ding Zhao
Clustering of Naturalistic Driving Encounters Using Unsupervised Learning. CoRR (2018)
Huajing Zhao, Zhaobin Mo, Macheng Shen, Jing Sun, Ding Zhao
Interpenetrating Cooperative Localization in Dynamic Connected Vehicle Networks. CoRR (2018)
Zhaobin Mo, Sisi Li, Diange Yang, Ding Zhao
Extracting V2V Encountering Scenarios from Naturalistic Driving Database. CoRR (2018)
Sisi Li, Wenshuo Wang, Zhaobin Mo, Ding Zhao
Cluster Naturalistic Driving Encounters Using Deep Unsupervised Learning. Intelligent Vehicles Symposium (2018)