Multimedia Fusion at Semantic Level in Vehicle Cooperactive Perception. - Zhongyang Xiao; Zhaobin Mo; Kun Jiang 002; Diange Yang (ICME Workshops, 2018)

Cluster Naturalistic Driving Encounters Using Deep Unsupervised Learning. - Sisi Li; Wenshuo Wang 001; Zhaobin Mo; Ding Zhao (Intelligent Vehicles Symposium, 2018)

Extracting V2V Encountering Scenarios from Naturalistic Driving Database. - Zhaobin Mo; Sisi Li; Diange Yang; Ding Zhao (CoRR, 2018)

Clustering of Naturalistic Driving Encounters Using Unsupervised Learning. - Sisi Li; Wenshuo Wang 001; Zhaobin Mo; Ding Zhao (CoRR, 2018)

Interpenetrating Cooperative Localization in Dynamic Connected Vehicle Networks. - Huajing Zhao; Zhaobin Mo; Macheng Shen; Jing Sun 003; Ding Zhao (CoRR, 2018)

When Do Drivers Concentrate? Attention-based Driver Behavior Modeling With Deep Reinforcement Learning. - Xingbo Fu; Xuan Di; Zhaobin Mo (CoRR, 2020)

A Physics-Informed Deep Learning Paradigm for Car-Following Models. - Zhaobin Mo; Xuan Di; Rongye Shi (CoRR, 2020)

Physics-Informed Deep Learning for Traffic State Estimation: A Hybrid Paradigm Informed By Second-Order Traffic Models. - Rongye Shi; Zhaobin Mo; Xuan Di (AAAI, 2021)

Physics-Informed Deep Learning for Traffic State Estimation. - Rongye Shi; Zhaobin Mo; Kuang Huang; Xuan Di; Qiang Du 001 (CoRR, 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 001 (CoRR, 2021)

Quantifying Uncertainty In Traffic State Estimation Using Generative Adversarial Networks. - Zhaobin Mo; Yongjie Fu; Xuan Di (ITSC, 2022)

TrafficFlowGAN: Physics-Informed Flow Based Generative Adversarial Network for Uncertainty Quantification. - Zhaobin Mo; Yongjie Fu; Daran Xu; Xuan Di (ECML/PKDD (3), 2022)

A Physics-Informed Deep Learning Paradigm for Traffic State and Fundamental Diagram Estimation. - Rongye Shi; Zhaobin Mo; Kuang Huang; Xuan Di; Qiang Du 001 (IEEE Trans. Intell. Transp. Syst., 2022)

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)

Robust Data Sampling in Machine Learning: A Game-Theoretic Framework for Training and Validation Data Selection. - Zhaobin Mo; Xuan Di; Rongye Shi (Games, 2023)

Physics-Informed Deep Learning for Traffic State Estimation: A Survey and the Outlook. - Xuan Di; Rongye Shi; Zhaobin Mo; Yongjie Fu (Algorithms, 2023)

PI-NeuGODE: Physics-Informed Graph Neural Ordinary Differential Equations for Spatiotemporal Trajectory Prediction. - Zhaobin Mo; Yongjie Fu; Xuan Di (AAMAS, 2024)

SafeAug: Safety-Critical Driving Data Augmentation from Naturalistic Datasets. - Yunlong Li; Zhaobin Mo; Xuan Di (ITSC, 2024)

Causal Adjacency Learning for Spatiotemporal Prediction Over Graphs. - Zhaobin Mo; Qingyuan Liu; Baohua Yan; Longxiang Zhang; Xuan Di (ITSC, 2024)

Can LLMs Understand Social Norms in Autonomous Driving Games? - Boxuan Wang; Haonan Duan 003; Yanhao Feng; Xu Chen 033; Yongjie Fu; Zhaobin Mo; Xuan Di (CoRR, 2024)

DriveGenVLM: Real-world Video Generation for Vision Language Model based Autonomous Driving. - Yongjie Fu; Anmol Jain; Xuan Di; Xu Chen 033; Zhaobin Mo (CoRR, 2024)

A Game-Theoretic Framework for Generic Second-Order Traffic Flow Models Using Mean Field Games and Adversarial Inverse Reinforcement Learning. - Zhaobin Mo; Xu Chen 033; Xuan Di; Elisa Iacomini; Chiara Segala; Michael Herty; Mathieu Laurière (Transp. Sci., 2024)

Cross- and Context-Aware Attention Based Spatial-Temporal Graph Convolutional Networks for Human Mobility Prediction. - Zhaobin Mo; Haotian Xiang; Xuan Di (ACM Trans. Spatial Algorithms Syst., 2024)

diffIRM: A Diffusion-Augmented Invariant Risk Minimization Framework for Spatiotemporal Prediction over Graphs. - Zhaobin Mo; Haotian Xiang; Xuan Di (Transp. Sci., 2025)

Physics-Informed Deep Learning for Trajectory Prediction and Uncertainty Quantification - Zhaobin Mo (2025)

AI-Powered Urban Transportation Digital Twin: Methods and Applications. - Xuan Di; Yongjie Fu; Mehmet Kerem Türkcan; Mahshid Ghasemi; Zhaobin Mo; Chengbo Zang; Abhishek Adhikari; Zoran Kostic; Gil Zussman (CoRR, 2025)

Discovering the Precursors of Traffic Breakdowns Using Spatiotemporal Graph Attribution Networks. - Zhaobin Mo; Xiangyi Liao; Dominik Karbowski; Yanbing Wang (CoRR, 2025)

Generative AI for Autonomous Driving: Frontiers and Opportunities. - Yuping Wang; Shuo Xing; Cui Can; Renjie Li 003; Hongyuan Hua; Kexin Tian; Zhaobin Mo; Xiangbo Gao; Keshu Wu; Sulong Zhou; Hengxu You; Juntong Peng; et al. (CoRR, 2025)