201 citations · 412 across the 18 of their papers we have counts for
20 papers
STRIDE: Evaluating Spatiotemporal Reasoning in Driving Edge Cases
Lieqi Liu, Rui gao, Jia-Chen Gu +6
In autonomous driving, perceiving surrounding objects is not enough: failing to understand how they move and interact over time can lead to dangerous decisions. Evaluating vision-l…
Generative AI for Autonomous Driving: Frontiers and Opportunities
Yuping Wang, Shuo Xing, Cui Can +46
Generative Artificial Intelligence (GenAI) constitutes a transformative technological wave that reconfigures industries through its unparalleled capabilities for content creation,…
Discovering the Precursors of Traffic Breakdowns Using Spatiotemporal Graph Attribution Networks
Zhaobin Mo, Xiangyi Liao, Dominik A. Karbowski +1
Understanding and predicting the precursors of traffic breakdowns is critical for improving road safety and traffic flow management. This paper presents a novel approach combining…
SafeAug: Safety-Critical Driving Data Augmentation from Naturalistic Datasets
Zhaobin Mo, Yunlong Li, Xuan Di
Safety-critical driving data is crucial for developing safe and trustworthy self-driving algorithms. Due to the scarcity of safety-critical data in naturalistic datasets, current a…
AI-Powered CPS-Enabled Vulnerable-User-Aware Urban Transportation Digital Twin: Methods and Applications
Yongjie Fu, Mehmet K. Turkcan, Mahshid Ghasemi +6
We present methods and applications for the development of digital twins (DT) for urban traffic management. While the majority of studies on the DT focus on its ``eyes," which is t…
diffIRM: A Diffusion-Augmented Invariant Risk Minimization Framework for Spatiotemporal Prediction over Graphs
Zhaobin Mo, Haotian Xiang, Xuan Di
Spatiotemporal prediction over graphs (STPG) is challenging, because real-world data suffers from the Out-of-Distribution (OOD) generalization problem, where test data follow diffe…
Causal Adjacency Learning for Spatiotemporal Prediction Over Graphs
Zhaobin Mo, Qingyuan Liu, Baohua Yan +2
Spatiotemporal prediction over graphs (STPG) is crucial for transportation systems. In existing STPG models, an adjacency matrix is an important component that captures the relatio…
Can LLMs Understand Social Norms in Autonomous Driving Games?
Boxuan Wang, Haonan Duan, Yanhao Feng +4
Social norm is defined as a shared standard of acceptable behavior in a society. The emergence of social norms fosters coordination among agents without any hard-coded rules, which…
DriveGenVLM: Real-world Video Generation for Vision Language Model based Autonomous Driving
Yongjie Fu, Anmol Jain, Xuan Di +2
The advancement of autonomous driving technologies necessitates increasingly sophisticated methods for understanding and predicting real-world scenarios. Vision language models (VL…
Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook
Xuan Di, Rongye Shi, Zhaobin Mo +1
For its robust predictive power (compared to pure physics-based models) and sample-efficient training (compared to pure deep learning models), physics-informed deep learning (PIDL)…
Quantifying Uncertainty In Traffic State Estimation Using Generative Adversarial Networks
Zhaobin Mo, Yongjie Fu, Xuan Di
This paper aims to quantify uncertainty in traffic state estimation (TSE) using the generative adversarial network based physics-informed deep learning (PIDL). The uncertainty of t…
TrafficFlowGAN: Physics-informed Flow based Generative Adversarial Network for Uncertainty Quantification
Zhaobin Mo, Yongjie Fu, Daran Xu +1
This paper proposes the TrafficFlowGAN, a physics-informed flow based generative adversarial network (GAN), for uncertainty quantification (UQ) of dynamical systems. TrafficFlowGAN…
A Physics-Informed Deep Learning Paradigm for Traffic State and Fundamental Diagram Estimation
Rongye Shi, Zhaobin Mo, Kuang Huang +2
Traffic state estimation (TSE) bifurcates into two categories, model-driven and data-driven (e.g., machine learning, ML), while each suffers from either deficient physics or small…
CVLight: Decentralized Learning for Adaptive Traffic Signal Control with Connected Vehicles
Mobin Zhao, Wangzhi Li, Yongjie Fu +2
This paper develops a decentralized reinforcement learning (RL) scheme for multi-intersection adaptive traffic signal control (TSC), called "CVLight", that leverages data collected…
Physics-Informed Deep Learning for Traffic State Estimation
Rongye Shi, Zhaobin Mo, Kuang Huang +2
Traffic state estimation (TSE), which reconstructs the traffic variables (e.g., density) on road segments using partially observed data, plays an important role on efficient traffi…
A Physics-Informed Deep Learning Paradigm for Car-Following Models
Zhaobin Mo, Xuan Di, Rongye Shi
Car-following behavior has been extensively studied using physics-based models, such as the Intelligent Driver Model. These models successfully interpret traffic phenomena observed…
When Do Drivers Concentrate? Attention-based Driver Behavior Modeling With Deep Reinforcement Learning
Xingbo Fu, Feng Gao, Jiang Wu
Driver distraction a significant risk to driving safety. Apart from spatial domain, research on temporal inattention is also necessary. This paper aims to figure out the pattern of…
Interpenetrating Cooperative Localization in Dynamic Connected Vehicle Networks
Huajing Zhao, Zhaobin Mo, Macheng Shen +2
In this paper, we proposed the Interpenetrating Cooperative Localization (ICL) method to enhance the localization accuracy in dynamic connected vehicle networks. This mechanism mak…
Cluster Naturalistic Driving Encounters Using Deep Unsupervised Learning
Sisi Li, Wenshuo Wang, Zhaobin Mo +1
Learning knowledge from driving encounters could help self-driving cars make appropriate decisions when driving in complex settings with nearby vehicles engaged. This paper develop…
Extraction of V2V Encountering Scenarios from Naturalistic Driving Database
Zhaobin Mo, Sisi Li, Diange Yang +1
It is necessary to thoroughly evaluate the effectiveness and safety of Connected Vehicles (CVs) algorithm before their release and deployment. Current evaluation approach mainly re…