124 citations · 134 across the 2 of their papers we have counts for
5 papers
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…
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…
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…