124 citations · 152 across the 3 of their papers we have counts for
6 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…
A Survey on Autonomous Vehicle Control in the Era of Mixed-Autonomy: From Physics-Based to AI-Guided Driving Policy Learning
Xuan Di, Rongye Shi
This paper serves as an introduction and overview of the potentially useful models and methodologies from artificial intelligence (AI) into the field of transportation engineering…
An LSTM-Based Autonomous Driving Model Using Waymo Open Dataset
Zhicheng Gu, Zhihao Li, Xuan Di +1
The Waymo Open Dataset has been released recently, providing a platform to crowdsource some fundamental challenges for automated vehicles (AVs), such as 3D detection and tracking.…
LightNN: Filling the Gap between Conventional Deep Neural Networks and Binarized Networks
Ruizhou Ding, Zeye Liu, Rongye Shi +2
Application-specific integrated circuit (ASIC) implementations for Deep Neural Networks (DNNs) have been adopted in many systems because of their higher classification speed. Howev…