124 citations · 279 across the 11 of their papers we have counts for
8 papers · 1 filter
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…
Dynamic driving and routing games for autonomous vehicles on networks: A mean field game approach
Kuang Huang, Xu Chen, Xuan Di +1
This paper aims to answer the research question as to optimal design of decision-making processes for autonomous vehicles (AVs), including dynamical selection of driving velocity a…
Multi-Agent Reinforcement Learning for Markov Routing Games: A New Modeling Paradigm For Dynamic Traffic Assignment
Zhenyu Shou, Xu Chen, Yongjie Fu +1
This paper aims to develop a paradigm that models the learning behavior of intelligent agents (including but not limited to autonomous vehicles, connected and automated vehicles, o…
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…
Long-Term Prediction of Lane Change Maneuver Through a Multilayer Perceptron
Zhenyu Shou, Ziran Wang, Kyungtae Han +3
Behavior prediction plays an essential role in both autonomous driving systems and Advanced Driver Assistance Systems (ADAS), since it enhances vehicle's awareness of the imminent…
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.…