201 citations · 922 across the 22 of their papers we have counts for
22 papers
Efficient Weak-Entropy PINN for Solving Hyperbolic Conservation Laws
Qi Gao, Kuang Huang, Xuan Di
In recent years, neural networks have significantly advanced numerical solutions of partial differential equations (PDEs). However, solving PDEs with discontinuous solutions, such…
Risk Control of Traffic Flow Through Chance Constraints and Large Deviation Approximation
Rui Xu, Shanyin Tong, Xuan Di
We develop a rare chance-constrained optimal control framework for autonomous traffic management, with traffic dynamics governed by stochastic partial differential equations. To ef…
Simple vs. Optimal Congestion Pricing
Devansh Jalota, Xuan Di, Adam N. Elmachtoub
Congestion pricing has emerged as an effective tool for mitigating traffic congestion, yet implementing welfare or revenue-optimal dynamic tolls is often impractical. Most real-wor…
Waymo Driverless Car Data Analysis and Driving Modeling using CNN and LSTM
Aashish Kumar Misraa, Naman Jain, Saurav Singh Dhakad
Self driving cars has been the biggest innovation in the automotive industry, but to achieve human level accuracy or near human level accuracy is the biggest challenge that researc…
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 Unified Network Equilibrium for E-Hailing Platform Operation and Customer Mode Choice
Xu Chen, Xuan Di
This paper aims to combine both economic and network user equilibrium for ride-sourcing and ride-pooling services, while endogenously optimizing the pooling sequence of two origin-…
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…
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.…
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…
Reward Design for Driver Repositioning Using Multi-Agent Reinforcement Learning
Zhenyu Shou, Xuan Di
A large portion of passenger requests is reportedly unserviced, partially due to vacant for-hire drivers' cruising behavior during the passenger seeking process. This paper aims to…
Liability Design for Autonomous Vehicles and Human-Driven Vehicles: A Hierarchical Game-Theoretic Approach
Xuan Di, Xu Chen, Eric Talley
Autonomous vehicles (AVs) are inevitably entering our lives with potential benefits for improved traffic safety, mobility, and accessibility. However, AVs' benefits also introduce…
Stabilizing Traffic via Autonomous Vehicles: A Continuum Mean Field Game Approach
Kuang Huang, Xuan Di, Qiang Du +1
This paper presents scalable traffic stability analysis for both pure autonomous vehicle (AV) traffic and mixed traffic based on continuum traffic flow models. Human vehicles are m…
Optimal Passenger-Seeking Policies on E-hailing Platforms Using Markov Decision Process and Imitation Learning
Zhenyu Shou, Xuan Di, Jieping Ye +3
Vacant taxi drivers' passenger seeking process in a road network generates additional vehicle miles traveled, adding congestion and pollution into the road network and the environm…
A Game-Theoretic Framework for Autonomous Vehicles Velocity Control: Bridging Microscopic Differential Games and Macroscopic Mean Field Games
Kuang Huang, Xuan Di, Qiang Du +1
This paper proposes an efficient computational framework for longitudinal velocity control of a large number of autonomous vehicles (AVs) and develops a traffic flow theory for AVs…