6 citations · 9 across the 13 of their papers we have counts for
13 papers
Securing Autonomous Vehicle Systems via Twin-Aware Federated Reinforcement Learning
Zifan Zhang, Minghong Fang, Dianwei Chen +5
Federated reinforcement learning (FRL) is crucial for enabling collaborative learning across multiple agents without sharing raw data, thereby enhancing privacy and scalability in…
Customized Generative AI Agent for Transportation Engineering Practice: A Development and Continued Pre-training Guideline
Dianwei Chen, Yuan-Zheng Lei, Zifan Zhang +2
Recent advancements in generative artificial intelligence (AI) and large language models (LLMs) have shown significant promise in automating complex reasoning, summarization, and q…
Act on What You See: Unlocking Safe Social Navigation in Vision-Language-Action Models
Qingzi Wang, Xiyang Wu, Guangyao Shi +3
Safe social navigation requires robots to distinguish people from ordinary obstacles and to react before danger becomes imminent. We show that pretrained Vision-Language-Action (VL…
Network Digital Untwinning: Towards Backward Optimization of Digital Twins
Zifan Zhang, Dianwei Chen, Anjun Gao +5
Network digital twins (NDTs) are transforming network management by offering precise virtual replicas of physical network systems. However, their reliance on diverse and sensitive…
Improving Feasibility in Quantum Approximate Optimization Algorithm for Vehicle Routing via Constraint-Aware Initialization and Hybrid XY-X Mixing
Yuan-Zheng Lei, Yaobang Gong, Xianfeng Terry Yang +1
The Quantum Approximate Optimization Algorithm (QAOA) is a leading framework for quantum combinatorial optimization. The Vehicle Routing Problem (VRP), a core problem in logistics…
Reconstructing Physics-Informed Machine Learning for Traffic Flow Modeling: a Multi-Gradient Descent and Pareto Learning Approach
Yuan-Zheng Lei, Yaobang Gong, Dianwei Chen +2
Physics-informed machine learning (PIML) is crucial in modern traffic flow modeling because it combines the benefits of both physics-based and data-driven approaches. In convention…
Potential failures of physics-informed machine learning in traffic flow modeling: theoretical and experimental analysis
Yuan-Zheng Lei, Yaobang Gong, Dianwei Chen +2
This study investigates why physics-informed machine learning (PIML) can fail in macroscopic traffic flow modeling. We define failure as cases where a PIML model underperforms both…
Advanced Longitudinal Control and Collision Avoidance for High-Risk Edge Cases in Autonomous Driving
Dianwei Chen, Yaobang Gong, Xianfeng Yang
Advanced Driver Assistance Systems (ADAS) and Advanced Driving Systems (ADS) are key to improving road safety, yet most existing implementations focus primarily on the vehicle ahea…
Synergizing AI and Digital Twins for Next-Generation Network Optimization, Forecasting, and Security
Zifan Zhang, Minghong Fang, Dianwei Chen +2
Digital network twins (DNTs) are virtual representations of physical networks, designed to enable real-time monitoring, simulation, and optimization of network performance. When in…
A Scalable Min-Max Multi-Gradient Descent Method for Multi-Objective Transportation Problems
Yuan-Zheng Lei, Yaobang Gong, Xianfeng Terry Yang
This paper develops a scalable min-max multi-gradient descent framework for multi-objective transportation problems with competing objectives and high-dimensional constraints. Unli…
INSIGHT: Enhancing Autonomous Driving Safety through Vision-Language Models on Context-Aware Hazard Detection and Edge Case Evaluation
Dianwei Chen, Zifan Zhang, Lei Cheng +2
Autonomous driving systems face significant challenges in handling unpredictable edge-case scenarios, such as adversarial pedestrian movements, dangerous vehicle maneuvers, and sud…
Deep Reinforcement Learning for Advanced Longitudinal Control and Collision Avoidance in High-Risk Driving Scenarios
Dianwei Chen, Yaobang Gong, Xianfeng Yang
Existing Advanced Driver Assistance Systems primarily focus on the vehicle directly ahead, often overlooking potential risks from following vehicles. This oversight can lead to ine…
Unraveling stochastic fundamental diagrams considering empirical knowledge: modeling, limitation and further discussion
Yuan-Zheng Lei, Yaobang Gong, Xianfeng Terry Yang
Traffic flow modeling relies heavily on fundamental diagrams. However, deterministic fundamental diagrams, such as single or multi-regime models, cannot capture the uncertainty pat…