6 citations · 9 across the 13 of their papers we have counts for
6 papers · 1 filter
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…