Search Results for author:
Found 4 papers, 1 papers with code
Discovering State Variables Hidden in Experimental Data
All physical laws are described as relationships between state variables that give a complete and non-redundant description of the relevant system dynamics.
A Physics-Informed Deep Learning Paradigm for Traffic State and Fundamental Diagram Estimation
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 data.
Physics-Informed Deep Learning for Traffic State Estimation
This paper focuses on highway TSE with observed data from loop detectors, using traffic density as the traffic variables.
Dynamic driving and routing games for autonomous vehicles on networks: A mean field game approach
In this paper, we aim to develop a game-theoretic model to solve for AVs's optimal driving strategies of velocity control in the interior of a road link and route choice at a junction node.
Autonomous Vehicles
Decision Making
Optimization and Control
Systems and Control
Systems and Control
