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Found 8 papers, 1 papers with code
Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook
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), a paradigm hybridizing physics-based models and deep neural networks (DNN), has been booming in science and engineering fields.
Quantifying Uncertainty In Traffic State Estimation Using Generative Adversarial Networks
This paper aims to quantify uncertainty in traffic state estimation (TSE) using the generative adversarial network based physics-informed deep learning (PIDL).
TrafficFlowGAN: Physics-informed Flow based Generative Adversarial Network for Uncertainty Quantification
TrafficFlowGAN adopts a normalizing flow model as the generator to explicitly estimate the data likelihood.
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.
A Physics-Informed Deep Learning Paradigm for Car-Following Models
We design physics-informed deep learning car-following (PIDL-CF) architectures encoded with two popular physics-based models - IDM and OVM, on which acceleration is predicted for four traffic regimes: acceleration, deceleration, cruising, and emergency braking.
Cluster Naturalistic Driving Encounters Using Deep Unsupervised Learning
Learning knowledge from driving encounters could help self-driving cars make appropriate decisions when driving in complex settings with nearby vehicles engaged.
Extraction of V2V Encountering Scenarios from Naturalistic Driving Database
To overcome this problem, we extract naturalistic V2V encounters data from the database, and then separate the primary vehicle encounter category by clustering.
