A systematic review of generative adversarial networks for traffic state prediction: overview, taxonomy, and future prospects
In recent years, advances in deep learning have had a significant impact in the
transportation domain, notably through the use of generative adversarial networks (GAN). As …
transportation domain, notably through the use of generative adversarial networks (GAN). As …
A Survey of Generative AI for Intelligent Transportation Systems
Intelligent transportation systems play a crucial role in modern traffic management and
optimization, greatly improving traffic efficiency and safety. With the rapid development of …
optimization, greatly improving traffic efficiency and safety. With the rapid development of …
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 …
efficient training (compared to pure deep learning models), physics-informed deep learning …
Stochastic analysis of touch-tone frequency recognition in two-way radio systems for dialed telephone number identification
This paper focuses on recognizing dialed numbers in a touch-tone telephone system based
on the Dual Tone Multi-Frequency (DTMF) signaling technique with analysis of stochastic …
on the Dual Tone Multi-Frequency (DTMF) signaling technique with analysis of stochastic …
Trafficflowgan: Physics-informed flow based generative adversarial network for uncertainty quantification
This paper proposes the TrafficFlowGAN, a physics-informed flow based generative
adversarial network (GAN), for uncertainty quantification (UQ) of dynamical systems …
adversarial network (GAN), for uncertainty quantification (UQ) of dynamical systems …
A survey on the application of generative adversarial networks in cybersecurity: prospective, direction and open research scopes
With the proliferation of Artificial Intelligence, there has been a massive increase in the
amount of data required to be accumulated and disseminated digitally. As the data are …
amount of data required to be accumulated and disseminated digitally. As the data are …
A gentle introduction and tutorial on deep generative models in transportation research
Deep Generative Models (DGMs) have rapidly advanced in recent years, becoming
essential tools in various fields due to their ability to learn complex data distributions and …
essential tools in various fields due to their ability to learn complex data distributions and …
Gendds: Generating diverse driving video scenarios with prompt-to-video generative model
Autonomous driving training requires a diverse range of datasets encompassing various
traffic conditions, weather scenarios, and road types. Traditional data augmentation methods …
traffic conditions, weather scenarios, and road types. Traditional data augmentation methods …
A game-theoretic framework for generic second-order traffic flow models using mean field games and adversarial inverse reinforcement learning
A traffic system can be interpreted as a multiagent system, wherein vehicles choose the most
efficient driving approaches guided by interconnected goals or strategies. This paper aims to …
efficient driving approaches guided by interconnected goals or strategies. This paper aims to …
Badclm: Backdoor attack in clinical language models for electronic health records
The advent of clinical language models integrated into electronic health records (EHR) for
clinical decision support has marked a significant advancement, leveraging the depth of …
clinical decision support has marked a significant advancement, leveraging the depth of …