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Genai-powered multi-agent paradigm for smart urban mobility: Opportunities and challenges for integrating large language models (llms) and retrieval-augmented …
Leveraging recent advances in generative AI, multi-agent systems are increasingly being
developed to enhance the functionality and efficiency of smart city applications. This paper …
developed to enhance the functionality and efficiency of smart city applications. This paper …
Graph neural networks for intelligent transportation systems: A survey
Graph neural networks (GNNs) have been extensively used in a wide variety of domains in
recent years. Owing to their power in analyzing graph-structured data, they have become …
recent years. Owing to their power in analyzing graph-structured data, they have become …
Use of social interaction and intention to improve motion prediction within automated vehicle framework: A review
Human errors contribute to 94%(±2.2%) of road crashes resulting in fatal/non-fatal
causalities, vehicle damages and a predicament in the pathway to safer road systems …
causalities, vehicle damages and a predicament in the pathway to safer road systems …
Traj-llm: A new exploration for empowering trajectory prediction with pre-trained large language models
Predicting the future trajectories of dynamic traffic actors is a cornerstone task in
autonomous driving. Though existing notable efforts have resulted in impressive …
autonomous driving. Though existing notable efforts have resulted in impressive …
Difftad: Denoising diffusion probabilistic models for vehicle trajectory anomaly detection
Vehicle trajectory anomaly detection plays an essential role in the fields of traffic video
surveillance, autonomous driving navigation, and taxi fraud detection. Deep generative …
surveillance, autonomous driving navigation, and taxi fraud detection. Deep generative …
KI-GAN: Knowledge-Informed Generative Adversarial Networks for Enhanced Multi-Vehicle Trajectory Forecasting at Signalized Intersections
Reliable prediction of vehicle trajectories at signalized intersections is crucial to urban traffic
management and autonomous driving systems. However it presents unique challenges due …
management and autonomous driving systems. However it presents unique challenges due …
MTP-GO: Graph-based probabilistic multi-agent trajectory prediction with neural ODEs
Enabling resilient autonomous motion planning requires robust predictions of surrounding
road users' future behavior. In response to this need and the associated challenges, we …
road users' future behavior. In response to this need and the associated challenges, we …
Incorporating driving knowledge in deep learning based vehicle trajectory prediction: A survey
Z Ding, H Zhao - IEEE Transactions on Intelligent Vehicles, 2023 - ieeexplore.ieee.org
Vehicle Trajectory Prediction (VTP) is one of the key issues in the field of autonomous
driving. In recent years, more researchers have tried applying Deep Learning methods and …
driving. In recent years, more researchers have tried applying Deep Learning methods and …
AMGB: Trajectory prediction using attention-based mechanism GCN-BiLSTM in IOV
R Li, Y Qin, J Wang, H Wang - Pattern Recognition Letters, 2023 - Elsevier
Accurate and reliable prediction of vehicle trajectories is closely related to the path planning
of intelligent vehicles and contributes to intelligent transportation safety, especially in …
of intelligent vehicles and contributes to intelligent transportation safety, especially in …
Trajectory distribution aware graph convolutional network for trajectory prediction considering spatio-temporal interactions and scene information
Pedestrian trajectory prediction has been broadly applied in video surveillance and
autonomous driving. Most of the current trajectory prediction approaches are committed to …
autonomous driving. Most of the current trajectory prediction approaches are committed to …