Genai-powered multi-agent paradigm for smart urban mobility: Opportunities and challenges for integrating large language models (llms) and retrieval-augmented …

H Xu, J Yuan, A Zhou, G Xu, W Li, X Ban… - arxiv preprint arxiv …, 2024 - arxiv.org
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 …

Graph neural networks for intelligent transportation systems: A survey

S Rahmani, A Baghbani, N Bouguila… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
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 …

Use of social interaction and intention to improve motion prediction within automated vehicle framework: A review

DE Benrachou, S Glaser, M Elhenawy… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
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 …

Traj-llm: A new exploration for empowering trajectory prediction with pre-trained large language models

Z Lan, L Liu, B Fan, Y Lv, Y Ren… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Predicting the future trajectories of dynamic traffic actors is a cornerstone task in
autonomous driving. Though existing notable efforts have resulted in impressive …

Difftad: Denoising diffusion probabilistic models for vehicle trajectory anomaly detection

C Li, G Feng, Y Li, R Liu, Q Miao, L Chang - Knowledge-Based Systems, 2024 - Elsevier
Vehicle trajectory anomaly detection plays an essential role in the fields of traffic video
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

C Wei, G Wu, MJ Barth, A Abdelraouf… - Proceedings of the …, 2024 - openaccess.thecvf.com
Reliable prediction of vehicle trajectories at signalized intersections is crucial to urban traffic
management and autonomous driving systems. However it presents unique challenges due …

MTP-GO: Graph-based probabilistic multi-agent trajectory prediction with neural ODEs

T Westny, J Oskarsson, B Olofsson… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Enabling resilient autonomous motion planning requires robust predictions of surrounding
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 …

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 …

Trajectory distribution aware graph convolutional network for trajectory prediction considering spatio-temporal interactions and scene information

R Wang, Z Hu, X Song, W Li - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Pedestrian trajectory prediction has been broadly applied in video surveillance and
autonomous driving. Most of the current trajectory prediction approaches are committed to …