Improving multi-agent trajectory prediction using traffic states on interactive driving scenarios
Predicting trajectories of multiple agents in interactive driving scenarios such as
intersections, and roundabouts are challenging due to the high density of agents, varying …
intersections, and roundabouts are challenging due to the high density of agents, varying …
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 …
Machine learning for autonomous vehicle's trajectory prediction: A comprehensive survey, challenges, and future research directions
V Bharilya, N Kumar - Vehicular Communications, 2024 - Elsevier
The significant contribution of human errors, accounting for approximately 94%(with a
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …
A survey on graph neural networks in intelligent transportation systems
Intelligent Transportation System (ITS) is vital in improving traffic congestion, reducing traffic
accidents, optimizing urban planning, etc. However, due to the complexity of the traffic …
accidents, optimizing urban planning, etc. However, due to the complexity of the traffic …
Hierarchical vector transformer vehicle trajectories prediction with diffusion convolutional neural networks
In dynamic and interactive autonomous driving scenarios, accurately predicting the future
movements of vehicle agents is crucial. However, current methods often fail to capture …
movements of vehicle agents is crucial. However, current methods often fail to capture …
A unified modeling framework for lane change intention recognition and vehicle status prediction
R Yuan, M Abdel-Aty, X Gu, O Zheng… - Physica A: Statistical …, 2023 - Elsevier
Accurately detecting and predicting Lane Change (LC) processes of human-driven vehicles
can help autonomous vehicles better understand their surrounding environment, recognize …
can help autonomous vehicles better understand their surrounding environment, recognize …
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 …
A Unified Approach to Lane Change Intention Recognition and Driving Status Prediction through TCN-LSTM and Multi-Task Learning Models
R Yuan, M Abdel-Aty, X Gu, O Zheng… - arXiv preprint arXiv …, 2023 - arxiv.org
Lane change (LC) is a continuous and complex operation process. Accurately detecting and
predicting LC processes can help traffic participants better understand their surrounding …
predicting LC processes can help traffic participants better understand their surrounding …
Cognitive accident prediction in driving scenes: A multimodality benchmark
Traffic accident prediction in driving videos aims to provide an early warning of the accident
occurrence, and supports the decision making of safe driving systems. Previous works …
occurrence, and supports the decision making of safe driving systems. Previous works …
STAG: A novel interaction-aware path prediction method based on Spatio-Temporal Attention Graphs for connected automated vehicles
MN Azadani, A Boukerche - Ad Hoc Networks, 2023 - Elsevier
Understanding social interactions between a vehicle and its surrounding agents enables
effective path prediction, which is critical for the motion planning and safe navigation of …
effective path prediction, which is critical for the motion planning and safe navigation of …