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 …
Fjmp: Factorized joint multi-agent motion prediction over learned directed acyclic interaction graphs
Predicting the future motion of road agents is a critical task in an autonomous driving
pipeline. In this work, we address the problem of generating a set of scene-level, or joint …
pipeline. In this work, we address the problem of generating a set of scene-level, or joint …
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 …
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 …
VT-Former: An Exploratory Study on Vehicle Trajectory Prediction for Highway Surveillance through Graph Isomorphism and Transformer
Enhancing roadway safety has become an essential computer vision focus area for
Intelligent Transportation Systems (ITS). As a part of ITS Vehicle Trajectory Prediction (VTP) …
Intelligent Transportation Systems (ITS). As a part of ITS Vehicle Trajectory Prediction (VTP) …
Pishgu: Universal path prediction network architecture for real-time cyber-physical edge systems
G Alinezhad Noghre, V Katariya… - Proceedings of the …, 2023 - dl.acm.org
Path prediction is an essential task for many real-world Cyber-Physical Systems (CPS)
applications, from autonomous driving and traffic monitoring/management to …
applications, from autonomous driving and traffic monitoring/management to …
Optimization and interpretability of graph attention networks for small sparse graph structures in automotive applications
M Neumeier, A Tollkühn, S Dorn… - 2023 IEEE Intelligent …, 2023 - ieeexplore.ieee.org
For automotive applications, the Graph Attention Network (GAT) is a prominently used
architecture to include relational information of a traffic scenario during feature embedding …
architecture to include relational information of a traffic scenario during feature embedding …
Chaotic time series prediction based on multi-scale attention in a multi-agent environment
H Miao, W Zhu, Y Dan, N Yu - Chaos, Solitons & Fractals, 2024 - Elsevier
A new problem at the intersection of multi-agent systems, chaotic time series prediction, and
flow map learning is formulated in this paper. The problem involves agents collaborating to …
flow map learning is formulated in this paper. The problem involves agents collaborating to …
Multi Agent Navigation in Unconstrained Environments using a Centralized Attention based Graphical Neural Network Controller
Y Ma, Q Khan, D Cremers - 2023 IEEE 26th International …, 2023 - ieeexplore.ieee.org
In this work, we propose a learning based neural model that provides both the longitudinal
and lateral control commands to simultaneously navigate multiple vehicles. The goal is to …
and lateral control commands to simultaneously navigate multiple vehicles. The goal is to …
DERGCN: Dynamic-Evolving graph convolutional networks for human trajectory prediction
J Mi, X Zhang, H Zeng, L Wang - Neurocomputing, 2024 - Elsevier
Pedestrian trajectory prediction is an increasingly important research area in applied
autonomous driving and social robotics. Effectively modeling the intricate interactions …
autonomous driving and social robotics. Effectively modeling the intricate interactions …