Graph Neural Network with RNNs based trajectory prediction of dynamic agents for autonomous vehicle

Singh, Divya; Srivastava, Rajeev

doi:10.1007/s10489-021-03120-9

Cited by 20 publications

(2 citation statements)

References 24 publications

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“…The spatialtemporal features are encoded-decoded via a GRU network in their framework. Singh and Rajeev [127] also employed a multi-scale GNN coupled with an LSTM-based encoderdecoder to fulfill the trajectory prediction task.…”

Section: Autonomous Vehiclesmentioning

confidence: 99%

Graph Neural Networks for Intelligent Transportation Systems: A Survey

Rahmani

Baghbani

Bouguila

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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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 broadly popular in intelligent transportation systems (ITS) applications as well. Despite their widespread applications in different transportation domains, there is no comprehensive review of recent advancements and future research directions that covers all transportation areas. Accordingly, in this survey, for the first time, we provide an overview of GNN studies in the general domain of ITS. Unlike previous surveys, which have been limited to traffic forecasting problems, we explore how GNN frameworks have evolved for different ITS applications, including traffic forecasting, demand prediction, autonomous vehicles, intersection management, parking management, urban planning, and transportation safety. Also, we micro-categorize the studies based on their transportation application to identify domainspecific research directions, opportunities, and challenges, which have been missing in previous surveys. Moreover, we identify unique and undiscussed research opportunities and directions, which is the result of reviewing a wide range of transportation applications. The neglected role of edge and graph learning in ITS applications, developing multi-modal models, and exploiting the power of unsupervised and reinforcement learning methods for developing more powerful GNNs are some examples of such new discussions in this survey. Finally, we have identified popular baseline models and datasets in each transportation domain, which facilitate the development and evaluation of future GNNbased frameworks.

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Section: Autonomous Vehiclesmentioning

confidence: 99%

Graph Neural Networks for Intelligent Transportation Systems: A Survey

Rahmani

Baghbani

Bouguila

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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“…Also, the assignment problem has been addressed by the majority of earlier methods using features that come from an underlying object detector, for example, by using nearest neighbors, clustering, or a case of non-negative matrix factorization, etc. In recent years, the use of GNN for prediction has gained more popularity [20]- [23]. Specifically for MOT, recent work [24]- [27] formulates data association as an edge classification task with GNNs, where each node denotes an object, and each edge relating to two nodes represents the similarity between detection and tracklets.…”

Section: Introductionmentioning

confidence: 99%

Learning to Track With Dynamic Message Passing Neural Network for Multi-Camera Multi-Object Tracking

Bilakeri,

Kotegar

2024

IEEE Access

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Multi-camera multi-object tracking (MC-MOT) has become pivotal in various real-world applications within computer vision. Despite extensive research, solving the data association problem remains one of the most formidable challenges in MC-MOT pipeline. This challenge is compounded by factors such as varying illumination, diverse walking patterns, and trajectory occlusions. In recent years, graph neural networks (GNNs) have emerged as promising tools for enhancing data association. However, prevalent graph-based MC-MOT methods often rely on computationally inefficient min-cost flow approaches for cross-camera association, with static graph structures that lack adaptability to new detections. Moreover, these methods typically process cameras in pairs, leading to localized solutions rather than a holistic global approach. To address these limitations, we propose a two-stage lightweight crosscamera tracker designed to achieve a global solution efficiently. Our approach prioritizes the quality of local tracklets, enhancing them through supervised learning on multi-source datasets using the DeepSort model. For multi-camera association, we leverage the dynamic connectivity of Message Passing Graph Neural Networks (MPGNNs) to jointly learn features and similarities previously untapped in this domain. Our proposed model significantly improves detection accuracy and feature extraction, outperforming current MC-MOT algorithms on cross-camera benchmarks. This advancement marks a notable step forward in the field, offering more precise tracking capabilities and demonstrating the potential of integrating state-of-theart techniques for enhanced performance in complex tracking scenarios.

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