Group-Aware Human Trajectory Prediction

Sethi, Ishneet; Trivedi, Alka Ben; Singhal, Pranav Mohan; Bhave, Mrinal; Agarwal, Rekha; Kala, Rahul; Nandi, Gora Chand

doi:10.1109/cict56698.2022.9997980

Cited by 1 publication

(1 citation statement)

References 26 publications

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“…The deep learning network TrajNet++ by Sethi et al [63] effectively simulates group interactions within high-density settings, thereby facilitating social navigation for robots. The model is able to learn a variety of social norms used by humans when walking in large groups.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review

Belter,

Hering,

Weichbroth

2023

Applied Sciences

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Background: In the context of Warehouse Management Systems, knowledge related to motion trajectory prediction methods utilizing machine learning techniques seems to be scattered and fragmented. Objective: This study seeks to fill this research gap by using a systematic literature review approach. Methods: Based on the data collected from Google Scholar, a systematic literature review was performed, covering the period from 2016 to 2023. The review was driven by a protocol that comprehends inclusion and exclusion criteria to identify relevant papers. Results: Considering the Warehouse Management Systems, five categories of motion trajectory prediction methods have been identified: Deep Learning methods, probabilistic methods, methods for solving the Travelling-Salesman problem (TSP), algorithmic methods, and others. Specifically, the performed analysis also provides the research community with an overview of the state-of-the-art methods, which can further stimulate researchers and practitioners to enhance existing and develop new ones in this field.

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Section: Deep Learning Methodsmentioning

confidence: 99%