Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-based Approaches

11 November 2021

Papers citing "Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-based Approaches"

8 / 8 papers shown

Title
AToM: Adaptive Theory-of-Mind-Based Human Motion Prediction in Long-Term Human-Robot Interactions Yuwen Liao Muqing Cao Xinhang Xu Lihua Xie 41 0 0 09 Feb 2025
Snapshot: Towards Application-centered Models for Pedestrian Trajectory Prediction in Urban Traffic Environments Nico Uhlemann Yipeng Zhou Tobias Mohr Markus Lienkamp 59 1 0 10 Jan 2025
SFMGNet: A Physics-based Neural Network To Predict Pedestrian Trajectories Sakif Hossain Fatema-Tuj-Johora J. P. Müller Sven Hartmann Andreas Reinhardt AI4CE 12 6 0 06 Feb 2022
Deep Social Force S. Kreiss 29 15 0 24 Sep 2021
A generic and density-sensitive method for multi-scale pedestrian dynamics Daniel H. Biedermann J. Clever A. Borrmann 13 8 0 11 Dec 2020
Transformer Networks for Trajectory Forecasting Francesco Giuliari Irtiza Hasan Marco Cristani Fabio Galasso 109 360 0 18 Mar 2020
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems J. Willard X. Jia Shaoming Xu M. Steinbach Vipin Kumar AI4CE 80 385 0 10 Mar 2020
Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction Ajay Jain Sergio Casas Renjie Liao Yuwen Xiong Song Feng Sean Segal R. Urtasun 138 73 0 17 Oct 2019