Pedestrian Trajectory Forecasting Using Deep Ensembles Under Sensing Uncertainty-Reference-Cited by-同舟云学术

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Pedestrian Trajectory Forecasting Using Deep Ensembles Under Sensing Uncertainty

Published:2024-09 Issue:9 Volume:25 Page:11317-11329
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Nayak Anshul¹^ORCID,Eskandarian Azim²^ORCID,Doerzaph Zachary³^ORCID,Ghorai Prasenjit¹^ORCID

Affiliation:

1. Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA, USA

2. VCU College of Engineering, Richmond, VA, USA

3. Virginia Tech Transportation Institute (VTTI) and the Department of Biomedical Engineering, Virginia Tech, Blacksburg, VA, USA

Funder

Safety Through Disruption (Safe-D) National University Transportation Center

U.S. Department of Transportation—Office of the Assistant Secretary for Research and Technology, University Transportation Centers Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6979/10659279/10495763.pdf?arnumber=10495763

Reference38 articles.

1. Vehicle trajectory prediction based on motion model and maneuver recognition

2. Uncertainty-aware reinforcement learning for collision avoidance;Kahn;arXiv:1702.01182,2017

3. Motion Planning of Autonomous Vehicles under Dynamic Traffic Environment in Intersections Using Probabilistic Rapidly Exploring Random Tree

4. Uncertainty Estimation of Pedestrian Future Trajectory Using Bayesian Approximation

5. Conditional Generative Neural System for Probabilistic Trajectory Prediction

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1. Modelling the unidirectional and bidirectional flow of pedestrians based on convolutional neural networks;Physica A: Statistical Mechanics and its Applications;2024-10

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