Uncertainty Estimation of Pedestrian Future Trajectory Using Bayesian Approximation

Uncertainty Estimation of Pedestrian Future Trajectory Using Bayesian Approximation

Past research on pedestrian trajectory forecasting mainly focused on deterministic predictions which provide only point estimates of future states. These future estimates can help an autonomous vehicle plan its trajectory and avoid collision. However, under dynamic traffic scenarios, planning based...

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Bibliographic Details
Main Authors: Anshul Nayak, Azim Eskandarian, Zachary Doerzaph
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Open Journal of Intelligent Transportation Systems
Subjects:
Uncertainty quantification
Bayesian neural network
Monte Carlo dropout
long short term memory
convolutional neural network
Online Access:https://ieeexplore.ieee.org/document/9882968/

Internet

https://ieeexplore.ieee.org/document/9882968/

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