Pedestrian potentially dangerous behaviour prediction based on attention‐long‐short‐term memory with egocentric vision

Pedestrian potentially dangerous behaviour prediction based on attention‐long‐short‐term memory with egocentric vision

Abstract This paper develops a pedestrian potentially dangerous behaviour prediction method based on attention‐long‐short‐term memory (Attention‐LSTM) architecture to predict pedestrian trajectory and intention for the unexpected pedestrian crossing accident avoidance. To extract the road scene info...

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Bibliographic Details
Main Authors: Ming‐Chih Lin, Yu‐Chen Lin, Ming‐Ku Hung
Format: Article
Language:English
Published: Wiley 2023-07-01
Series:IET Intelligent Transport Systems
Subjects:
advanced driver assistance systems
artificial intelligence
image recognition
perception
Online Access:https://doi.org/10.1049/itr2.12326

Internet

https://doi.org/10.1049/itr2.12326

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