Using meta-learning to establish a highly transferable driving speed prediction model from the visual road environment-Reference-Cited by-同舟云学术

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Using meta-learning to establish a highly transferable driving speed prediction model from the visual road environment

Published:2024-04 Issue: Volume:130 Page:107727
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Yu Bo,Feng Xiangyu,Kong You^ORCID,Chen Yuren,Cheng Zeyang,Bao Shan

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference46 articles.

1. Few-Shot learning for picture classification of common flora;Ball;ArXiv,2021

2. Meta-graph: Few Shot Link Prediction via Meta-Learning;Bose,2019

3. Driver-pedestrian interaction under different road environments;Bella;Transport. Res. Procedia,2017

4. A random forest guided tour;Biau;Test,2016

5. Interval type-3 fuzzy aggregators for ensembles of neural networks in COVID-19 time series prediction;Castillo;Eng. Appl. Artif. Intell.,2022

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1. An ADAS with better driver satisfaction under rear-end near-crash scenarios: A spatio-temporal graph transformer-based prediction framework of evasive behavior and collision risk;Transportation Research Part C: Emerging Technologies;2024-02

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