Real-Time Driver Cognitive Workload Recognition: Attention-Enabled Learning With Multimodal Information Fusion-Reference-Cited by-同舟云学术

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Real-Time Driver Cognitive Workload Recognition: Attention-Enabled Learning With Multimodal Information Fusion

Published:2024-05 Issue:5 Volume:71 Page:4999-5009
ISSN:0278-0046
Container-title:IEEE Transactions on Industrial Electronics
language:
Short-container-title:IEEE Trans. Ind. Electron.

Author:

Yang Haohan¹^ORCID,Wu Jingda¹^ORCID,Hu Zhongxu¹^ORCID,Lv Chen¹^ORCID

Affiliation:

1. School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore

Funder

Start-Up Grant

Nanyang Technological University

Agency for Science, Technology and Research

ANR-NRF joint

MTC Individual Research

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/41/10372144/10163920.pdf?arnumber=10163920

Reference38 articles.

1. Anticipating Human Activities Using Object Affordances for Reactive Robotic Response

2. Enhancing In-Vehicle Driving Assistance Information Under Connected Vehicle Environment

3. Dynamic State Estimation for the Advanced Brake System of Electric Vehicles by Using Deep Recurrent Neural Networks

4. In-Vehicle Infotainment Systems: Using Bayesian Networks to Model Cognitive Selection of Music Genres

5. Data-Driven Estimation of Driver Attention Using Calibration-Free Eye Gaze and Scene Features

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2. A robust operators’ cognitive workload recognition method based on denoising masked autoencoder;Knowledge-Based Systems;2024-10

3. EEG classification of resting state and arithmetic cognitive workload using functional connectivity of different frequency bands and machine learning techniques;Smart Science;2024-08-18

4. Single-pilot operations in commercial flight: Effects on neural activity and visual behaviour under abnormalities and emergencies;Chinese Journal of Aeronautics;2024-08

5. EEG-Based Driver Fatigue Detection Using Spatio-Temporal Fusion Network With Brain Region Partitioning Strategy;IEEE Transactions on Intelligent Transportation Systems;2024-08

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