Naturalistic Driving Scenario Recognition with Multimodal Data
Author:
Affiliation:
1. School of Computer and Artificial Intelligence, Zhengzhou University,Zhengzhou,China,450001
2. School of Cyber Science and Engineering, Zhengzhou University,Zhengzhou,China,450001
Funder
China Postdoctoral Science Foundation
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9861039/9861093/09861106.pdf?arnumber=9861106
Reference26 articles.
1. Driver Sleepiness Classification Based on Physiological Data and Driving Performance From Real Road Driving
2. Deep unsupervised multi-modal fusion network for detecting driver distraction
3. Detection of driver stress in real-world driving environment using physiological signals
4. An Ensemble Classification Model With Unsupervised Representation Learning for Driving Stress Recognition Using Physiological Signals
5. Personalized Driver Workload Estimation Using Deep Neural Network Learning From Physiological and Vehicle Signals
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1. Multi-scale Data Fusion and Machine Learning for Vehicle Manoeuvre Classification;2023 IEEE 13th International Conference on System Engineering and Technology (ICSET);2023-10-02
2. Classification of Roads and Types of Public Roads Using EOG Smart Glasses and an Algorithm Based on Machine Learning While Driving a Car;Electronics;2022-09-18
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