相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。A subjective one-item measure based on NASA-TLX to assess cognitive workload in driver-vehicle interaction
Nikolai von Janczewski et al.
TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR (2022)
Distracted driving recognition method based on deep convolutional neural network
Xuli Rao et al.
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING (2021)
Feature selection using Fisher score and multilabel neighborhood rough sets for multilabel classification
Lin Sun et al.
INFORMATION SCIENCES (2021)
Influence of built environment on the severity of vehicle crashes caused by distracted driving: A multi-state comparison
Youngbin Lym et al.
ACCIDENT ANALYSIS AND PREVENTION (2021)
Factors affecting behavior of mobile phone use while driving and effect of mobile phone use on driving performance
Natakorn Phuksuksakul et al.
ACCIDENT ANALYSIS AND PREVENTION (2021)
Classification of Driver Distraction: A Comprehensive Analysis of Feature Generation, Machine Learning, and Input Measures
Anthony D. McDonald et al.
HUMAN FACTORS (2020)
A Novel Classification Method for a Driver's Cognitive Stress Level by Transferring Interbeat Intervals of the ECG Signal to Pictures
Jing Huang et al.
SENSORS (2020)
Towards Intelligent Data Analytics: A Case Study in Driver Cognitive Load Classification
Shaibal Barua et al.
BRAIN SCIENCES (2020)
Study on the driving style adaptive vehicle longitudinal control strategy
Jing Huang et al.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA (2020)
Review of noise removal techniques in ECG signals
Shubhojeet Chatterjee et al.
IET SIGNAL PROCESSING (2020)
Distracted driver detection by combining in-vehicle and image data using deep learning
Furkan Omerustaoglu et al.
APPLIED SOFT COMPUTING (2020)
HCF: A Hybrid CNN Framework for Behavior Detection of Distracted Drivers
Chen Huang et al.
IEEE ACCESS (2020)
The injury epidemiology of adult riders in vehicle-two-wheeler crashes in China, Ningbo, 2011-2015
Lin Hu et al.
JOURNAL OF SAFETY RESEARCH (2020)
Drowsiness and distraction while driving: A study based on smartphone app data
Sonia Soares et al.
JOURNAL OF SAFETY RESEARCH (2020)
Detection and Evaluation of Driver Distraction Using Machine Learning and Fuzzy Logic
Andrei Aksjonov et al.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2019)
Driver Distraction Identification with an Ensemble of Convolutional Neural Networks
Hesham M. Eraqi et al.
JOURNAL OF ADVANCED TRANSPORTATION (2019)
Removal of Artifacts from EEG Signals: A Review
Xiao Jiang et al.
SENSORS (2019)
A Three-Class Classification of Cognitive Workload Based on EEG Spectral Data
Malgorzata Plechawska-Wojcik et al.
APPLIED SCIENCES-BASEL (2019)
Comparison among driving state prediction models for car-following condition based on EEG and driving features
Liu Yang et al.
ACCIDENT ANALYSIS AND PREVENTION (2019)
A data-driven framework for learners' cognitive load detection using ECG-PPG physiological feature fusion and XGBoost classification
Chixiang Wang et al.
2018 INTERNATIONAL CONFERENCE ON IDENTIFICATION, INFORMATION AND KNOWLEDGE IN THE INTERNET OF THINGS (2019)
Analyzing the effect of fog weather conditions on driver lane-keeping performance using the SHRP2 naturalistic driving study data
Anik Das et al.
JOURNAL OF SAFETY RESEARCH (2019)
Measuring mental workload with the NASA-TLX needs to examine each dimension rather than relying on the global score: an example with driving
Edith Galy et al.
ERGONOMICS (2018)
Electrocardiographic features for the measurement of drivers' mental workload
Tobias Heine et al.
APPLIED ERGONOMICS (2017)
Deep in thought while driving: An EEG study on drivers' cognitive distraction
Hossam Almahasneh et al.
TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR (2014)