Clustering of Drivers' State Before Takeover Situations Based on Physiological Features Using Unsupervised Machine Learning

[1]  A. Sonderegger,et al.  Classification of Drivers' Workload Using Physiological Signals in Conditional Automation , 2021, Frontiers in Psychology.

[2]  Yoonjin Yoon,et al.  Modeling Individual Differences in Driver Workload Inference Using Physiological Data , 2021 .

[3]  Jan C. Brammer,et al.  NeuroKit2: A Python toolbox for neurophysiological signal processing , 2020, Behavior Research Methods.

[4]  Domen Novak,et al.  Identifying the Causes of Drivers’ Hazardous States Using Driver Characteristics, Vehicle Kinematics, and Physiological Measurements , 2018, Front. Neurosci..

[5]  Lanlan Chen,et al.  Detecting driving stress in physiological signals based on multimodal feature analysis and kernel classifiers , 2017, Expert Syst. Appl..

[6]  Micheal Drieberg,et al.  A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability , 2017, Sensors.

[7]  Nanxiang Li,et al.  Driver behavior event detection for manual annotation by clustering of the driver physiological signals , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

[8]  Kathrin Zeeb,et al.  What determines the take-over time? An integrated model approach of driver take-over after automated driving. , 2015, Accident; analysis and prevention.

[9]  C. Giraud Introduction to High-Dimensional Statistics , 2014 .

[10]  Bernd Johannes,et al.  Author's Personal Copy Biological Psychology a Methodology to Compensate for Individual Differences in Psychophysiological Assessment , 2022 .

[11]  Natasha Merat,et al.  Highly Automated Driving, Secondary Task Performance, and Driver State , 2012, Hum. Factors.

[12]  Jennifer Healey,et al.  Detecting stress during real-world driving tasks using physiological sensors , 2005, IEEE Transactions on Intelligent Transportation Systems.