## Abbreviations  
The following abbreviations are used in this manuscript: \begin{tabular}{l l} AUC & area under curve \\ BPM & beats per minute \\ BR & breathing rate \\ BVP & blood volume pulse \\ CNN & convolutional neural network \\ DT & decision tree \\ FN & false negatives \\ FP & false positives \\ LSTM & long short-term memory \\ ECG & electrocardiogram \\ EEG & electroencephalogram \\ EMG & electromyogram \\ GSR & galvanic skin response \\ IBI & inter-beat intervals \\ KNN & K nearest neighbors \\ RF & random forest \\ ROC & receiver operating characteristic \\ SVM & support vector machine \\ TN & true negatives \\ TP & true positives \\ WHO & World Health Organization \\ \end{tabular}   
* [PERSON] et al. (2007) [PERSON]; [PERSON]; [PERSON]; [PERSON] Driver distraction detection with a camera vision system. In Proceedings of the 2007 IEEE International Conference on Image Processing, San Antonio, TX, USA, 16-19 September 2007; IEEE: Piscataway, NJ, USA, 2007; Volume 6, p. VI-201. * [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] All In One Network for Driver Attention Monitoring. In Proceedings of the ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 4-8 May 2020; IEEE: Piscataway, NJ, USA, 2020; pp. 2258-2262. * [PERSON] et al. (2007) [PERSON]; [PERSON]; [PERSON]; [PERSON] An on-road assessment of cognitive distraction: Impacts on drivers' visual behavior and braking performance. _Acid. Anal. Prev._**2007**, _39_, 372-379. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON] [PERSON] Classification of Driving Behaviour using Short-term and Long-term Summaries of Sensor Data. In Proceedings of the 2020 5 th Fourth-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), Corfu, Greece, 25-27 September 2020; IEEE: Piscataway, NJ, USA, 2020; pp. 1-4. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON] Personalized Driver Workload Estimation Using Deep Neural Network Learning from Physiological and Vehicle Signals. _IEEE Trans. Intell. Veh._**2019**. [CrossRef]
* [PERSON] and [PERSON] (2005) [PERSON]; [PERSON] Detecting stress during real-world driving tasks using physiological sensors. _IEEE Trans. Intell. Transp. Syst._**2005**, \(6\), 156-166. [CrossRef]
* [PERSON] et al. (2013) [PERSON]; [PERSON]; [PERSON] A comparative evaluation of neural network classifiers for stress level analysis of automotive drivers using physiological signals. _Biomed. Signal Process. Control_**2013**, \(8\), 740-754. [CrossRef]
* [PERSON] and [PERSON] (2020) [PERSON]; [PERSON] An Ensemble Classification Model With Unsupervised Representation Learning for Driving Stress Recognition Using Physiological Signals. _IEEE Trans. Intell. Transp. Syst._**2020**. [CrossRef]
* [PERSON] and [PERSON] (2009) [PERSON]; [PERSON] Individual differences in stress and fatigue in two field studies of driving. _Transp. Res. Part F Traffic Psychol. Behav._**2009**, _12_, 265-276. [CrossRef]
* [PERSON] [PERSON] (2010) [PERSON]; [PERSON] Monitoring drivers' mental workload in driving simulators using physiological measures. _Accid. Anal. Prev._**2010**, _42_, 898-903. [CrossRef]
* [PERSON] and [PERSON] (2011) [PERSON]; [PERSON] The impact of cognitive workload on physiological arousal in young adult drivers: A field study and simulation validation. _Ergonomics_**2011**, _54_, 932-942. [CrossRef]
* [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Cognitive load measurement in a virtual reality-based driving system for autism intervention. _IEEE Trans. Affect. Comput._**2017**, \(8\), 176-189. [CrossRef] [PubMed]
* [PERSON] et al. (2017) [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] Detecting users' cognitive load by galvanic skin response with affective interference. _ACM Trans. Interact. Intell. Syst. TiS_**2017**, \(7\), 1-20. [CrossRef]
* [PERSON] et al.