A. Anund, C. Ahlström, C. Fors, and T. Åkerstedt, Are professional drivers less sleepy than non-professional drivers?, Scand. J. Work Environ. Health, vol.44, issue.1, pp.88-95, 2018.

C. Jacobé-de-naurois, Accident Analysis and Prevention, vol.121, pp.118-128, 2018.

J. T. Arnedt, G. J. Wilde, P. W. Munt, and A. W. Maclean, How do prolonged wakefulness and alcohol compare in the decrements they produce on a simulated driving task?, Accid. Anal. Prev, vol.33, issue.3, pp.337-344, 2001.

M. Awais, N. Badruddin, and M. Drieberg, A non-invasive approach to detect drowsiness in a monotonous driving environment, 2014.

M. Beale, M. T. Hagan, and H. B. Demuth, Neural network toolbox. Neural Network Toolbox, The Math Works 5, p.25, 1992.

J. Chen and Q. Ji, Drowsy driver posture, facial, and eye monitoring methods, pp.913-940, 2012.
DOI : 10.1007/978-0-85729-085-4_35

I. G. Daza, L. M. Bergasa, S. Bronte, J. J. Yebes, J. Almazán et al., Fusion of optimized indicators from Advanced Driver Assistance Systems (ADAS) for driver drowsiness detection, Sensors, vol.14, issue.1, pp.1106-1131, 2014.

E. De-valck, E. De-groot, and R. Cluydts, Effects of slow-release caffeine and a nap on driving simulator performance after partial sleep deprivation, Percept. Mot. Skills, vol.96, issue.1, pp.67-78, 2003.

Y. Dong, Z. Hu, K. Uchimura, and N. Murayama, Driver inattention monitoring system for intelligent vehicles: a review, IEEE Trans. Intell. Transp. Syst, vol.12, issue.2, pp.596-614, 2011.
DOI : 10.1109/ivs.2009.5164395

C. Fors, C. Ahlstrom, and A. Anund, A comparison of driver sleepiness in the simulator and on the real road, J. Transp. Saf. Secur, vol.10, issue.1-2, pp.72-87, 2018.

J. F. Golding, Motion sickness susceptibility questionnaire revised and its relationship to other forms of sickness, Brain Res. Bull, vol.47, issue.5, pp.507-516, 1998.

J. A. Horne and O. Ostberg, A self-assessment questionnaire to determine morningness-eveningness in human circadian rhythms, Int. J. Chronobiol, vol.4, issue.2, pp.97-110, 1975.

J. Horne and L. Reyner, Vehicle accidents related to sleep: a review, Occup. Environ. Med, vol.56, issue.5, pp.289-294, 1999.
DOI : 10.1136/oem.56.5.289
URL : https://oem.bmj.com/content/56/5/289.full.pdf

M. Ingre, T. Åkerstedt, B. Peters, A. Anund, and G. Kecklund, Subjective sleepiness, simulated driving performance and blink duration: examining individual differences, J. Sleep Res, vol.15, issue.1, pp.47-53, 2006.

C. Jacobé-de-naurois, C. Bourdin, A. Stratulat, E. Diaz, and J. Vercher, Detection and prediction of driver drowsiness using artificial neural network models, Accid. Anal. Prev, 2017.

M. W. Johns, A new method for measuring daytime sleepiness: the Epworth sleepiness scale, Sleep, vol.14, issue.6, pp.540-545, 1991.

K. Karrer, T. Vöhringer-kuhnt, T. Baumgarten, and S. Briest, The role of individual differences in driver fatigue prediction, Third International Conference on Traffic and Transport Psychology 5-9 Citeseer, 2004.

K. Levenberg, A method for the solution of certain non-linear problems in least squares, Q. Appl. Math, vol.2, issue.2, pp.164-168, 1944.

C. C. Liu, S. G. Hosking, and M. G. Lenné, Predicting driver drowsiness using vehicle measures: recent insights and future challenges, J. Safety Res, vol.40, issue.4, pp.239-245, 2009.
DOI : 10.1016/j.jsr.2009.04.005

E. Nilsson, C. Ahlström, S. Barua, C. Fors, P. Lindén et al., Vehicle Driver Monitoring: Sleepiness and Cognitive Load, 2017.

P. Philip, J. Taillard, P. Sagaspe, C. Valtat, M. Sanchez-ortuno et al., Age, performance and sleep deprivation, J. Sleep Res, vol.13, issue.2, pp.105-110, 2004.

P. Philip, P. Sagaspe, J. Taillard, C. Valtat, N. Moore et al., Fatigue, sleepiness, and performance in simulated versus real driving conditions, Sleep, vol.28, issue.12, p.1511, 2005.
DOI : 10.1093/sleep/28.12.1511
URL : https://academic.oup.com/sleep/article-pdf/28/12/1511/13662669/sleep-28-12-1511.pdf

R. Rossi, M. Gastaldi, and G. Gecchele, Analysis of driver task-related fatigue using driving simulator experiments, Proc. Soc. Behav. Sci, vol.20, pp.666-675, 2011.
DOI : 10.1016/j.sbspro.2011.08.074
URL : https://doi.org/10.1016/j.sbspro.2011.08.074

M. Rost, E. Zilberg, Z. M. Xu, Y. Feng, D. Burton et al., Comparing contribution of algorithm based physiological indicators for characterisation of driver drowsiness, J. Med. Bioeng, vol.4, issue.5, 2015.

S. Samiee, S. Azadi, R. Kazemi, A. Nahvi, and A. Eichberger, Data fusion to develop a driver drowsiness detection system with robustness to signal loss, Sensors, vol.14, issue.9, 2014.
DOI : 10.3390/s140917832
URL : http://www.mdpi.com/1424-8220/14/9/17832/pdf

P. Thiffault and J. Bergeron, Monotony of road environment and driver fatigue: a simulator study, Accid. Anal. Prev, vol.35, issue.3, pp.381-391, 2003.
DOI : 10.1016/s0001-4575(02)00014-3

H. P. Van-dongen, N. L. Rogers, and D. F. Dinges, Sleep debt: theoretical and empirical issues, Sleep Biol. Rhythms, vol.1, issue.1, pp.5-13, 2003.

H. P. Van-dongen, M. D. Baynard, G. Maislin, and D. F. Dinges, Systematic interindividual differences in neurobehavioral impairment from sleep loss: evidence of trait-like differential vulnerability, Sleep, vol.27, issue.3, pp.423-433, 2004.

H. P. Van-dongen, G. Maislin, and D. F. Dinges, Dealing with inter-individual differences in the temporal dynamics of fatigue and performance: importance and techniques, Aviat. Space Environ. Med, vol.75, issue.3, pp.147-154, 2004.

P. Wang, J. Lu, B. Zhang, and Z. Tang, A review on transfer learning for braincomputer interface classification, 5th International Conference on Information Science and Technology (ICIST), pp.315-322, 2015.
DOI : 10.1109/icist.2015.7288989

A. Watson and G. Zhou, Microsleep prediction using an EKG capable heart rate monitor, IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), pp.328-329, 2016.
DOI : 10.1109/chase.2016.30

C. Wei, Y. Lin, Y. Wang, T. Jung, N. Bigdely-shamlo et al., Selective transfer learning for EEG-based drowsiness detection, IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp.3229-3232, 2015.
DOI : 10.1109/smc.2015.560

W. W. Wierwille and L. A. Ellsworth, Evaluation of driver drowsiness by trained raters, Accid. Anal. Prev, vol.26, issue.5, pp.571-581, 1994.
DOI : 10.1016/0001-4575(94)90019-1

D. Wu, C. Chuang, and C. Lin, Online driver's drowsiness estimation using domain adaptation with model fusion, International Conference on Affective Computing and Intelligent Interaction (ACII), pp.904-910, 2015.
DOI : 10.1109/acii.2015.7344682

D. Wu, V. J. Lawhern, S. Gordon, B. J. Lance, and C. Lin, Offline EEG-based driver drowsiness estimation using enhanced batch-mode active learning (EBMAL) for regression, Proc. IEEE Int'L Conf. on Systems, Man and Cybernetics, 2016.
DOI : 10.1109/smc.2016.7844328
URL : https://opus.lib.uts.edu.au/bitstream/10453/113460/4/2.pdf

J. Zhang, Y. Wang, and S. Li, Cross-subject mental workload classification using kernel spectral regression and transfer learning techniques, Cogn. Technol. Work, pp.1-19, 2017.
DOI : 10.1007/s10111-017-0425-3