References
- [1] World Health Organization, Global status report on road safety 2015, World Health Organization, Tech. Rep., 2015.
- [2] S. Singh, Critical Reasons for Crashes Investigated in the National Motor Vehicle Crash Causation Survey, National Highway Traffic Safety Administration, Washington, DC, Tech. Rep., 2015.
- [3] C. Craye, A. Rashwan, M. S. Kamel, and F. Karray, A Multi-Modal Driver Fatigue and Distraction Assessment System, International Journal of Intelligent Transportation Systems Research, vol. 14, no. 3, pp. 173–194, Sept. 2016.10.1007/s13177-015-0112-9
- [4] G. Turan and S. Gupta, Road Accidents Prevention system using Driver’s Drowsiness Detection, International Journal of Advanced Research in Computer Engineering & Technology, vol. 2, no. 11, Nov. 2013.
- [5] C. Braunagel, E. Kasneci, W. Stolzmann, and W. Rosenstiel, Driver-Activity Recognition in the Context of Conditionally Autonomous Driving, in 2015 IEEE 18th International Conference on Intelligent Transportation Systems, Sept. 2015, pp. 1652–1657.
- [6] SAE International, Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems, SAE International, Tech. Rep., 2016.
- [7] M. Cunningham and M. Regan, Autonomous Vehicles: Human Factors Issues and Future Research, in Proceedings of the 2015 Australasian Road Safety Conference, Gold Coast, 2015.
- [8] N. Merat, A. H. Jamson, F. C. H. Lai, and O. Carsten, Highly Automated Driving, Secondary Task Performance, and Driver State, Human Factors, vol. 54, no. 5, pp. 762–771, 2012.
- [9] J. Radlmayr, C. Gold, L. Lorenz, M. Farid, and K. Bengler, How Traffic Situations and Non-Driving Related Tasks Affect the Take-Over Quality in Highly Automated Driving, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 58, no. 1, pp. 2063–2067, Oct. 2014.
- [10] N. Schömig, V. Hargutt, A. Neukum, I. Petermann-Stock, and I. Othersen, The Interaction Between Highly Automated Driving and the Development of Drowsiness, Procedia Manufacturing, vol. 3, pp. 6652–6659, 2015.
- [11] H. Rahman, S. Begum, and M. U. Ahmed, Driver monitoring in the context of autonomous vehicle, in Frontiers in Artificial Intelligence and Applications, vol. 278. Mälardalen University, Embedded Systems, 2015, pp. 108–117.
- [12] A. Amditis, L. Andreone, K. Pagle, G. Markkula, E. Deregibus, M. Romera Rue, F. Bellotti, A. Engelsberg, R. Brouwer, B. Peters, and A. De Gloria, Towards the Automotive HMI of the Future: Overview of the AIDE-Integrated Project Results, IEEE Transactions on Intelligent Transportation Systems, vol. 11, no. 3, pp. 567–578, Sept. 2010.
- [13] Q. He, W. Li, X. Fan, and Z. Fei, Evaluation of driver fatigue with multi-indicators based on artificial neural network, IET Intelligent Transport Systems, vol. 10, no. 8, pp. 555–561, 2016.
- [14] J. Jo, S. J. Lee, H. G. Jung, K. R. Park, and J. Kim, Vision-based method for detecting driver drowsiness and distraction in driver monitoring system, Optical Engineering, vol. 50, no. 12, pp. 127 202–1–127 202–24, 2011.
- [15] T.-H. Chang and Y.-R. Chen, Driver fatigue surveillance via eye detection, in 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), Oct. 2014, pp. 366–371.
- [16] O. Gusikhin, N. Rychtyckyj, and D. Filev, Intelligent systems in the automotive industry: applications and trends, Knowledge and Information Systems, vol. 12, no. 2, pp. 147–168, July 2007.
- [17] G. Lugano, Virtual assistants and self-driving cars, in 2017 15th International Conference on ITS Telecommunications (ITST), May 2017, pp. 1–5.
- [18] S. Maralappanavar, R. Behera, and U. Mudenagudi, Driver’s distraction detection based on gaze estimation, in 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Sept. 2016, pp. 2489–2494.
- [19] Erie Insurance, Erie Insurance releases police data on top 10 driving distractions involved in fatal car crashes, Erie Insurance Group, Tech. Rep., Apr. 2013.
- [20] J. F. May and C. L. Baldwin, Driver fatigue: The importance of identifying causal factors of fatigue when considering detection and countermeasure technologies, Transportation Research Part F: Traffic Psychology and Behaviour, vol. 12, no. 3, pp. 218–224, 2009.10.1016/j.trf.2008.11.005
- [21] M. Körber, A. Cingel, M. Zimmermann, and K. Bengler, Vigilance Decrement and Passive Fatigue Caused by Monotony in Automated Driving, Procedia Manufacturing, vol. 3, pp. 2403–2409, 2015.
- [22] B. Pfleging, M. Rang, and N. Broy, Investigating User Needs for Non-driving-related Activities During Automated Driving, in Proceedings of the 15th International Conference on Mobile and Ubiquitous Multimedia, ser. MUM ’16. Rovaniemi, Finland: ACM, 2016, pp. 91–99.
- [23] R. Spies, M. Ablaßmeier, H. Bubb, and W. Hamberger, Augmented Interaction and Visualization in the Automotive Domain, in Human-Computer Interaction. Ambient, Ubiquitous and Intelligent Interaction, J. A. Jacko, Ed. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009, pp. 211–220.
- [24] B. Pfleging and A. Schmidt, (Non-) Driving-Related Activities in the Car: Defining Driver Activities for Manual and Automated Driving, in Workshop on Experiencing Autonomous Vehicles: Crossing the Boundaries between a Drive and a Ride at CHI ’15, 2015.
- [25] A. Bulling, J. A. Ward, H. Gellersen, and G. Troster, Eye Movement Analysis for Activity Recognition Using Electrooculography, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 4, pp. 741–753, 2011.10.1109/TPAMI.2010.86
- [26] S. Chen and J. Epps, Automatic classification of eye activity for cognitive load measurement with emotion interference, Computer Methods and Programs in Biomedicine, vol. 110, no. 2, pp. 111–124, 2013.10.1016/j.cmpb.2012.10.021
- [27] C. Helmchen and H. Rambold, The Eyelid and Its Contribution to Eye Movements, Developments in Ophthalmology, vol. 40, pp. 110–131, 2007.
- [28] Smart Eye AB. SE PRO — Smart Eye. (2018, Sept. 29). [Online]. Available: http://smarteye.se/research-instruments/se-pro/
- [29] Flat Earth, Inc. Ancho Radar Development Kit - LTSA mini 5-11 GHz – Flat Earth Online Store. (2018, Oct. 9). [Online]. Available: https://store.flatearthinc.com/products/ancho-radar-development-kit-ltsa-mini-5-11-ghz
- [30] Ergoneers GmbH, Sim Lab driving simulator — ERGONEERS, (2018, Oct. 29). [Online]. Available: https://www.ergoneers.com/en/hardware/simlab-driving-simulator/
- [31] K. Kircher and C. Ahlström, Issues related to the driver distraction detection algorithm AttenD, in 1st International Conference on Driver Distraction and Inattention (DDI 2009), Gothenburg, Sweden, Sept. 2009.
- [32] Daimler AG, Driver Assistance Systems — Technology Guide, Daimler AG, Stuttgart, Germany, Tech. Rep., 2013.
- [33] D. Stawarczyk and A. D’Argembeau, Conjoint Influence of Mind-Wandering and Sleepiness on Task Performance, Journal of Experimental Psychology: Human Perception and Performance, vol. 42, no. 10, pp. 1587–1600, 2016.
- [34] R. Nowosielski and L. M. Trick, How Common In-Car Distractions Affect Driving Performance in Simple and Complex Road Environments, in Proceedings of the 9th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design. University of Iowa, Nov. 2017, pp. 249–255.
- [35] R. Chen, X. Wang, L. Zhang, W. Yi, Y. Ke, H. Qi, F. He, X. Zhao, X. Wang, D. Ming, and P. Zhou, Research on multi-dimensional N-back task induced EEG variations, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5163–5166, 2015.
- [36] R. Griffin, C. Huisingh, and G. McGwin, Prevalence of and factors associated with distraction among public transit bus drivers, Traffic injury prevention, vol. 15, no. 7, pp. 720–725, 2014.
- [37] G. Lemaître, F. Nogueira, and C. K. Aridas, Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning, Journal of Machine Learning Research, vol. 18, no. 1, pp. 559–563, 2017.
- [38] C. Huertas and R. Juárez-Ramirez, Filter feature selection performance comparison in high-dimensional data: A theoretical and empirical analysis of most popular algorithms, in 17th International Conference on Information Fusion (FUSION), 2014, pp. 1–8.
- [39] M. A. Hall, Feature Selection for Discrete and Numeric Class Machine Learning, 1999.
- [40] J. Strickland, Data Analytics Using Open-Source Tools. Lulu.com, 2016.
- [41] P. Kashyap, Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making. Apress, 2018.
- [42] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
- [43] M. Sacco and R. A. Farrugia, Driver fatigue monitoring system using Support Vector Machines, in 2012 5th International Symposium on Communications, Control and Signal Processing, May 2012, pp. 1–5.
- [44] W. Zhang, B. Cheng, and Y. Lin, Driver drowsiness recognition based on computer vision technology, Tsinghua Science and Technology, vol. 17, no. 3, pp. 354–362, June 2012.
- [45] IEE. Hands Off Detection - IEE - a sense for innovation. (2018, Oct. 17). [Online]. Available: https://www.iee.lu/en/products/automotive/hands-off-detection
- [46] M. Miyaji, H. Kawanaka, and K. Oguri, Driver’s cognitive distraction detection using physiological features by the adaboost, in 2009 12th International IEEE Conference on Intelligent Transportation Systems, Oct. 2009, pp. 90–95.
- [47] Y. Liao, S. E. Li, G. Li, W. Wang, B. Cheng, and F. Chen, Detection of driver cognitive distraction: an SVM based real-time algorithm and its comparison study in typical driving scenarios, in 2016 IEEE Intelligent Vehicles Symposium (IV), 2016, pp. 394–399.
- [48] J. Engström, E. Johansson, and J.Östlund, Effects of visual and cognitive load in real and simulated motorway driving, Transportation Research Part F: Traffic Psychology and Behaviour, vol. 8, no. 2, pp. 97–120, 2005.