The present study aimed to introduce an approach to implement facial analysis on monitoring driver emotional status in real time. For this purpose, an experimental setup had been performed based on commercial technologies. Such experimental protocol includes the main variables involved in driving and how those variables influence in the driver performance. The goal of the experimental design was to detect which emotions of the driver s face are present during driving and how those emotions can be changed in presence of stimuli from a passive advanced driver for assistance systems (ADAS). Finally, the idea is to investigate if the driver performance changes when some external stimuli are applied as hazards are reported to the driver. The experimental results suggest that the ADAS is not sufficient to enhance the driver’s performance. As a result, the authors propose a new framework for driver assistance systems based on driver state, especially emotions: advanced driver monitoring for assistance systems (ADMAS), refer to Fig. 1. With ADMAS implementation security and intelligence can be provided to a car and thus help to reduce traffic accidents.
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Anders, L., Fang, C., PatrickW, J., Haixin, Z.: Requirements for the design of advanced driver assistance systems—the differences between Swedish and Chinese drivers. Int. J. Des. 2(2), 41–54 (2008)
Akamatsu, M., Green, P., Bengler, K.: Review article automotive technology and human factors research : past , present , and future. Int. J. Veh. Technol. 2013 (2013)
ChingFu, L., JyhChing, J., KunRui, L.: Active collision avoidance system for steering control of autonomous vehicles. IET Intell. Transp. Syst. 8(6), 550–557 (2014)
Grace, R., Byrne, V.E., Bierman, D.M., Legrand, J.M., Gricourt D., Davis, B.K., et al.: A drowsy driver detection system for heavy vehicles. 17th DASC AIAA/IEEE/SAE Digit Avion Syst Conf Proc (Cat No98CH36267) 2 (1998)
Yannis, G., Antoniou, C.: State of art of advanced driver assistance systems (2000)
Guotai, J., Xuemin, S., Fuhui, Z., Peipei, W., Ashgan, O.: Facial expression recognition using thermal image. Conf Proc Annu Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Annu Conf 1, 631–633 (2005)
Guojiang, W., Xiaoxiao, W., Kechang, F.: Behavior decision model of intelligent agent based on artificial emotion. Adv. Comput. Control. ICACC 2010 2nd Int Conf 2010; 4:185–189
Bassem, H., Gausemeier, J.: A design framework for developing a reconfigurable driving simulator. IARIA 8(1&2), 1–17 (2015)
De Filippo, F., Stork, A., Schmedt, H., Bruno, F.: A modular architecture for a driving simulator based on the FDMU approach. Int. J. Interact. Des. Manuf. 8(2), 139–150 (2014)
Tideman, M., van der Voort, M.C., van Houten, F.J.A.M.: A new product design method based on virtual reality, gaming and scenarios. Int. J. Interact. Des. Manuf. 2(4), 195–205 (2008)
Francesca, L., Franco, P., Sara, B.: Design characterization and automatic identification of character lines in automotive field. Int. J. Interact. Des. Manuf. 9(2), 135–143 (2015)
Abhishek, G., Venimadhav, S., NareshKumar, R., Surbhi, J., Abdulmalik, A., MdAfsarKamal, R.: Context-awareness based intelligent driver behavior detection: integrating wireless sensor networks and vehicle ad hoc networks. Int. Conf. Adv. Comput. Commun. Informatics 2014, 2155–2162 (2014)
Imamura, T., Yamashita, H., Zhang, Z., Othman, R., Miyake, T.: A study of classification for driver conditions using driving behaviors. Syst Man Cybern 2008 SMC 2008 IEEE Int Conf pp. 1506–1511 (2008)
Anup, D., Trivedi, M.M.: Examining the impact of driving style on the predictability and responsiveness of the driver: real-world and simulator analysis. IEEE Intell Veh Symp Proc pp. 232–237 (2010)
Bonnin, S., Kummert, F., Schmüdderich, J.: A generic concept of a system for predicting driving behaviors. ITSC, IEEE Conf Intell Transp Syst Proceedings, pp. 1803–1808 (2012)
Kondyli, A., Sisiopiku, V.P., Zhao, L., Barmpoutis, A.: Computer assisted analysis of drivers’ body activity using a range camera. Intell. Transp. Syst. Mag. IEEE 7(3), 18–28 (2015)
Agamennoni, G., Nieto, J.I., Nebot, E.M.: A Bayesian approach for driving behavior inference. In: Proc. IEEE Intell. Veh. Symp. Iv. IEEE Intelligent Vehicles Symposium (IV); pp. 595–600 (2011)
Okamoto, M., Otani, S., Kaitani, Y., Uchida, K.: Identification of driver operations with extraction of driving primitives. Proc IEEE Int Conf Control p. 338–344 (2011)
Keshuang, T., Zhu Shengfa, X., Yanqing, W.F.: Modeling drivers ’ dynamic decision-making behavior during the phase transition period : an analytical approach based on hidden Markov model theory. IEEE Trans. Intell. Transp. 17(1), 206–214 (2016)
Lin, C.T., Chen, S.A., Ko, L.W., Wang, Y.K.: Acquisition AEEGSignal, pp. 1497–1500. Neural Networks, EEG-based Brain Dynamics of Driving Distraction. Proc Int Jt Conf (2011)
Shiwu, L., Linhong, W., Zhifa, Y., Bingkui, J., Feiyan, Q., Zhongkai, Y.: An active driver fatigue identification technique using multiple physiological features. Proc 2011 Int Conf Mechatron Sci Electr Eng Comput MEC 2011 pp. 733–737 (2011)
Rodriguez-Ibanez, N., Garcia-Gonzalez, M.A., Fernandez-Chimeno, M., Ramos-Castro, J.: Drowsiness detection by thoracic effort signal analysis in real driving environments. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2011, 6055–6058 (2011)
Qichang, H., Zhimin, F., Xiumin, F., Wei, L.: Driver fatigue evaluation model with integration of multi-indicators based on dynamic Bayesian network. IET Intell. Transp. Syst. 9(5), 547–554 (2015)
Martin, S., Tawari, A., Trivedi, M.M.: Monitoring head dynamics for driver assistance systems: A multi-perspective approach. In: IEEE Conf. Intell. Transp. Syst. Proceedings, ITSC. Itsc. pp. 2286–2291 (2013)
Garbas, J.U., Ruf, T., Unfried, M., Dieckmann, A.: Towards Robust Real-Time Valence Recognition from Facial Expressions for Market Research Applications. In: Affect. Comput. Intell. Interact. Humaine Association Conference on. pp. 570–575 (2013)
Ahmad, P., Svetlana, Y., Marina, G.: An efficient facial expression recognition system in infrared images. Fourth Int. Conf. Emerg. Secur. Technol. 2013, 25–28 (2013)
Wang, S., Shen, P., Liu, Z.: Facial expression recognition from infrared thermal images using temperature difference by voting. In: Proc. IEEE CCIS2012 ; pp. 94–98 (2012)
Neagoe VE, Ieee SeniorMember, Ropot AD, Mugioiu AC. Real Time Face Recognition Using Decision Fusion of Neural Classifiers in the Visible and Thermal Infrared Spectrum 3 . Decision Fusion of Neural Classifiers Using Dempster-Shafer Theory 2 . A Neural Pattern Classifier Composed by Concurrent Self- Organiz. Time, pp. 301–306 (2007)
Flores, M.J., Armingol, M.J.M., de la Escalera, A.: Sistema avanzado de asistencia a la conducción para la detección de la somnolencia. RIAI Rev. Iberoam Autom e Inform Ind 8(3), 216–228 (2011)
Bartra, A., Meca, P., Guamán, A., Pardo, A., Marco, S., Montes, A. A.: feasability study of drowsiness detection using driving behaviour parameters. IEEE Intell Veh Symp Proc pp. 111–116 (2012)
Guo, X.: Research on emotion recognition based on physiological signal and AuBT. 2011 Int Conf Consum Electron Commun Networks, CECNet 2011—Proc pp. 614–617 (2011)
Liu, Y., Sourina, O.: E.E.G., databases for emotion recognition. Proc: Int Conf Cyberworlds. CW, pp. 302–309 (2013)
Zirui, L., Olga, S., Lipo, W., Yisi, L.: Stability of features in real-time EEG-based emotion recognition algorithm. Int. Conf. Cyberworlds 2014, 137–144 (2014)
Kolli, A., Fasih, A., Al Machot, F., Kyamakya, K.: Non-intrusive car driver ’ s emotion recognition using thermal camera. Nonlinear Dyn Synchronization 16th Int’l Symp Theor Electr Eng (ISTET), 2011 Jt 3rd Int’l Work (2011)
Al Machot, F., Mosa, A.H., Dabbour, K., Fasih, A., Schwarzlmüller, C., Ali, M., et al.: A novel real-time emotion detection system from audio streams based on Bayesian Quadratic Discriminate Classifier for ADAS. Proc Jt 3rd Int Work Nonlinear Dyn Synchronization, INDS’11 16th Int Symp Theor Electr Eng ISTET’11; pp. 47–51 (2011)
Jeamin, K., Kwac Jungsuk, J., Wendy, S.M., Larry, L., Clifford, N.: Why did my car just do that? Explaining semi-autonomous driving actions to improve driver understanding, trust, and performance. Int. J. Interact. Des. Manuf. 9(4), 269–275 (2015)
Toshihiro, W., Koji, O., Chiyomi, M., Kei, I., Katsunobu, I., Kazuya, T., et al.: Driver identification using driving behavior signals. IEEE Conf. Intell. Transp. Syst. Proc. ITSC 2005, 907–912 (2005)
Chiyomi, M., Yoshihiro, N., Koji, O., Toshihiro, W., Katsunobu, I., Kazuya, T., et al.: Driver modeling based on driving behavior and its evaluation in driver identification. Proc. IEEE 95(2), 427–437 (2007)
Pongtep, A., Ryuta, T., Toshihiro, W.: On the use of stochastic driver behavior model in lane departure warning. IEEE Trans. Intell. Transp. Syst. 12(1), 174–183 (2011)
VadimA, B., Petros, I.: Personalized driver/vehicle lane change models for ADAS. IEEE Trans. Veh. Technol. 64(10), 4422–4431 (2015)
Stéphanie, L., Ashwin, C., Yiqi, G., Tseng, H.E., Borrelli, F.: Driver models for personalised driving assistance. Veh. Syst. Dyn. 00(July), 1–16 (2015)
This research was carried out with the help of Consejo Nacional de Ciencia y Tecnología (CONACYT) Mexico, scholarship 593255.
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Izquierdo-Reyes, J., Ramirez-Mendoza, R.A., Bustamante-Bello, M.R. et al. Advanced driver monitoring for assistance system (ADMAS). Int J Interact Des Manuf 12, 187–197 (2018). https://doi.org/10.1007/s12008-016-0349-9
- Driver monitoring
- Advanced driver assistance systems
- Advance driver monitoring for assistance systems
- Emotions recognition