Advertisement

Advanced driver monitoring for assistance system (ADMAS)

Based on emotions
  • Javier Izquierdo-Reyes
  • Ricardo A. Ramirez-MendozaEmail author
  • Martin R. Bustamante-Bello
  • Sergio Navarro-Tuch
  • Roberto Avila-Vazquez
Technical Paper

Abstract

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.

Keywords

Driver monitoring Advanced driver assistance systems Advance driver monitoring for assistance systems Emotions recognition 

Notes

Acknowledgments

This research was carried out with the help of Consejo Nacional de Ciencia y Tecnología (CONACYT) Mexico, scholarship 593255.

References

  1. 1.
    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)Google Scholar
  2. 2.
    Akamatsu, M., Green, P., Bengler, K.: Review article automotive technology and human factors research : past , present , and future. Int. J. Veh. Technol. 2013 (2013)Google Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    Yannis, G., Antoniou, C.: State of art of advanced driver assistance systems (2000)Google Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    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; 4:185–189Google Scholar
  8. 8.
    Bassem, H., Gausemeier, J.: A design framework for developing a reconfigurable driving simulator. IARIA 8(1&2), 1–17 (2015)Google Scholar
  9. 9.
    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)CrossRefGoogle Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    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)Google Scholar
  13. 13.
    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)Google Scholar
  14. 14.
    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)Google Scholar
  15. 15.
    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)Google Scholar
  16. 16.
    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)CrossRefGoogle Scholar
  17. 17.
    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)Google Scholar
  18. 18.
    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)Google Scholar
  19. 19.
    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)CrossRefGoogle Scholar
  20. 20.
    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)Google Scholar
  21. 21.
    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)Google Scholar
  22. 22.
    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)Google Scholar
  23. 23.
    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)CrossRefGoogle Scholar
  24. 24.
    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)Google Scholar
  25. 25.
    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)Google Scholar
  26. 26.
    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)Google Scholar
  27. 27.
    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)Google Scholar
  28. 28.
    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)Google Scholar
  29. 29.
    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)CrossRefGoogle Scholar
  30. 30.
    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)Google Scholar
  31. 31.
    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)Google Scholar
  32. 32.
    Liu, Y., Sourina, O.: E.E.G., databases for emotion recognition. Proc: Int Conf Cyberworlds. CW, pp. 302–309 (2013)Google Scholar
  33. 33.
    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)Google Scholar
  34. 34.
    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)Google Scholar
  35. 35.
    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)Google Scholar
  36. 36.
    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)CrossRefGoogle Scholar
  37. 37.
    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)Google Scholar
  38. 38.
    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)CrossRefzbMATHGoogle Scholar
  39. 39.
    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)CrossRefGoogle Scholar
  40. 40.
    VadimA, B., Petros, I.: Personalized driver/vehicle lane change models for ADAS. IEEE Trans. Veh. Technol. 64(10), 4422–4431 (2015)Google Scholar
  41. 41.
    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)Google Scholar

Copyright information

© Springer-Verlag France 2016

Authors and Affiliations

  1. 1.Escuela de Ingeniería y CienciasTecnológico de MonterreyCiudad de MéxicoMexico

Personalised recommendations