Affective computing to help recognizing mistaken pedal-pressing during accidental braking

  • Rahadian YusufEmail author
  • Ivan Tanev
  • Katsunori Shimohara
Original Article


Affective computing has been used to improve computer usability and user interface, by considering user’s emotion. One aspect of affective computing is emotion recognition. There have been many researches regarding emotion recognition, yet there is still room for exploration in applying affective computing into a driver assistance system. On driving assistance, one aspect is about emergency braking. Several researches have been analyzing emergency braking and proposed approaches to detect them. A more focused but significant (especially for elderly and beginner driver) case is mistakenly pressing accelerator instead of brake pedal during emergency braking, which often leads to accidents. This paper investigates researches on affective computing, affective sensors, emergency braking, and mistaken pedal pressing. It is also investigating on a possible approach to realize the objective of improving the existing driving assistance system using affective computing, on the case of mistaken pedal-pressing during emergency braking. For preliminary experiment, driving simulator’s brake pedal is manipulated to act as accelerator pedal during emergency braking, while observing driver’s change of expression, and measuring time approximation.


Affective computing Emergency braking Mistaken pedal pressing 


  1. 1.
    Picard R (1995), Affective computing, MIT Media Laboratory Perceptual Computing Section Technical Report no. 321Google Scholar
  2. 2.
    Tao J, Tan T (2005) Affective computing: a review, Affective computing and intelligent interaction. LNCS 3784:981–995Google Scholar
  3. 3.
    Suzuki T (2017), Method for detecting operation mistakes with accelerator medal. In: Proceedings of Fast-zero 2017, pp TuB–P2-1Google Scholar
  4. 4.
    Oliver N, Pentland AP (2000) Graphical models for driver behavior recognition in a SmartCar. In: Proceedings of the IEEE intelligent vehicles symposium 2000, pp 7–12Google Scholar
  5. 5.
    Xiao Q et al (2015) A new study on the driver’s emotion model. Fifth international conference on communication systems and network technologies. pp 1181–1184Google Scholar
  6. 6.
    Reichardt DM (2008) Approaching driver models which integrate models of emotion and risk. In: Proceedings of IEEE intelligent vehicles symposium 2008, pp 234–239Google Scholar
  7. 7.
    Malta L et al (2010) Analysis of real-world driver’s frustration. IEEE Trans Intell Transp Syst 12(1):109–118CrossRefGoogle Scholar
  8. 8.
    Paschero M (2012) A real time classifier for emotion and stress recognition in a vehicle driver. In: 2012 IEEE international symposium on industrial electronics. pp 1690–1695Google Scholar
  9. 9.
    Yamakoshi T (2006) Hemodynamic responses during simulated automobile driving in a monotonous situation. In: Proceedings of 28th IEEE EMBS annual international conference, pp 5129–5132Google Scholar
  10. 10.
    Sharma DG et al (2017) Effects of cruising speed on steering oscillations of car induced by modeled cognitively impaired human driver. SICE J Control Meas Syst Integr 10:156–164CrossRefGoogle Scholar
  11. 11.
    Lee JD et al (2001) Speech-based interaction with in-vehicle computers: the effect of speech-based e-mail on drivers’ attention to the rodway. Hum Factors 43(4):631–640CrossRefGoogle Scholar
  12. 12.
    Haigney D, Westerman SJ (2001) Mobile (cellular) phone use and driving: a critical review of research methodology. Ergonom 44(2):132–143CrossRefGoogle Scholar
  13. 13.
    Strayer DL et al (2003) Cell phone-induced failures of visual attention during simulated driving. J Exp Psychol 9(1):462–466Google Scholar
  14. 14.
    Liang Y, Reyes L, Lee JD (2007) Real-time detection of driver cognitive distraction using support vector machines. IEEE Trans Intell Transp Syst 8(2):340–350CrossRefGoogle Scholar
  15. 15.
    Laio Y et al (2016) Detection of driver cognitive distraction: an SVM based real-time algorithm and its comparison study in typical driving scenarios. In: IEEE intelligent vehicles symposium, pp 394–399Google Scholar
  16. 16.
    Podusenko A et al (2017) Comparative analysis of classifiers for classification of emergency braking of road motor vehicles. Algorithms 10:129MathSciNetCrossRefGoogle Scholar
  17. 17.
    Tsukuda S, Shiozawa Y, Mouri H (2017) Proposal of advanced emergency braking system adapted to the road surface condition. In: Proceedings of Fast-zero 2017, pp TuA-A1-2Google Scholar
  18. 18.
    Yusuf R, Tanev I, Shimohara K (2015) Application of genetic programming and genetic algorithm in evolving emotion recognition module. IEEE congress on evolutionary computation (CEC) 2015, pp 1444–1449 (ISBN 978-1-4799-7491-7) Google Scholar
  19. 19.
    Strait M, Scheutz M (2014) What we can and cannot (yet) do with functional near infrared spectroscopy. Front Neurosci 8:117. CrossRefGoogle Scholar
  20. 20.
    Frey J, Mühl C, Lotte F, Hachet M (2014) Review of the use of electroencephalography as an evaluation method for human-computer interaction. Int Conf Physiol Comput Syst (PhyCS). CrossRefGoogle Scholar
  21. 21.
    Canning C, Scheutz M (2013) Function near-infrared spectroscopy in human-robot interaction. J Hum Rob Interact 2:62–84Google Scholar
  22. 22.
    Hoshi Y (2011) Towards the next generation of near-infrared spectroscopy. Philos Trans R Soc A Math Phys Eng Sci 369:4425–4439MathSciNetCrossRefGoogle Scholar
  23. 23.
    Herff C, Heger D, Putze F, Hennrich J, Fortman O, Schultz T (2013) Classification of mental tasks in prefrontal cortex using fNIRS. In: Proceedings of 35th annual international conference of the IEEE engineering in medicine and biology society, pp. 2160–2163Google Scholar
  24. 24.
    Huve G, Takahashi K, Hashimoto M (2017) Brain activity recognition with a wearable fNIRS using neural networks. In: Proceedings of 2017 IEEE international conference on mechatronics and automaton, pp. 1573–1578Google Scholar
  25. 25.
    Teng T, Luzheng B, Liu Y (2017) EEG-based detection of driver emergency braking intention for brain-controlled vehicles. IEEE Trans Intell Transp Syst 19:1–8Google Scholar
  26. 26.
    Rose T (2016) The end of average. Harpercollins, USAGoogle Scholar

Copyright information

© International Society of Artificial Life and Robotics (ISAROB) 2018

Authors and Affiliations

  • Rahadian Yusuf
    • 1
    Email author
  • Ivan Tanev
    • 1
  • Katsunori Shimohara
    • 1
  1. 1.Doshisha UniversityKyotoJapan

Personalised recommendations