Skip to main content
SpringerLink
Log in

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

  • Original Article
  • Published:
Artificial Life and Robotics Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Picard R (1995), Affective computing, MIT Media Laboratory Perceptual Computing Section Technical Report no. 321

  2. Tao J, Tan T (2005) Affective computing: a review, Affective computing and intelligent interaction. LNCS 3784:981–995

    Google Scholar 

  3. Suzuki T (2017), Method for detecting operation mistakes with accelerator medal. In: Proceedings of Fast-zero 2017, pp TuB–P2-1

  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 7–12

  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 1181–1184

  6. Reichardt DM (2008) Approaching driver models which integrate models of emotion and risk. In: Proceedings of IEEE intelligent vehicles symposium 2008, pp 234–239

  7. Malta L et al (2010) Analysis of real-world driver’s frustration. IEEE Trans Intell Transp Syst 12(1):109–118

    Article  Google Scholar 

  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 1690–1695

  9. Yamakoshi T (2006) Hemodynamic responses during simulated automobile driving in a monotonous situation. In: Proceedings of 28th IEEE EMBS annual international conference, pp 5129–5132

  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 10:156–164

    Article  Google Scholar 

  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):631–640

    Article  MathSciNet  Google Scholar 

  12. Haigney D, Westerman SJ (2001) Mobile (cellular) phone use and driving: a critical review of research methodology. Ergonom 44(2):132–143

    Article  Google Scholar 

  13. Strayer DL et al (2003) Cell phone-induced failures of visual attention during simulated driving. J Exp Psychol 9(1):462–466

    Google Scholar 

  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):340–350

    Article  Google Scholar 

  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 394–399

  16. Podusenko A et al (2017) Comparative analysis of classifiers for classification of emergency braking of road motor vehicles. Algorithms 10:129

    Article  MathSciNet  Google Scholar 

  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-A1-2

  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)

  19. Strait M, Scheutz M (2014) What we can and cannot (yet) do with functional near infrared spectroscopy. Front Neurosci 8:117. https://doi.org/10.3389/fnins.2014.00117

    Article  Google Scholar 

  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). https://doi.org/10.5220/0004708102140223

    Google Scholar 

  21. Canning C, Scheutz M (2013) Function near-infrared spectroscopy in human-robot interaction. J Hum Rob Interact 2:62–84

    Google Scholar 

  22. Hoshi Y (2011) Towards the next generation of near-infrared spectroscopy. Philos Trans R Soc A Math Phys Eng Sci 369:4425–4439

    Article  MathSciNet  MATH  Google Scholar 

  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. 2160–2163

  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. 1573–1578

  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 19:1–8

    Google Scholar 

  26. Rose T (2016) The end of average. Harpercollins, USA

Download references

Author information

Authors and Affiliations

  1. Doshisha University, 1-3 Tatara Miyakodani, Kyotanabe-shi, Kyoto, 610-0394, Japan

    Rahadian Yusuf, Ivan Tanev & Katsunori Shimohara

Authors
  1. Rahadian Yusuf
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ivan Tanev
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Katsunori Shimohara
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rahadian Yusuf.

Additional information

This work was presented in part at the 23rd International Symposium on Artificial Life and Robotics, Beppu, Oita, January 18–20, 2018.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yusuf, R., Tanev, I. & Shimohara, K. Affective computing to help recognizing mistaken pedal-pressing during accidental braking. Artif Life Robotics 24, 212–218 (2019). https://doi.org/10.1007/s10015-018-0515-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10015-018-0515-1

Keywords

Search

Navigation