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On-road experimental study on driving anger identification model based on physiological features by ROC curve analysis

On-road experimental study on driving anger identification model based on physiological features by ROC curve analysis

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Road rage is a serious psychological issue affecting traffic safety, which has attracted increasing concern regarding driving anger intervention. This study proposed a method for driving anger identification based on physiological features. First, 30 drivers were recruited to perform on-road experiments on a busy route in Wuhan, China. The drivers’ anger could be inducted on the study route by elicitation events, e.g. vehicles weaving/cutting in line, jaywalking, traffic congestion and waiting at red light if they want to finish the experiments ahead of basic time for extra pay. Subsequently, significance analysis was used to determine that five physiological features including heart rate, skin conductance, respiration rate, the relative energy spectrum of θ and β bands of electroencephalogram were effective for driving anger identification. Finally, a linear discriminant model was proposed to identify driving anger based on the optimal thresholds of the five features which were determined by receiver operating characteristic (ROC) curve analysis. The results show that the proposed model achieves an accuracy of 85.84% which is 7.95 and 5.71% higher than the models using back propagation neural network and support vector machine, respectively. The results can provide theoretical foundation for developing driving anger detection devices based on physiological features.

References

    1. 1)
      • C.Z. Wu , H. Lei .
        1. Wu, C.Z., Lei, H.: ‘Review on the study of motorists’ driving anger’, China Saf. Sci. J., 2010, 20, (7), pp. 38.
        . China Saf. Sci. J. , 7 , 3 - 8
    2. 2)
      • (2011)
        2. National Highway Traffic Safety Administration (NHTSA): ‘Traffic safety facts: a compilation of motor vehicle crash data from the fatality analysis reporting system and the general estimates system’ (US Department of Transportation, 2011).
        .
    3. 3)
      • H. Lei .
        3. Lei, H.: ‘The characteristics of angry driving behaviors and its effects on traffic safety’. Master's thesis, Wuhan University of Technology, 2011.
        .
    4. 4)
      • E.R. Dahlen , R.C. Martin , K. Ragan .
        4. Dahlen, E.R., Martin, R.C., Ragan, K., et al: ‘Driving anger, sensation seeking, impulsiveness, and boredom proneness in the prediction of unsafe driving’, Accident Anal. Prev., 2005, 37, (2), pp. 341348.
        . Accident Anal. Prev. , 2 , 341 - 348
    5. 5)
      • T. Lajunen , D. Parker .
        5. Lajunen, T., Parker, D.: ‘Are aggressive people aggressive drivers? a study of the relationship between self-reported general aggressiveness, driver anger and aggressive driving’, Accident Anal. Prev., 2001, 33, (2), pp. 243255.
        . Accident Anal. Prev. , 2 , 243 - 255
    6. 6)
      • L. Kessous , G. Castellano , G. Caridakis .
        6. Kessous, L., Castellano, G., Caridakis, G.: ‘Multimodal emotion recognition in speech-based interaction using facial expression, body gesture and acoustic analysis’, J. Multimodal User Interfaces, 2010, 3, (1), pp. 3348.
        . J. Multimodal User Interfaces , 1 , 33 - 48
    7. 7)
      • H. Lei , X.P. Yan , C.Z. Wu .
        7. Lei, H., Yan, X.P., Wu, C.Z.: ‘The characteristic of vehicle speed under angry driving in china’. Proc. of the 2nd Int. Conf. on Transportation Information and Safety, Wuhan, China, 28 June–1 July, 2013, pp. 15421547.
        . Proc. of the 2nd Int. Conf. on Transportation Information and Safety , 1542 - 1547
    8. 8)
      • R. Abdu , D. Shinar , N. Meiran .
        8. Abdu, R., Shinar, D., Meiran, N.: ‘Situational (state) anger and driving’, Transp. Res. F, Traffic Psychol. Behav., 2012, 15, pp. 575580.
        . Transp. Res. F, Traffic Psychol. Behav. , 575 - 580
    9. 9)
      • P.N. Juslin , J.A. Sloboda . (2010)
        9. Juslin, P.N., Sloboda, J.A.: ‘Handbook of music and emotion: theory, research, applications’ (Oxford University Press, New York, 2010).
        .
    10. 10)
      • H. Cai , Y.Z. Lin .
        10. Cai, H., Lin, Y.Z.: ‘Modeling of operators’ emotion and task performance in a virtual driving environment’, Int. J. Hum.-Comput. Stud., 2011, 69, (9), pp. 571586.
        . Int. J. Hum.-Comput. Stud. , 9 , 571 - 586
    11. 11)
      • K. Oeltze , C. Schie .
        11. Oeltze, K., Schie, C.: ‘Benefits and challenges of multi-driver simulator studies’, IET Intell. Transp. Syst., 2015, 9, (6), pp. 618625.
        . IET Intell. Transp. Syst. , 6 , 618 - 625
    12. 12)
      • D. Zhang , B. Wan , D. Ming .
        12. Zhang, D., Wan, B., Ming, D.: ‘Research progress on emotion recognition based on physiological signals’, J. Biomed. Eng., 2015, 32, (1), pp. 229234.
        . J. Biomed. Eng. , 1 , 229 - 234
    13. 13)
      • A.J. Flidlund , E.Z. Izard . (1983)
        13. Flidlund, A.J., Izard, E.Z.: ‘Electromyographic studies of facial expressions of emotions and patterns of emotions’ (Guilford Press, New York, 1983).
        .
    14. 14)
      • J. Wang , Y. Gong .
        14. Wang, J., Gong, Y.: ‘Normalizing multi-subject variation for drivers’ emotion recognition’. IEEE Int. Conf. on Multimedia and Expo, New York, USA, 28 June–3 July, 2009, pp. 354357.
        . IEEE Int. Conf. on Multimedia and Expo , 354 - 357
    15. 15)
      • C.D. Katsis , N. Katertsidis , G. Ganiatsas .
        15. Katsis, C.D., Katertsidis, N., Ganiatsas, G., et al: ‘Toward emotion recognition in car-racing drivers: a biosignal processing approach’, IEEE Trans. Syst. Man Cybern. A, 2008, 38, (3), pp. 502512.
        . IEEE Trans. Syst. Man Cybern. A , 3 , 502 - 512
    16. 16)
      • K. Schaaff , T. Schultz .
        16. Schaaff, K., Schultz, T.: ‘Towards emotion recognition from electroencephalographic signals’. 3rd Int. Conf. on Affective Computing and Intelligent Interaction and Workshops, Amsterdam, Netherlands, 9–12 September 2009, pp. 16.
        . 3rd Int. Conf. on Affective Computing and Intelligent Interaction and Workshops , 1 - 6
    17. 17)
      • J. Choi , J. Bang , H. Heo .
        17. Choi, J., Bang, J., Heo, H., et al: ‘Evaluation of fear using nonintrusive measurement of multimodal sensors’, Sensors, 2015, 15, pp. 1750717533.
        . Sensors , 17507 - 17533
    18. 18)
      • H. Wang , C. Zhang , T. Shi .
        18. Wang, H., Zhang, C., Shi, T., et al: ‘Real-time EEG-based detection of fatigue driving danger for accident prediction’, Int. J. Neural Syst., 2015, 25, (02), p. 1550002.
        . Int. J. Neural Syst. , 2 , 1550002
    19. 19)
      • G. Rebolledo-Mendez , A. Reyes , S. Paszkowicz .
        19. Rebolledo-Mendez, G., Reyes, A., Paszkowicz, S., et al: ‘Developing a body sensor network to detect emotions during driving’, IEEE Trans. Intell. Transp., 2014, 15, (4), pp. 18501854.
        . IEEE Trans. Intell. Transp. , 4 , 1850 - 1854
    20. 20)
      • D. Shinar , R.P. Compton .
        20. Shinar, D., Compton, R.P.: ‘Aggressive driving: an observational study of driver, vehicle, and situational variables’, Accident Anal. Prev., 2004, 36, (3), pp. 429437.
        . Accident Anal. Prev. , 3 , 429 - 437
    21. 21)
      • P. Wan , C.Z. Wu , X.F. Ma .
        21. Wan, P., Wu, C.Z., Ma, X.F.: ‘A study of Chinese professional drivers’ electroencephalogram characteristics under angry driving based on field experiments’. COTA Int. Conf. of Transportation Professionals, CICTP, Changsha, China, 4–7 July 2014, pp. 21922208.
        . COTA Int. Conf. of Transportation Professionals, CICTP , 2192 - 2208
    22. 22)
      • Q.C. He , W. Li , X.M. Fan .
        22. He, Q.C., Li, W., Fan, X.M., et al: ‘Driver fatigue evaluation model with integration of multi-indicators based on dynamic Bayesian network’, IET Intell. Transp. Syst., 2015, 9, (5), pp. 547554.
        . IET Intell. Transp. Syst. , 5 , 547 - 554
    23. 23)
      • Y. Kwon , K. Kim , J. Tompkinet .
        23. Kwon, Y., Kim, K., Tompkinet, J., et al: ‘Efficient learning of image super-resolution and compression artifact removal with semi-local Gaussian processes’, IEEE Trans. Pattern Anal., 2015, 37, (9), pp. 17921805.
        . IEEE Trans. Pattern Anal. , 9 , 1792 - 1805
    24. 24)
      • S. Bhardwaj , P. Jadhav , B. Adapa .
        24. Bhardwaj, S., Jadhav, P., Adapa, B., et al: ‘Online and automated reliable system design to remove blink and muscle artifact in EEG’. 37th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBS), Milan, 25–29 August 2015, pp. 67846787.
        . 37th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBS) , 6784 - 6787
    25. 25)
      • W. Cheng , Z. Ni , X. Pan .
        25. Cheng, W., Ni, Z., Pan, X.: ‘Receiver operating characteristic curves to determine the optimal operating point and doubtable value interval’, Mod. Prev. Med., 2005, 32, (7), pp. 729731.
        . Mod. Prev. Med. , 7 , 729 - 731
    26. 26)
      • T. Bechtel , L. Capineri , C. Windsor .
        26. Bechtel, T., Capineri, L., Windsor, C., et al: ‘Comparison of ROC curves for landmine detection by holographic radar with ROC data from other methods’. 8th Int. Workshop on Advanced Ground Penetrating Radar, Florence, Italy, 7–10 July 2015, pp. 14.
        . 8th Int. Workshop on Advanced Ground Penetrating Radar , 1 - 4
    27. 27)
      • X.H. Zhao , H.J. Du , J. Rong .
        27. Zhao, X.H., Du, H.J., Rong, J.: ‘Research on identification method of fatigue driving based on ROC curves’, J. Transp. Inf. Saf., 2014, 32, (5), pp. 8894.
        . J. Transp. Inf. Saf. , 5 , 88 - 94
    28. 28)
      • J.L. Deffenbacher .
        28. Deffenbacher, J.L.: ‘Anger, aggression, and risky behavior on the road: a preliminary study of urban and rural differences’, J. Appl. Soc. Psychol., 2008, 38, pp. 2236.
        . J. Appl. Soc. Psychol. , 22 - 36
    29. 29)
      • H. Cai , Y.Z. Lin , R.R. Mourant .
        29. Cai, H., Lin, Y.Z., Mourant, R.R.: ‘Study on driver emotion in driver–vehicle–environment systems using multiple networked driving simulators’. Driving Simulation Conf. of Transportation Research Board of the National Academies, Iowa, USA, 12–14 September 2007, pp. 18.
        . Driving Simulation Conf. of Transportation Research Board of the National Academies , 1 - 8
    30. 30)
      • B. Mehler , B. Reimer , J.F. Coughlin .
        30. Mehler, B., Reimer, B., Coughlin, J.F., et al: ‘Impact of incremental increases in cognitive workload on physiological arousal and performance in young adult drivers’, Transp. Res. Rec., J. Transp. Res. Board, 2009, 2138, pp. 612.
        . Transp. Res. Rec., J. Transp. Res. Board , 6 - 12
    31. 31)
      • M.E. Zhong , P. Wu , J. Peng .
        31. Zhong, M.E., Wu, P., Peng, J.: ‘Study on an emotional state recognition technology based on drivers’ EEGs’, China Saf. Sci. J., 2011, 21, (9), pp. 6469.
        . China Saf. Sci. J. , 9 , 64 - 69
    32. 32)
      • J. Xiang , R. Cao , L. Li .
        32. Xiang, J., Cao, R., Li, L.: ‘Emotion recognition based on the sample entropy of EEG’, Bio-Med. Mater. Eng., 2014, 24, pp. 11851192.
        . Bio-Med. Mater. Eng. , 1185 - 1192
    33. 33)
      • B. González-Iglesias , J.A. Gómez-Fraguela , M.Á. Luengo-Martín .
        33. González-Iglesias, B., Gómez-Fraguela, J.A., Luengo-Martín, M.Á.: ‘Driving anger and traffic violations: Gender differences’, Transp. Res. F, Traffic Psychol. Behav., 2012, 15, pp. 404412.
        . Transp. Res. F, Traffic Psychol. Behav. , 404 - 412
    34. 34)
      • Y. Ge , W. Qu , C. Jiang .
        34. Ge, Y., Qu, W., Jiang, C., et al: ‘The effect of stress and personality on dangerous driving behavior among Chinese drivers’, Accident Anal. Prev., 2014, 73, pp. 3440.
        . Accident Anal. Prev. , 34 - 40
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