Driver's intention is a self-internal state that represents a commitment to carrying out driving action at the next moment, which could be affected by driver's emotion. Therefore, understanding driver's emotion is an important basis for developing driver intention recognition models. This study aims to gain a better insight of the characteristics of driver intention transition trigged by driver's emotion. The Hidden Markov model was used to develop a driver intention recognition model with the involvement of driver's emotions. Assorted materials including visual, auditory and olfactory stimuli were used to evoke driver's emotions before the driving experiments, as well as keep and increase the emotional level during driving. Real and virtual driving experiments were conducted to collect human-vehicle-environment dynamic data in two-lane roads. The results show that the proposed model can achieve high accuracy and reliability in estimating driver's intention transition with the evolution of driver emotion. Our findings of this study can be used to develop the personalized driving warning system and intelligent human-machine interaction in vehicles. This study would be of great theoretical significance for improving road traffic safety.

References

1. 1)
  - 3. Buchholz, C., Vorsatz, T., Kind, S., et al: ‘SHPbench – a smart hybrid prototyping, based environment for early testing, verification and (user based) validation of advanced driver assistant systems of cars’, Proc. Cirp, 2017, 60, pp. 139–144.
2. 2)
  - 8. Sani, S., Tabibi, Z., Fadardi, J., et al: ‘Aggression, emotional self-regulation, attentional bias, and cognitive inhibition predict risky driving behavior’, Accid. Anal. Prev., 2017, 109, (dec.), pp. 78–88.
3. 3)
  - 20. Quan, C., Ren, F.: ‘Weighted high-order hidden markov models for compound emotions recognition in text’, Inf. Sci., 2016, 329, pp. 581–596.
4. 4)
  - 6. Kinnear, N., Helman, S., Wallbank, C., et al: ‘An experimental study of factors associated with driver frustration and overtaking intentions’, Accident Anal. Prev., 2015, 79, pp. 221–230.
5. 5)
  - 7. Zoellick, J., Kuhlmey, A., Schenk, L., et al: ‘Amused, accepted, and used? Attitudes and emotions towards automated vehicles, their relationships, and predictive value for usage intention’, Transp. Res. F Traffic Psychology Behav., 2019, 65, pp. 68–78.
6. 6)
  - 15. Li, Y., Wu, C., Ma, X.: ‘A recognition model for lane change intention based on neural network with EKF algorithm’, J. Wuhan Univ. Technol. (Transp. Sci. Eng.), 2013, 37, (4), pp. 843–847.
7. 7)
  - 22. Wang, K.: ‘Study on the coupling mechanism of vehicle cluster situation and driver's propensity’, Shandong University of Technology, 2015.
8. 8)
  - 9. Welch, K., Harnett, C., Lee, Y.: ‘A review on measuring affect with practical sensors to monitor driver behavior’, Safety, 2019, 5, (4), p. 72.
9. 9)
  - 18. Gmez-Lopera, J., Martínez-Aroza, J., Román-Roldán, R., et al: ‘The evaluation problem in discrete semi-hidden markov models’, Math. Comput. Simulation, 2016, 137, (C), pp. 350–365.
10. 10)
  - 13. Berndt, H., Emmert, J., Dietmayer, K.: ‘Continuous driver intention recognition with hidden markov models’. Int. IEEE Conf. on Intelligent Transportation Systems, People's Republic of China, 2008, pp. 1189–1194.
11. 11)
  - 19. Akay, B., Oguz, K.: ‘Speech emotion recognition: emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers’, Speech Commun., 2020, 116, pp. 56–76.
12. 12)
  - 1. Yu, F., Wang, B., Wu, X., et al: ‘Highway management’ (China Communications Press, People's Republic of China, 2000).
13. 13)
  - 5. Haag, A., Goronzy, S., Schaich, P., et al: ‘Emotion recognition using bio-sensors: first steps towards an automatic system’, Int. J. Comput. Electr. Eng., 2012, 3068, pp. 36–48.
14. 14)
  - 25. Wu, D., Lin, Y., Peng, C., et al: ‘Small sample based calculation of annual vehicle kilometers traveled’, J. Transp. Syst. Eng. Inf. Technol., 2009, 9, (2), pp. 155–160.
15. 15)
  - 10. Wang, X., Liu, Y., Wang, F., et al: ‘Feature extraction and dynamic identification of drivers’ emotions’, Transp. Res. F, Traffic Psychol. Behav., 2019, 62, pp. 175–191.
16. 16)
  - 4. Agrawal, U., Giripunje, S., Bajaj, P.: ‘Emotion and gesture recognition with soft computing tool for drivers assistance system in human centered transportation’. Int. Conf. On Systems, Man, And Cybernetics, IEEE, Manchester, UK, 2013, pp. 4612–4616.
17. 17)
  - 24. Zhang, M.: ‘Study of car-following behavior of novice drivers under different risk levels based on real car simulation platform’, Shanghai Jiao Tong University, 2014.
18. 18)
  - 16. Johnston, V.S.: ‘Why we feel, the science of human emotions’ (Perseus Publishing, New York, NY, USA, 1999).
19. 19)
  - 17. Li, J., Pedrycz, W., Jamal, I.: ‘Multivariate time series anomaly detection: a framework of hidden markov models’, Appl. Soft Comput., 2017, 60, pp. 229–240.
20. 20)
  - 14. Aoude, G., How, J.: ‘Using support vector machines and Bayesian filtering for classifying agent intentions at road intersections’, 2009, http://hdl.handle.net/1721.1/46720.
21. 21)
  - 2. Jiménez, F., Naranjo, J., Anaya, J., et al: ‘Advanced driver assistance system for road environments to improve safety and efficiency’, Transp. Res. Procedia, 2016, 14, pp. 2245–2254.
22. 22)
  - 11. Dogan, U., Edelbrunner, J, Iossifidis, I.: ‘Autonomous driving: A comparison of machine learning techniques by means of the prediction of lane change behavior’. IEEE Int. Conf. on Robotics and Biomimetics, Phuket, Thailand, 2011, pp. 1837–1843.
23. 23)
  - 26. Liu, J.: ‘Study on feature extraction and identification model of driving tendency based on dynamic driver-vehicle environment data’, Shandong University of Technology, 2012.
24. 24)
  - 12. Bocklisch, F., Bocklisch, S., Beggiato, M., et al: ‘Adaptive fuzzy pattern classification for the online detection of driver lane change intention’, Neurocomputing, 2017, 162, pp. 148–158.
25. 25)
  - 21. Wang, X., Wang, J., Zhang, J., et al: ‘Driver's behavior and decision-making optimization model in mixed traffic environment’, Adv. Mech. Eng., 2015, 7, (2), pp. 759571–759571.
26. 26)
  - 23. Wang, G.: ‘Study on the driver's propensity adapted to vehicle safety warning system’, Shandong University of Technology, 2014.

Transition characteristics of driver's intentions triggered by emotional evolution in two-lane urban roads

References

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