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Differences in Driving Intention Transitions Caused by Driver’s Emotion Evolutions

Author

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  • Yaqi Liu

    (School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China)

  • Xiaoyuan Wang

    (College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000, China
    Joint Laboratory for Internet of Vehicles, Ministry of Education-China Mobile Communications Corporation, Tsinghua University, Beijing 100048, China)

Abstract

Joining worldwide efforts to understand the relationship between driving emotion and behavior, the current study aimed at examining the influence of emotions on driving intention transition. In Study 1, taking a car-following scene as an example, we designed the driving experiments to obtain the driving data in drivers’ natural states, and a driving intention prediction model was constructed based on the HMM. Then, we analyzed the probability distribution and transition probability of driving intentions. In Study 2, we designed a series of emotion-induction experiments for eight typical driving emotions, and the drivers with induced emotion participated in the driving experiments similar to Study 1. Then, we obtained the driving data of the drivers in eight typical emotional states, and the driving intention prediction models adapted to the driver’s different emotional states were constructed based on the HMM severally. Finally, we analyzed the probabilistic differences of driving intention in divers’ natural states and different emotional states, and the findings showed the changing law of driving intention probability distribution and transfer probability caused by emotion evolution. The findings of this study can promote the development of driving behavior prediction technology and an active safety early warning system.

Suggested Citation

  • Yaqi Liu & Xiaoyuan Wang, 2020. "Differences in Driving Intention Transitions Caused by Driver’s Emotion Evolutions," IJERPH, MDPI, vol. 17(19), pages 1-22, September.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:19:p:6962-:d:417940
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    References listed on IDEAS

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    1. Gipps, P.G., 1981. "A behavioural car-following model for computer simulation," Transportation Research Part B: Methodological, Elsevier, vol. 15(2), pages 105-111, April.
    2. Ma, Liang & Chen, Bin & Wang, Xiaodong & Zhu, Zhengqiu & Wang, Rongxiao & Qiu, Xiaogang, 2019. "The analysis on the desired speed in social force model using a data driven approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 894-911.
    3. Juan Li & Qinglian He & Hang Zhou & Yunlin Guan & Wei Dai, 2016. "Modeling Driver Behavior near Intersections in Hidden Markov Model," IJERPH, MDPI, vol. 13(12), pages 1-15, December.
    4. Chen, Danjue & Laval, Jorge & Zheng, Zuduo & Ahn, Soyoung, 2012. "A behavioral car-following model that captures traffic oscillations," Transportation Research Part B: Methodological, Elsevier, vol. 46(6), pages 744-761.
    5. Folkes, Valerie S, 1988. "Recent Attribution Research in Consumer Behavior: A Review and New Directions," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 14(4), pages 548-565, March.
    6. Xiang Feng & Yaojia Wei & Xianglin Pan & Longhui Qiu & Yongmei Ma, 2020. "Academic Emotion Classification and Recognition Method for Large-scale Online Learning Environment—Based on A-CNN and LSTM-ATT Deep Learning Pipeline Method," IJERPH, MDPI, vol. 17(6), pages 1-16, March.
    7. Shima Ghassempour & Federico Girosi & Anthony Maeder, 2014. "Clustering Multivariate Time Series Using Hidden Markov Models," IJERPH, MDPI, vol. 11(3), pages 1-23, March.
    8. Xinhua Mao & Changwei Yuan & Jiahua Gan & Shiqing Zhang, 2019. "Risk Factors Affecting Traffic Accidents at Urban Weaving Sections: Evidence from China," IJERPH, MDPI, vol. 16(9), pages 1-17, May.
    9. Muhammad Zahid & Yangzhou Chen & Sikandar Khan & Arshad Jamal & Muhammad Ijaz & Tufail Ahmed, 2020. "Predicting Risky and Aggressive Driving Behavior among Taxi Drivers: Do Spatio-Temporal Attributes Matter?," IJERPH, MDPI, vol. 17(11), pages 1-21, June.
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    Cited by:

    1. Xiaoyuan Wang & Yaqi Liu & Longfei Chen & Huili Shi & Junyan Han & Shijie Liu & Fusheng Zhong, 2022. "Research on Emotion Activation Efficiency of Different Drivers," Sustainability, MDPI, vol. 14(21), pages 1-27, October.
    2. Yaqi Liu & Xiaoyuan Wang & Longfei Chen & Shijie Liu & Junyan Han & Huili Shi & Fusheng Zhong, 2022. "Driver’s Visual Attention Characteristics and Their Emotional Influencing Mechanism under Different Cognitive Tasks," IJERPH, MDPI, vol. 19(9), pages 1-28, April.

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