IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v16y2019i7p1236-d220669.html
   My bibliography  Save this article

Change-Point Analysis of Eye Movement Characteristics for Female Drivers in Anxiety

Author

Listed:
  • Yongqing Guo

    (School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255049, 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)

  • Qing Xu

    (Department of Automotive Engineering, Tsinghua University, Beijing 100084, China)

  • Feifei Liu

    (School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255049, China)

  • Yaqi Liu

    (School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255049, China)

  • Yuanyuan Xia

    (College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000, China)

Abstract

Driver hazard perception is highly related to involvement in traffic accidents, and vision is the most important sense with which we perceive risk. Therefore, it is of great significance to explore the characteristics of drivers’ eye movements to promote road safety. This study focuses on analyzing the changes of drivers’ eye-movement characteristics in anxiety. We used various materials to induce drivers’ anxiety, and then conducted the real driving experiments and driving simulations to collect drivers’ eye-movement data. Then, we compared the differences between calm and anxiety on drivers’ eye-movement characteristics, in order to extract the key eye-movement features. The least squares method of change point analysis was carried out to detect the time and locations of sudden changes in eye movement characteristics. The results show that the least squares method is effective for identifying eye-movement changes of female drivers in anxiety. It was also found that changes in road environments could cause a significant increase in fixation count and fixation duration for female drivers, such as in scenes with traffic accidents or sharp curves. The findings of this study can be used to recognize unexpected events in road environment and improve the geometric design of curved roads. This study can also be used to develop active driving warning systems and intelligent human–machine interactions in vehicles. This study would be of great theoretical significance and application value for improving road traffic safety.

Suggested Citation

  • Yongqing Guo & Xiaoyuan Wang & Qing Xu & Feifei Liu & Yaqi Liu & Yuanyuan Xia, 2019. "Change-Point Analysis of Eye Movement Characteristics for Female Drivers in Anxiety," IJERPH, MDPI, vol. 16(7), pages 1-17, April.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:7:p:1236-:d:220669
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/16/7/1236/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/16/7/1236/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Oviedo-Trespalacios, Oscar & Truelove, Verity & Watson, Barry & Hinton, Jane A., 2019. "The impact of road advertising signs on driver behaviour and implications for road safety: A critical systematic review," Transportation Research Part A: Policy and Practice, Elsevier, vol. 122(C), pages 85-98.
    2. Michael W. Robbins & Colin M. Gallagher & Robert B. Lund, 2016. "A General Regression Changepoint Test for Time Series Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 670-683, April.
    3. Li Qin & Li-Li Dong & Wen-Hai Xu & Li-Dong Zhang & Arturo S. Leon, 2018. "Influence of Vehicle Speed on the Characteristics of Driver’s Eye Movement at a Highway Tunnel Entrance during Day and Night Conditions: A Pilot Study," IJERPH, MDPI, vol. 15(4), pages 1-17, April.
    4. Moosup Kim & Sangyeol Lee, 2016. "On the tail index inference for heavy-tailed GARCH-type innovations," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 68(2), pages 237-267, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhuo Chen & Kang Tian, 2022. "Optimization of Evaluation Indicators for Driver’s Traffic Literacy: An Improved Principal Component Analysis Method," SAGE Open, , vol. 12(2), pages 21582440221, June.
    2. Tanvir Chowdhury & Shakil Mohammad Rifaat & Richard Tay, 2022. "Characteristics of Pedestrians in Bangladesh Who Did Not Receive Public Education on Road Safety," Sustainability, MDPI, vol. 14(16), pages 1-11, August.
    3. Karolina M. Zielinska-Dabkowska, 2022. "Healthier and Environmentally Responsible Sustainable Cities and Communities. A New Design Framework and Planning Approach for Urban Illumination," Sustainability, MDPI, vol. 14(21), pages 1-22, November.
    4. Zhongxiang Feng & Miaomiao Yang & Yingjie Du & Jin Xu & Congjun Huang & Xu Jiang, 2021. "Effects of the Spatial Structure Conditions of Urban Underpass Tunnels’ Longitudinal Section on Drivers’ Physiological and Behavioral Comfort," IJERPH, MDPI, vol. 18(20), pages 1-20, October.
    5. Shi, Xuesheng & Gallagher, Colin & Lund, Robert & Killick, Rebecca, 2022. "A comparison of single and multiple changepoint techniques for time series data," Computational Statistics & Data Analysis, Elsevier, vol. 170(C).
    6. Jorge Tiago Bastos & Pedro Augusto B. dos Santos & Eduardo Cesar Amancio & Tatiana Maria C. Gadda & José Aurélio Ramalho & Mark J. King & Oscar Oviedo-Trespalacios, 2020. "Naturalistic Driving Study in Brazil: An Analysis of Mobile Phone Use Behavior while Driving," IJERPH, MDPI, vol. 17(17), pages 1-14, September.
    7. Alex Karagrigoriou & Ioannis Mavrogiannis & Georgia Papasotiriou & Ilia Vonta, 2023. "An Exponentiality Test of Fit Based on a Tail Characterization against Heavy and Light-Tailed Alternatives," Risks, MDPI, vol. 11(10), pages 1-22, September.
    8. Wei, Sen & Li, Yanping & Yang, Hanqing & Xie, Minghui & Wang, Yuanqing, 2023. "A comprehensive operation and maintenance assessment for intelligent highways: A case study in Hong Kong-Zhuhai-Macao bridge," Transport Policy, Elsevier, vol. 142(C), pages 84-98.
    9. Tianbo Ji & Xuanhua Yin & Peng Cheng & Liting Zhou & Siyou Liu & Wei Bao & Chenyang Lyu, 2022. "IvCDS: An End-to-End Driver Simulator for Personal In-Vehicle Conversational Assistant," IJERPH, MDPI, vol. 19(23), pages 1-19, November.
    10. Shenfei Chen & Bing Zhang & Xianxian Zeng & Yi Lin & Haitian Zhao, 2023. "Methods to Reduce Flicker and Light Pollution of Low-Mounting-Height Luminaires in Urban Road Lighting," Sustainability, MDPI, vol. 15(10), pages 1-19, May.
    11. Moosup Kim & Sangyeol Lee, 2019. "Test for tail index constancy of GARCH innovations based on conditional volatility," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(4), pages 947-981, August.
    12. Yongzheng Yang & Zhigang Du & Fangtong Jiao & Fuquan Pan, 2021. "Analysis of EEG Characteristics of Drivers and Driving Safety in Undersea Tunnel," IJERPH, MDPI, vol. 18(18), pages 1-18, September.
    13. Mo Li & QiQi Lu, 2022. "Changepoint detection in autocorrelated ordinal categorical time series," Environmetrics, John Wiley & Sons, Ltd., vol. 33(7), November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:16:y:2019:i:7:p:1236-:d:220669. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.