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Discriminant Model of Driving Distraction During Mobile Phone Conversation Based on Eye Movements

  • Lian Xie
  • Min Duan
  • Wenyong LiEmail author
Conference paper
  • 18 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 617)

Abstract

In order to investigate the characteristics of drivers’ eye movements during distracted driving caused by mobile phone conversation and establish a driving distraction discriminant model, a driving simulation experiment was conducted. The eye movement index data were collected by eye tracker under different traffic scenes which include normal driving and perform simple or complex conversation secondary task on the urban road and freeway, then the variance analysis was used to analyze the characteristics. Finally, according to the characteristics of drivers’ eye movements, a driving distraction discriminant model based on fisher discriminant analysis was constructed for different road types. The ANOVA results showed that the effectiveness of road type and conversation task on the cumulative proportion of the driver’s focus on the area of interest in the front road is not statistically significant. However, the average duration of the driver’s attention under urban road scene is significantly higher than that of the freeway, and with the increasing of difficulty of driving task, the average duration of attention increased significantly. In addition, the road type and conversation task significantly influenced the change range of pupil area. The accuracy rate of the discriminant model is 75.2% for the driving distraction on urban roads, and 78.3% for the distraction on freeway.

Keywords

Traffic safety Distraction Fisher’s linear discriminant Eye movement Driving simulation 

Notes

Funding

The authors acknowledge the support from the National Nature Science Foundation of China (nos. 61963011, 5177051327, 51678460, U1664262, 71861006) and the Innovation and Entrepreneurship Training Program for Undergraduates (201810595188).

References

  1. 1.
    Jin LS, Li KY, Xian HC, Gao LL (2014) A study of secondary driving tasks on safety. J Transp Inf Saf 32(5):7–12, 19Google Scholar
  2. 2.
    Dingus TA, Klauer SG, Neale VL, Petersen A, Lee SE, Sudweeks J et al (2006) The 100-car naturalistic driving study: phase II—results of the 100-car field experiment. U.S. Department of Transportation, National Highway Traffic Safety Administration. National Highway Traffic Safety Administration, Washington, DCGoogle Scholar
  3. 3.
    Graab B, Donner E, Chiellino U, Hoppe M (2008) Analyse von Verkehrsunfällen hinsichtlich unterschiedlicher Fahrerpopulationen und daraus ableitbarer Ergebnisse für die Entwicklung adaptiver Fahrerassistenzsysteme. Tagung aktive Sicherheit, MünchenGoogle Scholar
  4. 4.
    Ma Y, Fu R (2015) Research and development of drivers visual behavior and driving safety. China J Highw Transp 28(6):82–92MathSciNetGoogle Scholar
  5. 5.
    Yekhshatyan L (2010) Detecting distraction and degraded driver performance with visual behavior metrics. The University of Iowa, Iowa CityCrossRefGoogle Scholar
  6. 6.
    Liu ZF, Fu R, Cheng WD, Wu FW (2015) Overview of researches on drivers’ visual distraction and cognitive distraction. Chin Saf Sci J 25(7):29–34Google Scholar
  7. 7.
    Massel L, Harbluk J (2006) The impact of performing cognitive tasks on drivers’ braking behavior. In: Proceeding of the Canadian multidisciplinary road safety conference XII, pp 1–8Google Scholar
  8. 8.
    Li PF, Wang DH, Liu DB, Wang JJ (2010) Effect of using cell phone while driving on mental workload and driving behavior. J Transp Inf Saf 28(4):103–107Google Scholar
  9. 9.
    Jiang QY, Li J, Cheng PP (2016) Investigation of the safety awareness of the influential factors among the construction workers. J Saf Env 16(6):174–178Google Scholar
  10. 10.
    Arien C, Jongen EMM, Brijs K et al (2013) A simulator study on the impact of traffic calming measures in urban areas on driving behavior and workload. Accid Anal Prev 61(6):43–53CrossRefGoogle Scholar
  11. 11.
    Xu CC, Liu P, Wang W, Jiang X (2012) Discriminant analysis based method to develop real-time crash indicator for evaluating freeway safety. J Southeast Univ (Nat Sci Ed) 42(3):555–559Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School Architecture and Transportation EngineeringGuilin University of Electronic TechnologyGuilinChina
  2. 2.Intelligent Transportation Systems Research CenterWuhan University of TechnologyWuhanChina
  3. 3.School of Mathematics & Computing ScienceGuilin University of Electronic TechnologyGuilinChina

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