Advertisement

Risk Identification of In-Vehicle Information System Operation Based on Traffic Environment Complexity

  • Yanli Ma
  • Luyang Fan
  • Gaofenga Gu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 503)

Abstract

In order to reduce driver distraction and its traffic safety problems caused by the operation of in-vehicle information systems, carried out IVIS (In-vehicle information system) operation risk identification based on different traffic environment conditions. In this chapter, on the basis of traffic environment complexity definition, designed the driving distraction experiments under different traffic conditions, the vehicle, road video scene, and driving conditions data were collected in the experiment in which forty participants interacted with an IVIS while driving. Combined with IVIS operation time and the traffic environment information, build operation risk identification model, determined the safety risks of different IVIS operations. The IVIS operation suggestions under different environment are presented. Results show that the model can accurately identify the safety risks of the IVIS operating under different environmental conditions. When traffic environment complexity is higher, some vehicle information system operations need to be warned or banned. Potential applications of this chapter include the strategy of driving behavior intervention and the evaluation of driver distraction.

Keywords

IVIS Driver distraction Traffic environment Safety risk identification Intervention strategies 

Notes

Acknowledgments

This work was completed as part of the National Science Foundation of China Program “Driver Multi-channel Distracted Characteristics Based on IVIS and Its Effects on Driving Performance (51108136)”. The authors would like to thank the reviewers and the graduate students of the Institute of Traffic Engineering for their valuable comments.

References

  1. 1.
    Birrell SA, Young MS (2011) The impact of smart driving aids on driving performance and driver distraction. Transp Res Part F Traffic Psychol Behav 14(6):484–493CrossRefGoogle Scholar
  2. 2.
    Brodsky W, Slor Z (2013) Background music as a risk factor for distraction among young-novice drivers. Accid Anal Prev 2013(59):382–393CrossRefGoogle Scholar
  3. 3.
    Green P (1999) The 15-second rule for driver information systems. In: Proceedings of the ITS America ninth annual meetingGoogle Scholar
  4. 4.
    Gu GF (2014) Research on the impact of IVIS distraction on driving performance. Harbin Institute of Technology, HarbinGoogle Scholar
  5. 5.
    Hagiwara T, Sakakima R, Hashimoto T et al. (2013) Effect of distraction on driving performance using touch screen while driving on test track. In: Intelligent Vehicles Symposium (IV), 2013 IEEE, pp 1149–1154Google Scholar
  6. 6.
    Janke MK (1994) Age-related disabilities that may impair driving and their assessment. California State Department of Motor Vehicles, National Highway Safety Administration, SacramentoGoogle Scholar
  7. 7.
    Ma YL, Leng X, Qi L (2014) Study of characteristics of phone distraction on drivers and its influence on traffic safety operation. Appl Mech Mater 505–506:1093–1096 (Trans Tech Publications, Switzerland 2014)Google Scholar
  8. 8.
    Ma YL, Gu GF, Gao YE et al (2016) Driver distraction judging model under in-vehicle information system operation based on driving performance. China J Highw Transport 29(4):123–129Google Scholar
  9. 9.
    Mitsopoulos-Rubens E, Trotter MJ, Lenné MG (2011) Effects on driving performance of interacting with an in-vehicle music player: a comparison of three interface layout concepts for information presentation. Appl Ergonomics 42(4):583–591CrossRefGoogle Scholar
  10. 10.
    Ranney TA, Baldwin GH, Smith LA et al (2013) Driver behavior during visual-manual secondary task performance: occlusion method versus simulated drivingGoogle Scholar
  11. 11.
    Ranney TA, Harbluk JL, Noy YI (2005) Effects of voice technology on test track driving performance: Implications for driver distraction. Hum Factors: J Hum Factors Ergon Soc 47(2):439–454CrossRefGoogle Scholar
  12. 12.
    Reed-Jones J, Trick LM, Matthews M (2010) Testing assumptions implicit in the use of the 15-second rule as an early predictor of whether an in-vehicle device produces unacceptable levels of distraction. Accid Anal Prev 2010(40):628–634Google Scholar
  13. 13.
    Regan M (2005) Driver distraction: reflection on the past, present and future. J Australas Coll Road Saf 12(2):22–33Google Scholar
  14. 14.
    Salvucci DD, Markley D, Zuber M et al (2007) iPod distraction: effects of portable music-player use on driver performance. In: Proceedings of the SIGCHI conference on Human factors in computing systems. ACM, pp 243–250Google Scholar
  15. 15.
    Weinberg G, Harsham B, Forlines C et al (2010) Contextual push-to-talk: shortening voice dialogs to improve driving performance. In: Proceedings of the 12th international conference on Human computer interaction with mobile devices and services. ACM, pp 113–122Google Scholar
  16. 16.
    Westat (2008) Driver distraction expert working group meeting summary and proceedings. National Highway Traffic Safety AdministrationGoogle Scholar
  17. 17.
    Wu GC, L Y, Yang XW (2008) Support vector machine classifier based on fuzzy partition and neighborhood pairs. Comput Appl 28(1):131–133zbMATHGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Transportation Science and EngineeringHarbin Institute of TechnologyHarbinChina
  2. 2.Chengdu Engineering Corporation LimitedChengduChina
  3. 3.Guangzhou Transport Planning Research of InstituteGuangzhouChina

Personalised recommendations