137
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

An XGBoost approach to detect driver visual distraction based on vehicle dynamics

, &
Pages 458-465 | Received 06 Apr 2023, Accepted 23 May 2023, Published online: 05 Jun 2023

References

  • Atiquzzaman M, Qi Y, Fries R. 2018. Real-time detection of drivers’ texting and eating behavior based on vehicle dynamics. Transp Res Part F: Traffic Psychol Behav. 58:594–604. doi:10.1016/j.trf.2018.06.027
  • Bowden VK, Loft S, Wilson MK, Howard J, Visser TAW. 2019. The long road home from distraction: investigating the time-course of distraction recovery in driving. Accid Anal Prev. 124:23–32. doi:10.1016/j.aap.2018.12.012
  • Chen T, Xgboost GC. 2016. Paper Presented at: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
  • Choudhary P, Velaga NR. 2017. Mobile phone use during driving: effects on speed and effectiveness of driver compensatory behaviour. Accid Anal Prev. 106:370–378. doi:10.1016/j.aap.2017.06.021
  • Deshmukh SV, Omid D. 2019. Characterization and identification of driver distraction during naturalistic driving: an analysis of ECG dynamics. In: Advances in Body Area Networks I: Post-Conference Proceedings of BodyNets 2017. Cham: Springer International Publishing. doi:10.1007/978-3-030-02819-0_1.
  • Khasawneh L, Das M. 2022. Real time estimation of road bank disturbance and vehicle side slip angle. Int J ITS Res. 20(3):759–767. doi:10.1007/s13177-022-00323-3
  • Klauer SG, Guo F, Simons-Morton BG, Ouimet MC, Lee SE, Dingus TA. 2014. Distracted driving and risk of road crashes among novice and experienced drivers. N Engl J Med. 370(1):54–59. doi:10.1056/NEJMsa1204142
  • Hiroaki K, Taku H, Akira Y, Hirotoshi I. 2016. Considering eye movement type when applying random forest to detect cognitive distraction. Paper presented at: 2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI* CC); IEEE.
  • Li Z, Bao S, Kolmanovsky IV, Yin X. 2018. Visual-manual distraction detection using driving performance indicators with naturalistic driving data. IEEE Trans Intell Transport Syst. 19(8):2528–2535. doi:10.1109/TITS.2017.2754467
  • Mayhew DR, Simpson HM, Wood KM, Lonero L, Clinton KM, Johnson AG. 2011. On-road and simulated driving: concurrent and discriminant validation. J Safety Res. 42(4):267–275. doi:10.1016/j.jsr.2011.06.004
  • Monjezi Kouchak S, Gaffar A. 2021. Detecting driver behavior using stacked long short term memory network with attention layer. IEEE Trans Intell Transport Syst. 22(6):3420–3429. doi:10.1109/TITS.2020.2986697
  • National Highway Traffic Safety Administration. 2022. Distracted driving 2020 [Research Note. Report No. DOT HS 813 309] [Accessed 2023 Mar 20]. https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/813309.
  • Osman OA, Hajij M, Karbalaieali S, Ishak S. 2019. A hierarchical machine learning classification approach for secondary task identification from observed driving behavior data. Accid Anal Prev. 123:274–281. doi:10.1016/j.aap.2018.12.005
  • Oviedo-Trespalacios O, Haque MM, King M, Washington S. 2019. Mate! I'm running 10 min late": an investigation into the self-regulation of mobile phone tasks while driving. Accid Anal Prev. 122:134–142. doi:10.1016/j.aap.2018.09.020
  • Papadakaki M, Tzamalouka G, Gnardellis C, Lajunen TJ, Chliaoutakis J. 2016. Driving performance while using a mobile phone: a simulation study of Greek professional drivers. Transp Res Part F: Traffic Psychol Behav. 38:164–170. doi:10.1016/j.trf.2016.02.006
  • Pope CN, Bell TR, Stavrinos D. 2017. Mechanisms behind distracted driving behavior: the role of age and executive function in the engagement of distracted driving. Accid Anal Prev. 98:123–129. doi:10.1016/j.aap.2016.09.030
  • Putatunda S, Rama K. 2018. A comparative analysis of hyperopt as against other approaches for hyper-parameter optimization of XGBoost. Paper presented at: Proceedings of the 2018 International Conference on Signal Processing and Machine Learning. doi:10.1145/3297067.3297080
  • Son J, Park M. 2018. Detection of cognitive and visual distraction using radial basis probabilistic neural networks. Int J Automot Technol. 19(5):935–940. doi:10.1007/s12239-018-0090-4
  • Vetturi D, Tiboni M, Maternini G, Bonera M. 2020. Use of eye tracking device to evaluate the driver’s behaviour and the infrastructures quality in relation to road safety. Transp Res Procedia. 45:587–595. doi:10.1016/j.trpro.2020.03.053
  • Wang J, Chen H. 2013. Driver distraction identification method based on driver performance and vehicle motion trajectory information. Automot Tech. 457(10):14–18.
  • Wu P, Song L, Meng X. 2022. Temporal analysis of cellphone-use-involved crash injury severities: calling for preventing cellphone-use-involved distracted driving. Accid Anal Prev. 169:106625. doi:10.1016/j.aap.2022.106625
  • Yan W, Xiang W, Wong SC, Yan X, Li YC, Hao W. 2018. Effects of hands-free cellular phone conversational cognitive tasks on driving stability based on driving simulation experiment. Transp Res Part F: Traffic Psychol Behav. 58:264–281. doi:10.1016/j.trf.2018.06.023
  • Yang J, Chang TN, Hou E. 2010. Driver distraction detection for vehicular monitoring. Paper presented at: IECON 2010-36th Annual Conference on IEEE Industrial Electronics Society; IEEE.
  • Ye M, Osman OA, Ishak S, Hashemi B. 2017. Detection of driver engagement in secondary tasks from observed naturalistic driving behavior. Accid Anal Prev. 106:385–391. doi:10.1016/j.aap.2017.07.010
  • Zhang S, Abdel-Aty M. 2022. Drivers’ visual distraction detection using facial landmarks and head pose. Transp Res Rec. 2676(9):491–501. doi:10.1177/03611981221087234
  • Zhong Y-J, Du L-P, Zhang K, Sun X-H. 2007. Localized energy study for analyzing driver fatigue state based on wavelet analysis. Paper presented at: 2007 International Conference on Wavelet Analysis and Pattern Recognition; IEEE.
  • Zuo X, Zhang C, Cong F, Zhao J, Hamalainen T. 2022. Driver distraction detection using bidirectional long short-term network based on multiscale entropy of EEG. IEEE Trans Intell Transport Syst. 23(10):19309–19322. doi:10.1109/TITS.2022.3159602

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.