Deep learning method for risk identification of autonomous bus operation considering image data augmentation strategies

References

• Alkheder S, Alrukaibi F, Aiash A. 2020. Risk analysis of traffic accidents’ severities: An application of three data mining models. ISA Trans. 106:213–220. doi:10.1016/j.isatra.2020.06.018
   PubMed Web of Science ®Google Scholar
• Bareiss M, Smith C, Gabler H. 2021. Finding and understanding pedal misapplication crashes using a deep learning natural language model. Traffic Inj Prev. 22(sup1):169–172. doi:10.1080/15389588.2021.1982616
   PubMedGoogle Scholar
• Cafiso S, Graziano A, Pappalardo G. 2013. Road safety issues for bus transport management. Accid Anal Prev. 60:324–333. doi:10.1016/j.aap.2013.06.010
   PubMed Web of Science ®Google Scholar
• Cheng C, Ren J. 2019. A classification method of CNN for numerical data based on radar chart representation. Inf Control. 48(04):429–436.
   Google Scholar
• Jarosch O, Kuhnt M, Bengler K, Paradies S. 2017. It’s out of our hands now! Effects of non-driving related tasks during highly automated driving on drivers’ fatigue. In: International Driving Symposium on Human Factors in Driver Assessment. p. 319–325.
   Google Scholar
• Kingma D, Ba J. 2014. Adam: A method for stochastic optimization. Paper presented at: International Conference on Learning Representations (ICLR); May 7–9; San Diego, CA.
   Google Scholar
• Krizhevsky A, Sutskever I, Hinton G. 2012. ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. 25(2):84–90.
   Google Scholar
• Lee K, Yoon H, Song M, Park K. 2018. Convolutional neural network-based classification of driver’s emotion during aggressive and smooth driving using multi-modal camera sensors. Sensors. 18(4):957. doi:10.3390/s18040957
   PubMed Web of Science ®Google Scholar
• Li X, Sun J. 2018. Signal multiobjective optimization for urban traffic network. IEEE Trans Intell Transp Syst. 99:1–9. doi:10.1109/TITS.2017.2787103
   Google Scholar
• Merriman S, Plant K, Revell K, Stanton N. 2021. What can we learn from automated vehicle collisions? A deductive thematic analysis of five Automated Vehicle collisions. Saf Sci. 141:105320. doi:10.1016/j.ssci.2021.105320
   Web of Science ®Google Scholar
• Naujoks F, Forster Y, Wiedemann K, Neukum A. 2017. A human-machine interface for cooperative highly automated driving. Adv Hum Aspects Transp. 484:585–595.
   Google Scholar
• Noh Y, Kwon O, Yoon Y. 2020. Comparative risk factor analyses on bi-level injury severity of taxi and private car crashes in Seoul, South Korea. Traffic Inj Prev. 21(3):188–194. doi:10.1080/15389588.2019.1710834
   PubMed Web of Science ®Google Scholar
• Paefgen J, Staake T, Thiesse F. 2013. Evaluation and aggregation of pay-as-you-drive insurance rate factors: A classification analysis approach. Decis Support Syst. 56:192–201. doi:10.1016/j.dss.2013.06.001
   Web of Science ®Google Scholar
• Peterson C, Gaugler J. 2021. To speed or not to speed: thematic analysis of American driving narratives. J Safety Res. 78:129–137. doi:10.1016/j.jsr.2021.04.005
   PubMed Web of Science ®Google Scholar
• Shahverdy M, Fathy M, Berangi R, Sabokrou M. 2020. Driver behavior detection and classification using deep convolutional neural networks. Expert Syst Appl. 149:113240. doi:10.1016/j.eswa.2020.113240
   Web of Science ®Google Scholar
• Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. 2014. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res. 15(1):1929–1958.
   Google Scholar
• Stapel J, Mullakkal B, Happee R. 2019. Automated driving reduces perceived workload, but monitoring causes higher cognitive load than manual driving. Transp Res Part F: Traffic Psychol Behav. 60:590–605. doi:10.1016/j.trf.2018.11.006
   Web of Science ®Google Scholar
• Streiffer C, Raghavendra R, Benson T, Srivatsa M. 2018. DarNet: A deep learning solution for distracted driving detection. Paper presented at: the 18th ACM/IFIP/USENIX Middleware Conference; December 22–28, Las Vegas, NV.
   Google Scholar
• Wang Z, Oates T. 2015. Encoding time series as images for visual inspection and classification using tiled convolutional neural networks. Paper presented at: the Workshops at the Twenty-ninth Aaai Conference on Artificial Intelligence, January; Austin, TX.
   Google Scholar
• Yoshitake H, Shino M. 2018. Risk assessment based on driving behavior for preventing collisions with pedestrians when making across-traffic turns at intersections. IATSS Res. 42(4):240–247. doi:10.1016/j.iatssr.2018.02.001
   Web of Science ®Google Scholar
• Zhang W, Li X, Li X. 2020. Deep learning-based prognostic approach for lithium-ion batteries with adaptive time-series prediction and on-line validation. Measurement. 164:108052. doi:10.1016/j.measurement.2020.108052
   Web of Science ®Google Scholar