Abstract
A careless driver can endanger their safety and passengers. To address the issue, we propose generative adversarial networks (GANs) to detect the distracted driver and determine the source of his distraction through the learning model. This article uses GAN to analyze distracted or normal driving behaviors. To construct our model, we collect a large of image data and train with multiple parameters to get the best accuracy. Based on the experimental result, our proposed model can produce D_Loss = 0.0391 and G_Loss = 5.7638. Therefore, our model can be an outstanding solution to deal with distracted driver detection problems using a sophisticated approach.
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Wijaya, N., Mulyani, S.H. & Noviadi Prabowo, A.C. DeepDrive: effective distracted driver detection using generative adversarial networks (GAN) algorithm. Iran J Comput Sci 5, 221–227 (2022). https://doi.org/10.1007/s42044-022-00103-y
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DOI: https://doi.org/10.1007/s42044-022-00103-y