Abstract
Being alert and cautious on the road is of utmost importance and this makes distraction of the driver on the road a major cause of concern. However, human nature is one that makes humans quite prone to distraction. Driving while being distracted is utterly irresponsible and results in life-altering mistakes for oneself as well as others. One needs to be absolutely vigilant while driving and sometimes even if a person drives sincerely the other person on the road might not do the same resulting in an accident. Hence, developing an efficient system to detect distracted drivers is of prime importance. With this aim, we have developed a convolutional neural network model using Keras library on State Farm Distracted Driver Detection Dataset with a training accuracy of 98.23% and testing accuracy of 97.54%. Using the technique of transfer learning of pre-trained ResNet50 and VGG16 models on the same dataset, comparisons have been drawn between the different models. Logistic regression was also performed on the same dataset and it proved to be comparable to CNN models in terms of accuracy as well as loss with average training and testing accuracy coming up to be 98% and 97%, respectively.
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Dhiman, A., Varshney, A., Hasani, F., Verma, B. (2023). A Comparative Study on Distracted Driver Detection Using CNN and ML Algorithms. In: Saraswat, M., Chowdhury, C., Kumar Mandal, C., Gandomi, A.H. (eds) Proceedings of International Conference on Data Science and Applications. Lecture Notes in Networks and Systems, vol 552. Springer, Singapore. https://doi.org/10.1007/978-981-19-6634-7_47
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