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Driver distraction detection using capsule network

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Abstract

With the onset of the new technological age, the distractions caused due to handheld devices have been a major cause of traffic accidents as they affect the decision-making capabilities of the driver and give them less time to react to difficult situations. Often drivers try to multitask which reduces their reaction time leading to accidents which could have been easily avoided if they had been attentive. As such problems are related to the driver’s negligence toward safety, a possible solution is to monitor driver’s behavior and notify if they are distracted. We propose a CapsNet-based approach for detecting the distracted driver which is a novel approach. The proposed method scores perform well on the real-world environment inputs when compared to other famous methods used for the same. Our proposed methods get high scores for all the most commonly used metrics for classification. On the testing set, the proposed method gets an accuracy of 0.90, 0.92 as precision score, 0.90 as recall score and 0.91 as F-measure.

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Abbreviations

CNN:

Convolutional neural network

ANN:

Artificial neural network

MSE:

Mean squared error

SVM:

Support vector machine

RNN:

Recurrent neural network

IMU:

Inertial measurement unit

LSTM:

Long short-term memory

ELM:

Extreme learning machine

KNN:

K-nearest neighbors

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Correspondence to Rachna Jain.

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Jain, D.K., Jain, R., Lan, X. et al. Driver distraction detection using capsule network. Neural Comput & Applic 33, 6183–6196 (2021). https://doi.org/10.1007/s00521-020-05390-9

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