Abstract
The effects of distracted driving are one of the main causes of deaths and injuries on U.S. roads. According to the National Highway Traffic Safety Administration (NHTSA), among the different types of distractions, the use of cellphones is highly related to car accidents, commonly known as “texting and driving”, with around 481,000 drivers distracted by their cellphones while driving, about 3450 people killed and 391,000 injured in car accidents involving distracted drivers in 2016 alone. Therefore, in this research, a novel methodology to detect distracted drivers using their cellphone is proposed. For this, a ceiling mounted wide angle camera coupled to a deep learning–convolutional neural network (CNN) are implemented to detect such distracted drivers. The CNN is constructed by the Inception V3 deep neural network, being trained to detect “texting and driving” subjects. The final CNN was trained and validated on a dataset of 85,401 images, achieving an area under the curve (AUC) of 0.891 in the training set, an AUC of 0.86 on a blind test and a sensitivity value of 0.97 on the blind test. In this research, for the first time, a CNN is used to detect the problem of texting and driving, achieving a significant performance. The proposed methodology can be incorporated into a smart infotainment car, thus helping raise drivers’ awareness of their driving habits and associated risks, thus helping to reduce careless driving and promoting safe driving practices to reduce the accident rate.
Highlights
Car manufacturers devote much of their attention to equipping cars with new integrated safety features
The confusion matrix of the convolutional neural network (CNN) is shown in Figure 9, obtaining a sensitivity of 0.81, specificity of 0.95, positive predictive values (PPV) of 0.86 and kappa value of 0.79
For the blind dataset, the confusion matrix of the CNN is shown in Figure 10, obtaining a sensitivity of 0.97, specificity of 0.75, PPV of 0.5479 and kappa value of 0.57
Summary
Car manufacturers devote much of their attention to equipping cars with new integrated safety features. These features include avoiding collisions, pedestrian detection, lane change warning, driver feedback, even semi-autonomous driving, among others. With the advance of technology, the car can infer dangerous behaviors such as drowsiness by using advanced sensors integrated in the vehicle (for example, night cameras, radars, ultrasonic sensors) [1]. Some high-end cars can activate automatic steering when the car moves to another lane without a safe warning (for example, the driver did not turn on the turn signal to indicate a line change), and the car can even brake before dangerously approaching the car ahead by means. Sci. 2019, 9, 2962 of the assistant of automatic braking. Only a small fraction of automobiles present these warning systems to the driver and, even with all these safety features, one of the biggest problems that has not yet been solved is the “distracted” driver, which continues to have significant repercussions throughout the world due to the accidents generated
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