“Texting & Driving” Detection Using Deep Convolutional Neural Networks

• Published in: Applied sciences Vol. 9; no. 15; p. 2962
• Main Authors: Celaya-Padilla, José María, Galván-Tejada, Carlos Eric, Lozano-Aguilar, Joyce Selene Anaid, Zanella-Calzada, Laura Alejandra, Luna-García, Huizilopoztli, Galván-Tejada, Jorge Issac, Gamboa-Rosales, Nadia Karina, Velez Rodriguez, Alberto, Gamboa-Rosales, Hamurabi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 2019
• Subjects: Cellular telephones; convolutional neural network; driver distraction; driver’s behavior detection; Eye movements; Neural networks; smart car; smart cities; smart infotainment; Text messaging; texting and driving; United States > US; Mexico
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app9152962

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.