“Texting &amp; Driving” Detection Using Deep Convolutional Neural Networks

Celaya-Padilla, José M.; Galván-Tejada, Carlos E.; Lozano-Aguilar, Joyce S. A.; Zanella-Calzada, Laura A.; Luna-García, Huizilopoztli; Galván-Tejada, Jorge I.; Gamboa-Rosales, Nadia-Karina; Rodríguez, Alfonso Insuasty; Gamboa-Rosales, Hamurabi

doi:10.3390/app9152962

Cited by 29 publications

(11 citation statements)

References 24 publications

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“…Additionally, their results demonstrated that different conditions, such as recently having a heavy meal, could affect the measuremente [22]; a similar approach was explored by Kulkarni et al [23]. Finally, and related to the topic of ubiquitous approaches to detect unsafely behaviors, Celaya et al [24] presented a system that detects when a subject is texting and driving. The authors used a wide angle camera inside the vehicle to record and analyze the behavior of the driver, detecting the use of a cellphone while driving by means of a deep neuronal network, with an accuracy of 0.89.…”

Section: Introductionmentioning

confidence: 92%

In-Vehicle Alcohol Detection Using Low-Cost Sensors and Genetic Algorithms to Aid in the Drinking and Driving Detection

Celaya-Padilla

Romero-González

Galván-Tejada

et al. 2021

Sensors

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Worldwide, motor vehicle accidents are one of the leading causes of death, with alcohol-related accidents playing a significant role, particularly in child death. Aiming to aid in the prevention of this type of accidents, a novel non-invasive method capable of detecting the presence of alcohol inside a motor vehicle is presented. The proposed methodology uses a series of low-cost alcohol MQ3 sensors located inside the vehicle, whose signals are stored, standardized, time-adjusted, and transformed into 5 s window samples. Statistical features are extracted from each sample and a feature selection strategy is carried out using a genetic algorithm, and a forward selection and backwards elimination methodology. The four features derived from this process were used to construct an SVM classification model that detects presence of alcohol. The experiments yielded 7200 samples, 80% of which were used to train the model. The rest were used to evaluate the performance of the model, which obtained an area under the ROC curve of 0.98 and a sensitivity of 0.979. These results suggest that the proposed methodology can be used to detect the presence of alcohol and enforce prevention actions.

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Section: Introductionmentioning

confidence: 92%

In-Vehicle Alcohol Detection Using Low-Cost Sensors and Genetic Algorithms to Aid in the Drinking and Driving Detection

Celaya-Padilla

Romero-González

Galván-Tejada

et al. 2021

Sensors

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“…In the successive layers, these filters are progressively increasing, finally achieving improved accuracy and the false positive rate is lower. J. M. Celaya-Padilla et al [17] have presented a method which is adapted ubiquitously for the detection of drivers who are distracted by using their cellphones. For this research study, the authors have used a wide-angle camera mounted on a ceiling which is integrated with a deep learning CNN.…”

Section: Recent Literaturementioning

confidence: 99%

HSDDD: A Hybrid Scheme for the Detection of Distracted Driving through Fusion of Deep Learning and Handcrafted Features

Alkinani

Khan

Arshad

et al. 2022

Sensors

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Traditional methods for behavior detection of distracted drivers are not capable of capturing driver behavior features related to complex temporal features. With the goal to improve transportation safety and to reduce fatal accidents on roads, this research article presents a Hybrid Scheme for the Detection of Distracted Driving called HSDDD. This scheme is based on a strategy of aggregating handcrafted and deep CNN features. HSDDD is based on three-tiered architecture. The three tiers are named as Coordination tier, Concatenation tier and Classification tier. We first obtain HOG features by using handcrafted algorithms, and then at the coordination tier, we leverage four deep CNN models including AlexNet, Inception V3, Resnet50 and VGG-16 for extracting DCNN features. DCNN extracted features are fused with HOG extracted features at the Concatenation tier. Then PCA is used as a feature selection technique. PCA takes both the extracted features and removes the redundant and irrelevant information, and it improves the classification performance. After feature fusion and feature selection, the two classifiers, KNN and SVM, at the Classification tier take the selected features and classify the ten classes of distracted driving behaviors. We evaluate our proposed scheme and observe its performance by using the accuracy metrics.

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“…J. Celaya-Padilla et al [101] have proposed a novel ubiquitous oriented methodology to detect distracted drivers who are using cellphones. The authors have mounted a wideangle camera on the roof to compile a video of the driver and then each video is is split into 24 pictures.…”

Section: A Comparative Study Of Driver Distraction Detection Techniqmentioning

confidence: 99%

Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges

2020

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Human drivers have different driving styles, experiences, and emotions due to unique driving characteristics, exhibiting their own driving behaviors and habits. Various research efforts have approached the problem of detecting abnormal human driver behavior with the aid of capturing and analyzing the face of driver and vehicle dynamics via image and video processing but the traditional methods are not capable of capturing complex temporal features of driving behaviors. However, with the advent of deep learning algorithms, a significant amount of research has also been conducted to predict and analyze driver's behavior or action related information using neural network algorithms. In this paper, we contribute to first classify and discuss Human Driver Inattentive Driving Behavior (HIDB) into two major categories, Driver Distraction (DD), Driver Fatigue (DF), or Drowsiness (DFD). Then we discuss the causes and effects of another human risky driving behavior called Aggressive Driving behavior (ADB). Aggressive driving Behavior (ADB) is a broad group of dangerous and aggressive driving styles that lead to severe accidents. Human abnormal driving behaviors DD, DFD, and ADB are affected by various factors including driver experience/inexperience of driving, age, and gender or illness. The study of the effects of these factors that may lead to deterioration in the driving skills and performance of a human driver is out of the scope of this paper. After describing the background of deep learning and its algorithms, we present an in-depth investigation of most recent deep learning-based systems, algorithms, and techniques for the detection of Distraction, Fatigue/Drowsiness, and Aggressiveness of a human driver. We attempt to achieve a comprehensive understanding of HIADB detection by presenting a detailed comparative analysis of all the recent techniques. Moreover, we highlight the fundamental requirements. Finally, we present and discuss some significant and essential open research challenges as future directions.

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“Texting & Driving” Detection Using Deep Convolutional Neural Networks

Cited by 29 publications

References 24 publications

In-Vehicle Alcohol Detection Using Low-Cost Sensors and Genetic Algorithms to Aid in the Drinking and Driving Detection

In-Vehicle Alcohol Detection Using Low-Cost Sensors and Genetic Algorithms to Aid in the Drinking and Driving Detection

HSDDD: A Hybrid Scheme for the Detection of Distracted Driving through Fusion of Deep Learning and Handcrafted Features

Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges

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