scholarly journals “Texting & Driving” Detection Using Deep Convolutional Neural Networks

2019 ◽  
Vol 9 (15) ◽  
pp. 2962 ◽  
Author(s):  
José María Celaya-Padilla ◽  
Carlos Eric Galván-Tejada ◽  
Joyce Selene Anaid Lozano-Aguilar ◽  
Laura Alejandra Zanella-Calzada ◽  
Huizilopoztli Luna-García ◽  
...  
Keyword(s):  
Neural Network ◽  
Traffic Safety ◽  
Area Under The Curve ◽  
Highway Traffic ◽  
Deep Convolutional Neural Networks ◽  
Blind Test ◽  
National Highway Traffic Safety ◽  
Safe Driving ◽  
Texting And Driving ◽  
Car Accidents

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.

An Intelligent Lane Markers Recognition and Localization System Using Improved Hough Transform

2011 ◽  
Vol 121-126 ◽  
pp. 1186-1190
Author(s):  
Kamarul Hawari Bin Ghazali ◽  
Rui Xiao ◽  
Jie Ma
Keyword(s):  
Traffic Safety ◽  
Hough Transform ◽  
Highway Traffic ◽  
National Highway Traffic Safety ◽  
Localization System ◽  
Video Information ◽  
Car Accidents ◽  
Optimum Algorithm ◽  
Single Vehicle ◽  
Lane Recognition

According to the U.S. National Highway Traffic Safety Administration, single vehicle road departures result in many serious accidents each year[1]. An intelligent lane markers recognition and localization system can assist vehicles stay in proper location of a lane that will reduce possibility of car accidents correspondingly. A great deal of lane recognition algorithms have been developed over the past several decades. However, reliable detection is still an issue because of variable road face conditions. An optimum algorithm, Probabilistic Hough Transform (PHT) is presented in this paper for intelligent Lane markers recognition which use a fixed camera installed on the vehicle to transmit video information. The result of experiment proved that under inconsistent illumination and a diversity of road conditions, the accuracy and efficiency of developed system have been improved greatly.

scholarly journals Distracted Driver Detection with Deep Convolutional Neural Network

2019 ◽  
Vol 8 (4) ◽  
pp. 6159-6163 ◽  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Traffic Safety ◽  
Network Architecture ◽  
The United States ◽  
Deep Convolutional Neural Network ◽  
World Health ◽  
Highway Traffic ◽  
National Highway Traffic Safety

According to the World Health Organization (WHO), over 1.3 million deaths occur worldwide each year due to traffic accidents alone. This figure elevates traffic mishaps to be the eight leading cause of death. According to another study the United States National Highway Traffic Safety Administration (NHTSA), the major cause of road deaths and injury is distracted drivers. Motivated by recent advancement of deep learning and computer vision in predicting drivers’ behaviour, this paper attempts to investigate the optimal deep learning network architecture to accurately detect distracted drivers over visual feed. Specifically, a thorough evaluation and detailed benchmark comparisons of pretrained deep convolutional neural network is carried out. Results indicate that the proposed VGG16network architecture is capable of achieving 96% accuracy on the test dataset images.

Hybrid Machine Learning Algorithm for Identifying Feature Levels Associated with Safety Critical Events

2021 ◽  
pp. 036119812199962
Author(s):  
Saleh Mousa ◽  
Ragab Mousa ◽  
Amany Fadaly ◽  
Khalid Jamil
Keyword(s):  
Traffic Safety ◽  
Sensation Seeking ◽  
Learning Algorithm ◽  
Traffic Density ◽  
Density Level ◽  
Highway Traffic ◽  
Critical Events ◽  
National Highway Traffic Safety ◽  
Safe Driving ◽  
Safety Critical

According to the National Highway Traffic Safety Administration (NHTSA), about seven million traffic accidents claimed more than 36,560 human lives in the U.S. in 2018 . These statistics have prompted researchers to investigate the driver characteristics associated with safety-critical events (SCE). This paper presents a hybrid CatBoost algorithm for identifying the feature levels associated with SCE. The model accounts for numerous difficulties and drawbacks reported in the literature. The model was trained and validated using the entire set of the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP2-NDS) events (crash/near-crash and normal/baseline). Results indicate that secondary tasks (interacting with object in-vehicle, reaching for objects in the vehicle, pet interaction, cellphone/tablet use, and writing/texting), intersection influence (parking lots/driveway/entrance/exit, uncontrolled intersections, traffic signals, interchanges, and stop signs), income (under $29,000 and $100,000–$149,000), age (16–19 and 20–24), traffic density (level of service C, D, and E/F), high sensation-seeking tendency (scoring 18–35 on a scale of 35), low driving knowledge (scoring 0–8.9 on a 19-point scoring system questionnaire), and gender = female are the feature levels having an association with SCE with a probability varying between 51% and 87%. Results also revealed that passenger interactions, eating/drinking, driving away from intersections or interchanges, being age 70—79, or driving in traffic density = A are more related to safe driving. Consideration of these results can contribute to reducing roadway crashes and improve traffic safety.

A Commented Synopsis of the Report of the Committee for the National Tire Efficiency Study4

2007 ◽  
Vol 35 (2) ◽  
pp. 70-93
Author(s):  
Marion G. Pottinger ◽  
Joseph D. Walter ◽  
John D. Eagleburger
Keyword(s):  
Traffic Safety ◽  
Rolling Resistance ◽  
The United States ◽  
Final Report ◽  
Highway Traffic ◽  
National Academy Of Sciences ◽  
Scrap Tire ◽  
National Highway Traffic Safety ◽  
Fuel Savings ◽  
Time Required

Abstract The Congress of the United States petitioned the Transportation Research Board of the National Academy of Sciences to study replacement passenger car tire rolling resistance in 2005 with funding from the National Highway Traffic Safety Administration. The study was initiated to assess the potential for reduction in replacement tire rolling resistance to yield fuel savings. The time required to realize these savings is less than the time required for automotive and light truck fleet replacement. Congress recognized that other factors besides fuel savings had to be considered if the committee’s advice was to be a reasonable guide for public policy. Therefore, the study simultaneously considered the effect of potential rolling resistance reductions in replacement tires on fuel consumption, wear life, scrap tire generation, traffic safety, and consumer spending for tires and fuel. This paper summarizes the committee’s report issued in 2006. The authors, who were members of the multidisciplinary committee, also provide comments regarding technical difficulties encountered in the committee’s work and ideas for alleviating these difficulties in further studies of this kind. The authors’ comments are clearly differentiated so that these comments will not be confused with findings, conclusions, and recommendations developed by the committee and contained in its final report.

Distribution of Speed and Acceleration on a Tread Wear Course

1981 ◽  
Vol 9 (1) ◽  
pp. 19-25 ◽  
Author(s):  
G. S. Ludwig ◽  
F. C. Brenner
Keyword(s):  
Traffic Safety ◽  
Highway Traffic ◽  
National Highway Traffic Safety ◽  
Level Crossings ◽  
Acceleration Level ◽  
Longitudinal Acceleration ◽  
Acceleration Data ◽  
National Highway

Abstract Belted bias and radial Course Monitoring Tires were run over the National Highway Traffic Safety Administration tread wear course at San Angelo on a vehicle instrumented to measure lateral and longitudinal accelerations, speed, and number of wheel rotations. The data were recorded as histograms. The distribution of speed, the distributions of lateral and longitudinal acceleration, and the number of acceleration level crossings are given. Acceleration data for segments of the course are also given.

The Analysis and Assessment of Test Method for Flammability of an Automotive Headlining Fabric

2011 ◽  
Vol 332-334 ◽  
pp. 1162-1166
Author(s):  
Zhuo Zhang ◽  
Ying Qing Liu ◽  
Zhong Hai Ren ◽  
Jia Zhuang Ma ◽  
Hu Shui Ye
Keyword(s):  
Traffic Safety ◽  
Test Method ◽  
Highway Traffic ◽  
National Highway Traffic Safety ◽  
Test Standards ◽  
Foreign Countries ◽  
Mandatory Standard ◽  
Testing Standards ◽  
Interior Textile ◽  
Automotive Interior

The flammability is one of the most important features about safety for automotive interior material. This paper summarized the testing standards for flammability performed testing on a type of interior textile material made by one of domestic manufacturers, in accordance with the Chart 571.302 Standard No. 302 of the National Highway Traffic Safety Administration of U.S. The complete introduction of national mandatory standard of China in flammability of interior material was introduced and domestic test standards of flammability with those of foreign countries all over world were compared. Finally, this paper proposed possible and would-be necessary parameters based on comprehensiveness of this kind of test due to safer requirement in future.

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