Human-Centric Detection and Mitigation Approach for Various Levels of Cell Phone-Based Driver Distractions

abstract: Driving a vehicle is a complex task that typically requires several physical interactions and mental tasks. Inattentive driving takes a driver’s attention away from the primary task of driving, which can endanger the safety of driver, passenger(s), as well as pedestrians. According to several traffic safety administration organizations, distracted and inattentive driving are the primary causes of vehicle crashes or near crashes. In this research, a novel approach to detect and mitigate various levels of driving distractions is proposed. This novel approach consists of two main phases: i.) Proposing a system to detect various levels of driver distractions (low, medium, and high) using a machine learning techniques. ii.) Mitigating the effects of driver distractions through the integration of the distracted driving detection algorithm and the existing vehicle safety systems. In phase- 1, vehicle data were collected from an advanced driving simulator and a visual based sensor (webcam) for face monitoring. In addition, data were processed using a machine learning algorithm and a head pose analysis package in MATLAB. Then the model was trained and validated to detect different human operator distraction levels. In phase 2, the detected level of distraction, time to collision (TTC), lane position (LP), and steering entropy (SE) were used as an input to feed the vehicle safety controller that provides an appropriate action to maintain and/or mitigate vehicle safety status. The integrated detection algorithm and vehicle safety controller were then prototyped using MATLAB/SIMULINK for validation. A complete vehicle power train model including the driver’s interaction was replicated, and the outcome from the detection algorithm was fed into the vehicle safety controller. The results show that the vehicle safety system controller reacted and mitigated the vehicle safety status-in closed loop real-time fashion. The simulation results show that the proposed approach is efficient, accurate, and adaptable to dynamic changes resulting from the driver, as well as the vehicle system. This novel approach was applied in order to mitigate the impact of visual and cognitive distractions on the driver performance.Dissertation/ThesisDoctoral Dissertation Applied Psychology 201

Killer Cell Phones and Complacent Companies: How Apple Fails to Cure Distracted Driving Fatalities

With an astounding 1.6 million car crashes occurring each year due to cell phone use while driving, it is clear that the United States is suffering from a serious epidemic of pervasive cell phone use while driving. Although a majority of Americans clearly understand the hazards and dangers involved in texting while driving, cell phone addiction continues to keep drivers glued to their phones. Apple has a tool at its disposal to ensure that drivers no longer use their cell phones while they are driving, yet it has failed to implement its technology. Apple\u27s Driver Handheld Computing Device Lock-Out patent, granted in April 2014, would disable all distracting functions on a driver\u27s phone through a lock-out mechanism. As one of the world\u27s greatest social influencers, Apple has the power and the responsibility to change the culture behind texting and driving, and implementation of its patent would be a great step toward eliminating deadly distracted driving caused by cell phone use. Because people are dependent on and addicted to their cell phones, it is irrational to believe that cell phone owners can, or will, take the initiative to stop using their cell phones while driving. And studies have shown that public service announcements and state bans and enforcement efforts largely have not helped. For this reason, the onus should be placed on the federal government to force Apple and other phone manufacturers to implement life-saving lock-out technology. Both automobile and cell phone manufacturers have the means to change the way we drive for the better, and with the help of the federal government, these new safety requirements that disable drivers’ cell phones when in a moving car can finally be realized. While Apple has exacerbated the distracted driving problem by creating the smartphone, the powerful tech giant has also created the solution. It is time Apple puts its solution to use

Automatic driver distraction detection using deep convolutional neural networks

Recently, the number of road accidents has been increased worldwide due to the distraction of the drivers. This rapid road crush often leads to injuries, loss of properties, even deaths of the people. Therefore, it is essential to monitor and analyze the driver's behavior during the driving time to detect the distraction and mitigate the number of road accident. To detect various kinds of behavior like- using cell phone, talking to others, eating, sleeping or lack of concentration during driving; machine learning/deep learning can play significant role. However, this process may need high computational capacity to train the model by huge number of training dataset. In this paper, we made an effort to develop CNN based method to detect distracted driver and identify the cause of distractions like talking, sleeping or eating by means of face and hand localization. Four architectures namely CNN, VGG-16, ResNet50 and MobileNetV2 have been adopted for transfer learning. To verify the effectiveness, the proposed model is trained with thousands of images from a publicly available dataset containing ten different postures or conditions of a distracted driver and analyzed the results using various performance metrics. The performance results showed that the pre-trained MobileNetV2 model has the best classification efficiency. © 2022 The Author(s

JAMA

20132013-12-27T00:00:00ZR01 HD074594/HD/NICHD NIH HHS/United StatesR21CE001820/CE/NCIPC CDC HHS/United StatesR49CE002109/CE/NCIPC CDC HHS/United StatesU54GM104942/GM/NIGMS NIH HHS/United States23462782PMC3873772884

Analysis and development of a novel algorithm for the in-vehicle hand-usage of a smartphone

Smartphone usage while driving is unanimously considered to be a really dangerous habit due to strong correlation with road accidents. In this paper, the problem of detecting whether the driver is using the phone during a trip is addressed. To do this, high-frequency data from the triaxial inertial measurement unit (IMU) integrated in almost all modern phone is processed without relying on external inputs so as to provide a self-contained approach. By resorting to a frequency-domain analysis, it is possible to extract from the raw signals the useful information needed to detect when the driver is using the phone, without being affected by the effects that vehicle motion has on the same signals. The selected features are used to train a Support Vector Machine (SVM) algorithm. The performance of the proposed approach are analyzed and tested on experimental data collected during mixed naturalistic driving scenarios, proving the effectiveness of the proposed approach

Twitter as a tool to warn others about sobriety checkpoints: A pilot observational study.

Anecdotal evidence suggests that young people use the website Twitter as a tool to warn drivers about the locations of sobriety checkpoints. Researchers investigated this claim by independently analyzing the website’s content regarding a sample of 10 sobriety checkpoints that were conducted in cities throughout the United States during the weekend of August 26, 2011. Researchers discovered that Twitter content either described one’s experience driving through a checkpoint or acted as a warning to others regarding the exact location of a checkpoint. In the study’s sample, there was over six times as many warnings as compared to experiences posted on Twitter. The warnings, 81 in total, reached an audience of over 64,000 people. The majority of warnings were made by males and by young people between the ages of 20 to 29 years old. Implications, limitations, and suggestions for future research are described

Driver Engagement In Secondary Tasks: Behavioral Analysis and Crash Risk Assessment

Distracted driving has long been acknowledged as one of the leading causes of death or injury in roadway crashes. The focus of past research has been mainly on the change in driving performance due to distracted driving. However, only a few studies attempted to predict the type of distraction based on driving performance measures. In addition, past studies have proven that driving performance is influenced by the drivers’ socioeconomic characteristics, while not many studies have attempted to quantify that influence. In essence, this study utilizes the rich SHRP 2 Naturalistic Driving Study (NDS) database to (a) develop a model for detecting the likelihood of a driver’s involvement in secondary tasks from distinctive attributes of driving performance, and (b) develop a grading system to quantify the crash risk associated with socioeconomic characteristics and distracted driving. The results show that the developed neural network models were able to detect the drivers’ involvement in calling, texting, and passenger interaction with an accuracy of 99.6%, 99.1%, and 100%, respectively. These results show that the selected driving performance attributes were effective in detecting the associated secondary tasks with driving performance. On the other hand, the grading system was developed by three main parameters: the crash risk coefficient, the significance level coefficient, and the category contribution coefficient. At the end, each driver’s crash risk index could be calculated based on his or her socioeconomic characteristics. The developed detection models and the systematic grading process could assist the insurance company to identify a driver’s probability of conducting distracted driving and assisting the development of cellphone banning regulation by states’ Departments of Transportation

Integration of body sensor networks and vehicular ad-hoc networks for traffic safety

The emergence of Body Sensor Networks (BSNs) constitutes a new and fast growing trend for the development of daily routine applications. However, in the case of heterogeneous BSNs integration with Vehicular ad hoc Networks (VANETs) a large number of difficulties remain, that must be solved, especially when talking about the detection of human state factors that impair the driving of motor vehicles. The main contributions of this investigation are principally three: (1) an exhaustive review of the current mechanisms to detect four basic physiological behavior states (drowsy, drunk, driving under emotional state disorders and distracted driving) that may cause traffic accidents is presented; (2) A middleware architecture is proposed. This architecture can communicate with the car dashboard, emergency services, vehicles belonging to the VANET and road or street facilities. This architecture seeks on the one hand to improve the car driving experience of the driver and on the other hand to extend security mechanisms for the surrounding individuals; and (3) as a proof of concept, an Android real-time attention low level detection application that runs in a next-generation smartphone is developed. The application features mechanisms that allow one to measure the degree of attention of a driver on the base of her/his EEG signals, establish wireless communication links via various standard wireless means, GPRS, Bluetooth and WiFi and issue alarms of critical low driver attention levels.Peer ReviewedPostprint (author's final draft

An Investigation of Patterns of Adolescent Driving Behaviors Resulting in Fatal Crashes and Their Implications on Policy

The purpose of this quantitative study was to investigate whether there is a statistical relationship between accident-related factors including use of drugs or alcohol, speeding, driver distractions, gender, driver drowsiness, practice of dysfunctional driving maneuvers, and use of occupant protection devices, and fatal vehicle crashes among young teen drivers. Secondary archival data from 84 North Carolina crashes occurring between 2009 and 2013 and involving young teen drivers between the ages of 15 and 18 years were obtained from North Carolina Department of Motor Vehicles Form 349 crash reports. These data were analyzed using chi-square tests for goodness-of-fit, chi-square tests for independence, and z-tests for proportions. The study found statistically significant associations between gender (p \u3c.019), speeding (p \u3c .001), practice of dysfunctional driving maneuvers (p \u3c .001), and non-use of occupant protection devices (p \u3c .001) and teen crash fatalities. The implications of this study for positive social change include recommendations to the State of North Carolina to enact legislative action related to driver education for new drivers, with the anticipated result of reducing traffic fatalities when a teenage driver is involved in an accident. In order to counteract deadly dysfunctional driving maneuvers on the part of young teen drivers, it was recommended that State driver education curricula be expanded to include exposure to more real world, on-the-road supervised driving experience conducted under more varied conditions and that high school driver education facilities be upgraded to include skid pads for student driving practice. Further research relating to the supervised implementation and verification of the requirement of the 50 hours of adult-supervised driving experience for Graduated Driver Licensure was also recommended