Distracted Driving in Clark County, Nevada: Analysis of an Intervention on College Students

Distracted driving is a growing public health concern. Highlighted in the media, local and government agencies and in peer-review literature are increased associations of motor vehicle crash related injuries and fatalities with distracted driving, especially involving youth drivers. The goal of this thesis was to analyze the effects of a distracted driving intervention on college students at University of Nevada, Las Vegas. Quantitative statistical analysis was performed to compare self-reported pre and post-intervention questionnaire responses of the experimental and control groups. Between-group analysis was performed using independent t-tests and ANOVA. Within-group differences were analyzed with Repeated Measures ANOVA (RM-ANOVA) and Cochran’s Q Chi-square tests. The results indicate an overall observed desired effect of change with statistical significance for the experimental group after the intervention, which was not observed for the control group. There were also statistically significant differences within the experimental group responses in all three themed components of the questionnaire: behavior, attitude, and knowledge. The most interesting finding of this analysis is that a classroom based intervention can have effects on self-reported distracted driving related behaviors, attitudes, and knowledge after two weeks of completing the intervention. These results can inform development of future evidence-based distracted driving intervention programs

“Texting & Driving” Detection Using Deep Convolutional Neural Networks

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.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

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

Leveraging contextual-cognitive relationships into mobile commerce systems

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyMobile smart devices are becoming increasingly important within the on-line purchasing cycle. Thus the requirement for mobile commerce systems to become truly context-aware remains paramount if they are to be effective within the varied situations that mobile users encounter. Where traditionally a recommender system will focus upon the user – item relationship, i.e. what to recommend, in this thesis it is proposed that due to the complexity of mobile user situational profiles the how and when must also be considered for recommendations to be effective. Though non-trivial, it should be, through the understanding of a user’s ability to complete certain cognitive processes, possible to determine the likelihood of engagement and therefore the success of the recommendation. This research undertakes an investigation into physical and modal contexts and presents findings as to their relationships with cognitive processes. Through the introduction of the novel concept, disruptive contexts, situational contexts, including noise, distractions and user activity, are identified as having significant effects upon the relationship between user affective state and cognitive capability. Experimental results demonstrate that by understanding specific cognitive capabilities, e.g. a user’s perception of advert content and user levels of purchase-decision involvement, a system can determine potential user engagement and therefore improve the effectiveness of recommender systems’ performance. A quantitative approach is followed with a reliance upon statistical measures to inform the development, and subsequent validation, of a contextual-cognitive model that was implemented as part of a context-aware system. The development of SiDISense (Situational Decision Involvement Sensing system) demonstrated, through the use of smart-phone sensors and machine learning, that is was viable to classify subjectively rated contexts to then infer levels of cognitive capability and therefore likelihood of positive user engagement. Through this success in furthering the understanding of contextual-cognitive relationships there are novel and significant advances that are now viable within the area of m-commerce

The Influence of Public Policy Interventions on Millennial Distracted Driving Behavior

Despite recent public policy initiatives limiting or banning forms of distracted driving resultant from cellular phone use, crashes remain on the rise. Individuals from the millennial generation, ages 16 to 35, appear to be most susceptible to distracted driving. Understanding the behaviors, attitudes, and habits of millennials is critical to developing effective policy for behavior change. A dual task ethnographic study framed by Skinner\u27s theory of behavior modification and Maslow\u27s hierarchy of needs motivational model, was used to investigate to what extent millennials feel public policy has influenced their driving, and if additional policy initiatives are required to deter distracted driving behavior. Two phases of inquiry, first, naturalistic observation, and then focus group were conducted at a commuter university. Distracted driving behaviors including hand held cellular phone use, eating, drinking, and passenger interaction of 100 drivers entering or exiting campus were observed, tracked, and analyzed using a researcher-developed tracking form. Eighty-four percent exhibited at least one distracted driving behavior. After which, 12 enrolled and licensed students, aged 18-35, were recruited via social media for two focus group discussions. Focus group data were inductively coded and analyzed using semantical attribution analysis. The students revealed that millennial drivers felt distracted driving policy did not address behaviors they see as worthy of intervention, they did not perceive that cellular phone use while driving posed a significant threat, and they felt current law was difficult to enforce with penalties they regarded as non-prohibitive. Social change implications include improved distracted driving public policy, which may result in driving behavior changes and a potential reduction of death, injury, and property loss

Influence of personal mobile phone ringing and usual intention to answer on driver error

Given evidence of effects of mobile phone use on driving, and also legislation, many careful drivers refrain from answering their phones when driving. However, the distracting influence of a call on driving, even in the context of not answering, has not been examined. Furthermore, given that not answering may be contrary to an individual’s normal habits, this study examined whether distraction caused by the ignored call varies according to normal intention to answer whilst driving. That is, determining whether the effect is more than a simple matter of noise distraction. Participants were 27 young drivers (18-29 years), all regular mobile users. A Theory of Planned Behaviour questionnaire examined predictors of intention to refrain from answering calls whilst driving. Participants provided their mobile phone number and were instructed not to answer their phone if it were to ring during a driving simulation. The simulation scenario had seven hazards (e.g. car pulling out, pedestrian crossing) with three being immediately preceded by a call. Infractions (e.g. pedestrian collisions, vehicle collisions, speed exceedances) were significantly greater when distracted by call tones than with no distraction. Lower intention to ignore calls whilst driving correlated with a larger effect of distraction, as was feeling unable to control whether one answered whilst driving (Perceived Behavioural Control). The study suggests that even an ignored call can cause significantly increased infractions in simulator driving, with pedestrian collisions and speed exceedances being striking examples. Results are discussed in relation to cognitive demands of inhibiting normal behaviour and to drivers being advised to switch phones off whilst driving

The Effects of Concurrent Driving and In-Vehicle Tasks: A Multivariate Statistical Analysis of Driver Distraction in a High-Fidelity Driving Simulator

Distracted driving continues to remain a cause of concern for a number of bodies, including government agencies, traffic safety advocacy groups and law enforcement agencies, because of its traffic safety risks. The driving simulator continues to be popular with researchers in collecting data on performance variables that provide scientific knowledge of the effects of distracted driving. Several of these performance variables can be used to quantify a single distracting effect, resulting in a multivariate dataset. A literature review of related studies revealed that researchers overwhelmingly use univariate (single and multiple) tests to analyze the resulting dataset. Performing multiple univariate tests on a multivariate dataset results in inflated Type-I error rates, and could result in inaccurately concluding that there is a distracting effect when there may not be. Researchers also provided very little or no justification for the selection of variables that were used for the univariate analysis. Being able to correctly identify a set of variables to be used to research a single distracting effect is critical in that different variables may lead to different conclusions of significant findings or not. The primary objective of this dissertation was to develop a sound statistical basis for correctly identifying a set of variables and also to demonstrate the benefits of adopting a multivariate gate-keeper test in distracted driving studies. This was demonstrated with an experiment where 67 drivers participated in a repeated measures driving simulator experiment. 14 commonly used performance variables were used as the multivariate response variables. The corresponding data were analyzed using univariate tests, and multivariate gate-keeper tests. The results indicate that ignoring the multivariate structure and performing multiple univariate tests, as has been found to be prevalent in past studies, will lead to inflated Type-I error rates and potentially misleading conclusions. The procedure developed in this study also led to the development of sound statistical basis for the selection of variables that can be best used to account for the distracting effect of the texting and phone call activities that were investigated. The findings of this study have significant educational value to the body of knowledge on distracted driving studies and any other studies that analyze multiple dependent variables for a single factor

ARE DISTRACTED DRIVERS AWARE THAT THEY ARE DISTRACTED?: EXPLORING AWARENESS, SELF-REGULATION, AND PERFORMANCE IN DRIVERS PERFORMING SECONDARY TASKS

Research suggests that driving while talking on a mobile telephone causes drivers not to respond to important events but has a smaller effect on their lane-keeping ability. This pattern is similar to research on night driving and suggests that problems associated with distraction may parallel those of night driving. Here, participants evaluated their driving performance before and after driving a simulated curvy road under different distraction conditions. In experiment 1 drivers failed to appreciate their distraction-induced performance decrements and did not recognize the dissociation between lane-keeping and identification. In Experiment 2 drivers did not adjust their speed to offset being distracted. Continuous feedback that steering skills are robust to distraction may prevent drivers from being aware that they are distracted

Text Messaging and Distracted Driving: Using Voice Dictation to Make Roads Safer

http://deepblue.lib.umich.edu/bitstream/2027.42/98081/1/Shah_lhc489_W2013_muir.pd