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Vehicle safety and road safety are two important issues. They are related to each other and road accidents are mostly caused by driver distraction. Issues related to driver distraction like eating, drinking, talking to a passenger, using IVIS (In-Vehicle Information System) and thinking something unrelated to driving are some of the main reasons for road accidents. Driver distraction can be categorized into 3 different types: visual distraction, manual distraction and cognitive distraction. Visual distraction is when driver's eyes are off the road and manual distraction is when the driver takes one or both hands off the steering wheel and places the hand/s on something that is not related to the driving safety. Cognitive distraction whereas happens when a driver's mind is not on the road. It has been found that cognitive distraction is the most dangerous among the three because the thinking process can induce a driver to view and/or handle something unrelated to the safety information while driving a vehicle. This study proposes a physiological measurement to detect driver cognitive distraction. Features like lips, eyebrows, mouth movement, eye movement, gaze rotation, head rotation and blinking frequency are used for the purpose. Three different sets of experiments were conducted. The first experiment was conducted in a lab with faceLAB cameras and served as a pilot study to determine the correlation between mouth movement and eye movement during cognitive distraction. The second experiment was conducted in a real traffic environment using faceAPI cameras to detect movement on lips and eyebrows. The third experiment was also conducted in a real traffic environment. However, both faceLAB and faceAPI toolkits were combined to capture more features. A reliable and stable classification algorithm called Dynamic Bayesian Network (DBN) was used as the main algorithm for analysis. A few more others algorithms like Support Vector Machine (SVM), Logistic Regression (LR), AdaBoost and Static Bayesian Network (SBN) were also used for comparison. Results showed that DBN is the best algorithm for driver cognitive distraction detection. Finally a comparison was also made to evaluate results from this study and those by other researchers. Experimental results showed that lips and eyebrows used in this study are strongly correlated and have a significant role in improving cognitive distraction detection.

The purpose of the current study was to assess the efficacy of mental models as a predictor of driving outcomes. In contrast to more traditional measures of knowledge, mental models capture the configural property of knowledge, that is, an individual's understanding of the interrelationships that exist among critical concepts within a particular knowledge domain. Given that research has consistently shown the usefulness of mental models for the prediction of performance in a number of settings, it was hypothesized that the development of accurate driving mental models would also play an important role in the prediction of driving outcomes, especially in comparison to traditional measures of driving knowledge—such as the multiple-choice type tests typically required to obtain a driver license. Mental models of 130 college students (52 percent females) between 17 and 21 years-old (M = 18.68, SD = 0.80) were analyzed and compared to a subject matter expert (SME) referent structure using Pathfinder. A statistically significant correlation was found for mental model accuracy and moving violations (r = –.18, p <.05), but not for at-fault crashes. Evidence of incremental validity of mental models over commonly used predictors of moving violations (but not for at-fault crashes) was also found. Exploratory analyses revealed that driving knowledge, general mental ability (GMA), and emotional stability were the best predictors of mental model accuracy. Issues related to the measurement of mental models were extensively addressed. First, statistically significant correlations between GMA and several mental model properties (i.e., accuracy scores, within participant similarity, and within participant correlation) suggest that challenges inherent to the task for eliciting mental models may influence mental model scores which, in turn, may lower mental model reliability estimates. Also, the selection of model components (i.e., terms) and the identification of the "best" reference structure for deriving mental model accuracy scores are undoubtedly critical aspects of mental model-related research. Along with illustrating the decisions made in the context of this particular study, some suggestions for conducting mental model-related research are provided.

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abstract: Safe headway learning plays a core role in driving education. Traditional safe headway education just use the oral and literal methods to educate drivers the concept of safe headway time, while with the limitation of combining drivers subject and situational domains for drivers to learn. This study investigated that whether using ego-moving metaphor to embody driver's self-awareness can help to solve this problem. This study used multiple treatments (ego-moving and time-moving instruction of safe time headway) and controls with pretest experimental design to investigate the embody self-awareness effect in a car-following task. Drivers (N=40) were asked to follow a lead car at a 2-seconds safe time headway. Results found that using embodied-based instructions in safe headway learning can help to improve driver's headway time accuracy and performance stability in the car-following task, which supports the hypothesis that using embodied-based instructions help to facilitate safe headway learning. However, there are still some issues needed to be solved using embodied-based instructions for the drivers' safe headway education. This study serves as a new method for the safe headway education while providing empirical evidence for the embodied theories and their applications. / Dissertation/Thesis / Masters Thesis Applied Psychology 2016

Vehicle crashes remain the leading cause of accidental death and injuries in the United States, claiming tens of thousands of lives and injuring millions of people each year. Many of these crashes occur during nighttime, where a variety of modifiers affect the risk of a crash, primarily through the reduction of object visibility. Furthermore, many of these modifiers also affect the nighttime mobility of older drivers, who avoid driving during the nighttime. Thus, a two-fold need exists for new technologies that enhance night visibility. Two separate studies were completed as part of this research. Study 1 served as a baseline by evaluating visual performance during nighttime driving under clear weather conditions. Visual performance was evaluated in terms of the detection and recognition distances obtained when different vision enhancement systems were used at the Smart Road testing facility. Study 2, also using detection and recognition distances, compared the visual performance of drivers during low visibility conditions (i.e., due to rain) to the risk perception of driving during nighttime under low visibility conditions. These comparisons were made as a function of various vision enhancement systems. The age of the driver and the characteristics of the object presented (e.g., contrast, motion) were variables of interest in both studies. The pivotal contribution of this investigation is the generation of a model describing the relationships between driver characteristics, risk perception, and visual performance in nighttime driving in the context of a variety of standard and prototype vision enhancement systems. Improvement of mobility, especially for older individuals, can be achieved through better understanding of the factors that increase risk perception, identification of systems that improve detection and recognition distances, and consideration of drivers' opinions on possible solutions that improve nighttime driving safety. In addition, this research effort empirically described the night vision enhancement capabilities of 12 different vision enhancement systems during clear and adverse weather environments. / Ph. D.

This study is the first examination on the association between seven types of sleep disorder and driving risk using large-scale naturalistic driving study data involving more than 3,400 participants. Regression analyses revealed that females with restless leg syndrome or sleep apnea and drivers with insomnia, shift work sleep disorder, or periodic limb movement disorder are associated with significantly higher driving risk than other drivers without those conditons. Furthermore, despite a small number of observations, there is a strong indication of increased risk for narcoleptic drivers. The findings confirmed results from simulator and epidemiological studies that the driving risk increases amongst people with certain types of sleep disorders. However, this study did not yield evidence in naturalistic driving settings to confirm significantly increased driving risk associated with migraine in prior research. The inconsistency may be an indication that the significant decline in cognitive performance among drivers with sleep disorders observed in laboratory settings may not nessarily translate to an increase in actual driving risk. Further research is necessary to define how to incentivize drivers with specific sleep disorders to balance road safety and personal mobility. / Master of Science / This study is the first examination on the association between seven types of sleep disorder and driving risk using large-scale naturalistic driving study data involving more than 3,400 participants. The study identified seven sleep disorders - narcolepsy, sleep apnea, insomnia, shift work sleep disorder, restless legs syndrome, periodic limb movement disorder, and migraine among the participants and revealed that that females with restless leg syndrome or sleep apnea and drivers with insomnia, shift work sleep disorder, or periodic limb movement disorder are associated with significantly higher driving risk than other drivers without those conditons. Furthermore, despite a small number of observations, there is a strong indication of increased risk for narcoleptic drivers. The findings confirmed most results from previous simulator and epidemiological studies that the driving risk increased amongst people with certain types of sleep disorders except for those with migraines – there is no evidence showing increased driving risk associated with drivers with migraine. The inconsistency may be an indication that the significant decline in cognitive performance among drivers with sleep disorders observed in laboratory settings may not nessarily translate to an increase in actual driving risk. The public and private sectors can use the results to target their investments in supporting high risk individuals. And physicians now have more representative data on the level of risk in real world driving and thus more able to practice evidence-based medicine in consulting their patients with sleep disorders regarding driving safety and personal mobility.

Objective: In order to introduce automated vehicles on public roads, it is necessary to ensure that these vehicles are safe to operate in traffic. One challenge is to prove that all physically possible variations of situations can be handled safely within the operational design domain of the vehicle. A promising approach to handling the set of possible situations is to identify a manageable number of logical scenarios, which provide an abstraction for object properties and behavior within the situations. These can then be transferred into concrete scenarios defining all parameters necessary to reproduce the situation in different test environments. Methods: This article proposes a framework for defining safety-relevant scenarios based on the potential collision between the subject vehicle and a challenging object, which forces the subject vehicle to depart from its planned course of action to avoid a collision. This allows defining only safety-relevant scenarios, which can directly be related to accident classification. The first criterion for defining a scenario is the area of the subject vehicle with which the object would collide. As a second criterion, 8 different positions around the subject vehicle are considered. To account for other relevant objects in the scenario, factors that influence the challenge for the subject vehicle can be added to the scenario. These are grouped as action constraints, dynamic occlusions, and causal chains. Results: By applying the proposed systematics, a catalog of base scenarios for a vehicle traveling on controlled-access highways has been generated, which can directly be linked to parameters in accident classification. The catalog serves as a basis for scenario classification within the PEGASUS project. Conclusions: Defining a limited number of safety-relevant scenarios helps to realize a systematic safety assurance process for automated vehicles. Scenarios are defined based on the point of the potential collision of a challenging object with the subject vehicle and its initial position. This approach allows defining scenarios for different environments and different driving states of the subject vehicle using the same mechanisms. A next step is the generation of logical scenarios for other driving states of the subject vehicle and for other traffic environments.

This dissertation explores innovative approaches in driving safety through the development of multi-modal learning frameworks that leverage high-frequency, high-resolution driving data and videos to detect safety-critical events (SCEs). The research unfolds across four methodologies, each contributing to advance the field. The introductory chapter sets the stage by outlining the motivations and challenges in driving safety research, highlighting the need for advanced data-driven approaches to improve SCE prediction and detection. The second chapter presents a framework that combines Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) with XGBoost. This approach reduces dependency on domain expertise and effectively manages imbalanced crash data, enhancing the accuracy and reliability of SCE detection. In the third chapter, a two-stream network architecture is introduced, integrating optical flow with TimeSFormer with a multi-head attention mechanism. This innovative combination achieves exceptional detection accuracy, demonstrating its potential for applications in driving safety. The fourth chapter focuses on the Dual Swin Transformer framework, which enables concurrent analysis of video and time-series data, this methodology shows effective in processing driving front videos for improved SCE detection. The fifth chapter explores the integration of corporate labels' semantic meaning into a classification model and introduces ScVLM, a hybrid approach that merges supervised learning with contrastive learning techniques to enhance understanding of driving videos and improve event description rationality for Vision-Language Models (VLMs). This chapter addresses existing model limitations by providing a more comprehensive analysis of driving scenarios. This dissertation addresses the challenges of analyzing multimodal data and paves the way for future advancements in autonomous driving and traffic safety management. It underscores the potential of integrating diverse data sources to enhance driving safety. / Doctor of Philosophy / This dissertation explores new approaches to enhance driving safety by using advanced learning frameworks that combine video data with high-frequency, high-resolution driving information, introducing innovative techniques to predict and detect critical driving events. The introduction chapter outlines the current challenges in driving safety and emphasizes the potential of data-driven methods to improve predictions and prevent accidents. The second chapter describes a method that uses machine learning models to analyze crash data, reducing the need for expert input and effectively handling data imbalances. This approach improves the accuracy of predicting safety-critical events. The third chapter introduces a two-stream network that processes both sensor data and video frames, achieving high accuracy in detecting safety-related driving incidents. The fourth chapter presents a framework that simultaneously analyzes video and time-series data, validated using a comprehensive driving study dataset. This technique enhances the detection of complex driving scenarios. The fifth chapter introduces a hybrid learning approach that improves understanding of driving videos and event descriptions. By combining different learning techniques, this method addresses limitations in existing models. This work tackles challenges in analyzing multimodal data and sets the stage for future advancements in autonomous driving and traffic safety management. It highlights the potential of integrating diverse data types to create safer driving environments.

I have developed time-varying coefficient models for recurrent event data to evaluate the temporal profiles for recurrence rate and covariate effects. There are three major parts in this dissertation. The first two parts propose a mixed Poisson process model with gamma frailties for single type recurrent events. The third part proposes a Bayesian joint model based on multivariate log-normal frailties for multi-type recurrent events. In the first part, I propose an approach based on penalized B-splines to obtain smooth estimation for both time-varying coefficients and the log baseline intensity. An EM algorithm is developed for parameter estimation. One issue with this approach is that the estimating procedure is conditional on smoothing parameters, which have to be selected by cross-validation or optimizing certain performance criterion. The procedure can be computationally demanding with a large number of time-varying coefficients. To achieve objective estimation of smoothing parameters, I propose a mixed-model representation approach for penalized splines. Spline coefficients are treated as random effects and smoothing parameters are to be estimated as variance components. An EM algorithm embedded with penalized quasi-likelihood approximation is developed to estimate the model parameters. The third part proposes a Bayesian joint model with time-varying coefficients for multi-type recurrent events. Bayesian penalized splines are used to estimate time-varying coefficients and the log baseline intensity. One challenge in Bayesian penalized splines is that the smoothness of a spline fit is considerably sensitive to the subjective choice of hyperparameters. I establish a procedure to objectively determine the hyperparameters through a robust prior specification. A Markov chain Monte Carlo procedure based on Metropolis-adjusted Langevin algorithms is developed to sample from the high-dimensional distribution of spline coefficients. The procedure includes a joint sampling scheme to achieve better convergence and mixing properties. Simulation studies in the second and third part have confirmed satisfactory model performance in estimating time-varying coefficients under different curvature and event rate conditions. The models in the second and third part were applied to data from a commercial truck driver naturalistic driving study. The application results reveal that drivers with 7-hours-or-less sleep prior to a shift have a significantly higher intensity after 8 hours of on-duty driving and that their intensity remains higher after taking a break. In addition, the results also show drivers' self-selection on sleep time, total driving hours in a shift, and breaks. These applications provide crucial insight into the impact of sleep time on driving performance for commercial truck drivers and highlights the on-road safety implications of insufficient sleep and breaks while driving. This dissertation provides flexible and robust tools to evaluate the temporal profile of intensity for recurrent events. / PHD / The overall objective of this dissertation is to develop models to evaluate the time-varying profiles for event occurrences and the time-varying effects of risk factors upon event occurrences. There are three major parts in this dissertation. The first two parts are designed for single event type. They are based on approaches such that the whole model is conditional on a certain kind of tuning parameter. The value of this tuning parameter has to be pre-specified by users and is influential to the model results. Instead of pre-specifying the value, I develop an approach to achieve an objective estimate for the optimal value of tuning parameter and obtain model results simultaneously. The third part proposes a model for multi-type events. One challenge is that the model results are considerably sensitive to the subjective choice of hyperparameters. I establish a procedure to objectively determine the hyperparameters. Simulation studies have confirmed satisfactory model performance in estimating the temporal profiles for both event occurrences and effects of risk factors. The models were applied to data from a commercial truck driver naturalistic driving study. The results reveal that drivers with 7-hours-or-less sleep prior to a shift have a significantly higher intensity after 8 hours of on-duty driving and that their driving risk remains higher after taking a break. In addition, the results also show drivers’ self-selection on sleep time, total driving hours in a shift, and breaks. These applications provide crucial insight into the impact of sleep time on driving performance for commercial truck drivers and highlights the on-road safety implications of insufficient sleep and breaks while driving. This dissertation provides flexible and robust tools to evaluate the temporal profile of both event occurrences and effects of risk factors.