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Studies that explore integration of visual, auditory or vestibular cues, are derived from stimulus detection and discrimination tasks in which stimuli are selective and controlled. Multisensory processing is not as well understood in more dynamic and realistic tasks such as driving. As visual information is the dominant source of information when controlling a vehicle, we were interested in the contribution of auditory and physical motion (vestibular and proprioceptive) information to vehicle control. The simulated environment consisted of a straight, two-lane road and the task was to drive in the center of the right lane and maintain a constant speed, slowing down for occasional speed bumps. We examined differences in driving performance under four sets of sensory cues: visual only, visual and auditory, visual and physical motion, and visual, auditory and physical motion. The quality of visual information was manipulated across two experiments. In Experiment 1, participants drove in daylight in sunny weather, providing excellent visual information. In Experiment 2, visual information was compromised by providing dark and stormy weather conditions. In both experiments we observed an advantage of multisensory information, an effect that was enhanced when visual information was compromised. Auditory cues were especially effective in improving driver control. / Thesis / Master of Science (MSc) / Multisensory processing (combining information from different sensory systems) is not well understood in realistic tasks such as driving. A simulated environment consisted of a straight, two-lane road was used for this study. The task was to drive in the center of the right lane and maintain a constant speed, slowing down for occasional speed bumps. We examined differences in driving performance under four sets of sensory cues: visual only, visual and auditory, visual and physical motion, and visual, auditory and physical motion. The visual information was manipulated across two experiments: first, participants drove in daylight in sunny weather, providing excellent visual information. Next, visual information was compromised by providing dark and stormy weather conditions. In both experiments we observed an advantage of multisensory information, an effect that was enhanced when visual information was compromised. Auditory cues were especially effective in improving driver control.

Anthropogenic sound in Florida's waters and coastal waterways is most commonly caused by overwater development, marine traffic, and military activity. Overwater construction has increased over the years as a result of aging infrastructure and rising expansions around the United States, including more than forty US Naval facilities containing tens of thousands of feet of pier. Construction methodology, such as pile driving, has risen in shallow waters to build structures such as bridges, piers, and wind farms, with significant consequences for marine life and the environment. More precisely, pile driving activities generate significant decibel levels in the surrounding marine environment. Measurements taken from hydrophones placed in the water near the construction site indicate that the high sound pressure levels produced may be harmful to marine life and the environment. As a result, standards have been established to help alleviate and decrease the possible harm that high decibel sound levels may produce. However, these additional steps increase the overall cost of the construction project. This thesis focuses on replicating the pile driving process using finite element modeling to hydroacoustic parametric study of pile driving-induced anthropogenic sound in neighboring Florida seas, as well as the possible environmental impact of the state's numerous naval base piers. The modeling predictions can then be used to identify the distance from the pile at which marine life and the environment are no longer adversely affected. In addition, computer modeling can reduce construction costs when compared to on-site sensors and monitoring. / Master of Science / Over recent years there has been an increase in the amount of manmade noise in Florida and its coastal waterways due to overwater construction, marine traffic, and military activities. Pile driving construction has increased in shallow waters to build infrastructure, which includes bridges, piers, and wind farms, resulting in a negative impact for marine life and the environment. Federal agencies have established guidelines to ease the harmful effects construction has on marine life and the environment. However, there is concern that these recent guidelines may not properly consider all the geometric and hydrographic variables of manmade noise that affect the high sound exposure levels during pile driving. With a more accurate understanding of the sound generation produced from pile driving, predictions can assist with sound mitigation to ensure less harm to the marine life and environment. In turn, construction companies and government agencies informed with this enhanced understanding can make better decisions that lead to fewer (or possibly eliminate) transmission loss discrepancies and costly noise mitigation measures. Consideration of the marine environment is one of the United States Navy's top priorities with naval stations located throughout the State of Florida that possess thousands of feet of waterfront structures, including piers, requiring routine maintenance and construction. This thesis models the pile driving process through finite element modeling in COMSOL Multiphysics computer software, testing the various parameters that Florida waters may encounter with pile driving on the surrounding coast as well as naval bases.

Altering driver's goals and motives for at-risk driving is likely to reduce the frequency of at-risk driving behaviors and their associated crashes and injuries. However, most driving occurs when people are alone with little supervisions or accountability. Thus, a self-management for safety (SMS) intervention may be the most appropriate technique to decrease at-risk driving behaviors. The current research evaluated an SMS process with college students on a simulated driving task. Participants included 93 university students (41 males, 52 females) randomly assigned to one of three groups (31 participants per group). Participants in the Control group did not receive any of the intervention materials; they were instructed to drive as they normally drive on each trial. Participants in the Self-Monitoring + Objective Feedback group received objective feedback from the experimenter about their actual performance on the target driving behavior as well as personal feedback from their self-monitoring forms. These participants recorded their individual improvement goals on the targeted driving behavior. Participants in the Self-Monitoring group recorded their individual improvement goals on the targeted driving behavior, but received only personal feedback from their self-monitoring forms. Similar to past self-management interventions directed at increasing safety-related driving behavior (Hickman & Geller, in press; Krause, 1997; Olson & Austin, 2001), SMS led to clear improvement in subsequent safety performance. Based on the recorded driving behaviors of 93 participants, SMS was effective in increasing the mean percentage of total driving time traveling below the posted speed limit compared to a Control group that did not receive any of the SMS components. Across the four trials, participants in the SM and SM + OFB group significantly increased the percentage of total driving time traveling below the posted speed limit by 13.4 (18.3%) and 14.5 (19.8%) percentage points, respectively, compared to participants in the Control group. / Ph. D.

Intelligent transportation systems data collected on drivers who presumably participated in a study of cognitive mapping and way-finding were evaluated with two basic procedures for data coding, including analysis of video data based on the occurrence or non-occurrence of a) critical behaviors during consecutive 15 second intervals of a driving trial, and b) the safe alternative when a safe behavior opportunity was available. Methods of data coding were assessed for practical use, reliability, and sensitivity to variation in driving style. A factor analysis of at-risk driving behaviors identified a cluster of correlated driving behaviors that appeared to share a common characteristic identified as aggressive/impatient driving. The relationship between personality and driving style was also assessed. That is, analysis of the demographics and personality variables associated with the occurrence of at-risk driving behaviors revealed that driver Age and Type A personality characteristics were significant predictors of vehicle speed and following distance to the preceding vehicle. Results are discussed with regard to implications for safe driving interventions and problem behavior theory. / Ph. D.

The dissertation aims to classify drivers based on driving and environmental behaviors. The research determined significant factors using factor analysis, identified different driver types using k-means clustering, and studied how the same drivers map in each classification domain. The research consists of two study cases. In the first study case, a new variable is proposed and then is used for classification. The drivers were divided into three groups. Two alternatives were designed to evaluate the environmental impact of driving behavior changes. In the second study case, two types of data sets were constructed: driving data and environmental data. The driving data represents driving behavior of individual drivers. The environmental data represents emissions and fuel consumption estimated by microscopic energy and emissions models. Significant factors were explored in each data set using factor analysis. A pair of factors was defined for each data set. Each pair of factors was used for each k-means clustering: driving clustering and environmental clustering. Then the factors were used to identify groups of drivers in each clustering domain. In the driving clustering, drivers were grouped into three clusters. In the environmental clustering, drivers were clustered into two groups. The groups from the driving clustering were compared to the groups from the environmental clustering in terms of emissions and fuel consumption. The three groups of drivers from the driving clustering were also mapped in the environmental domain. The results indicate that the differences in driving patterns among the three driver groups significantly influenced the emissions of HC, CO, and NOx. As a result, it was determined that the average target operating acceleration and braking did essentially influence the amount of emissions in terms of HC, CO, and NOx. Therefore, if drivers were to change their driving behavior to be more defensive, it is expected that emissions of HC, CO, and NOx would decrease. It was also found that spacing-based driving tended to produce less emissions but consumed more fuel than other groups, while speed-based driving produced relatively more emissions. On the other hand, the defensively moderate drivers consumed less fuel and produced fewer emissions. / Ph. D.

Researchers have postulated that the implementation of driving automation systems could reduce the prevalence of driver errors, or at least mitigate the severity of their consequences. While driving automation systems are becoming increasingly common on new vehicles, drivers seem to know very little about them. The following dissertation describes an investigation of driver behavior and behavioral adaptation while using driving automation systems in order to improve consumer education and training. This dissertation uses data collected from test track environments and two naturalistic driving studies, the Virginia Connected Corridor 50 (VCC50) Vehicle Naturalistic Driving Study and the NHTSA Level 2 Naturalistic Driving Study (L2 NDS), to investigate driver behavior with driving automation systems and make suggestions for modifications to current consumer education practices. Results from the test track study indicated that while training strategy elicited limited differences in knowledge and no difference in driver behaviors or attitudes, operator behaviors and attitudes were heavily influenced by time and experience with the driving automation. The naturalistic assessment of VCC50 data showed that drivers tended to activate systems more frequently in appropriate roadway environments. However, drivers spent more time looking away from the road while driving automation systems were active and drivers were more likely be observed browsing on their cell phones while using driving automation systems. The analysis of L2 NDS showed that drivers' time gap preferences changes as drivers gain experience using the driving automation systems. Additionally, driver eye glance behavior was significantly different with automation use and indicated the potential for an adaptive trend with increased exposure to the system for both glances away from the roadway and glances to the instrument panel. The penultimate chapter of this work presents training guidelines and recommendations for consumer education with driving automation systems based on this and other research that has been conducted on driver interaction with driving automation systems. The results of this research indicate that driver training should be a key focus in future efforts to ensure the continued safe use of driving automation systems as they continue to emerge in the vehicle fleet. / Doctor of Philosophy / While driving automation systems are becoming increasingly common on new vehicles, drivers seem to know very little about them. Previous studies have found that owners of vehicles equipped with advanced technologies have demonstrated misperceptions or lack of awareness about system limitations, which may impact driver comfort with and reliance on these systems. Partial driving automation systems are designed to assist drivers in some vehicle operation demands, they are not, however, designed to completely remove the driver from the driving task. The following dissertation describes an investigation of driver behavioral adaptation while using driving automation systems with the goal of improving consumer education and training.

Reducing the number of motor vehicle crashes is one of the major challenges of our times. Current strategies to reduce crash rates can be divided into two groups: identifying risky driving behavior prior to crashes to proactively reduce risk and automating some or all human driving tasks using intelligent vehicle systems such as Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS). For successful implementation of either strategy, a deeper understanding of human driving behavior is essential. This dissertation characterizes human driving behavior through an analysis of a large naturalistic driving study and offers four major contributions to the field. First, it describes the creation of the Surface Accelerations Reference, a catalog of all longitudinal and lateral surface accelerations found in the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS). SHRP 2 NDS is the largest naturalistic driving study in the world with 34.5 million miles of data collected from over 3,500 participants driving in six separate locations across the United States. An algorithm was developed to detect each acceleration epoch and summarize key parameters, such as the mean and maxima of the magnitude, roadway properties, and driver inputs. A statistical profile was then created for each participant describing their acceleration behavior in terms of rates, percentiles, and the magnitude of the strongest event in a distance threshold. The second major contribution is quantifying the effect of several factors that influence acceleration behavior. The rate of mild to harsh acceleration epochs was modeled using negative binomial distribution-based generalized linear mixed effect models. Roadway speed category, driver age, driver gender, vehicle class, and location were used as fixed effects, and a unique participant identifier was as the random effect. Subcategories of each fixed effect were compared using incident rate ratios. Roadway speed category was found to have the largest effect on acceleration behavior, followed by driver age, vehicle class, and location. This methodology accounts for the major influences while simultaneously ensuring that the comparisons are meaningful and not driven by coincidences of data collection. The third major contribution is the extraction of acceleration-based long-term driving styles and determining their relationship to crash risk. Rates of acceleration epochs experienced on ≤ 30 mph roadways were used to cluster the participants into four groups. The metrics to cluster the participants were chosen so that they represent long-term driving style and not short-term driving behavior being influenced by transient traffic and environmental conditions. The driving style was also correlated to driving risk by comparing the crash rates, near-crash rates, and speeding behavior of the participants. Finally, the fourth major contribution is the creation of a set of interactive analytics tools that facilitate quick characterization of human driving during regular as well as safety-critical driving events. These tools enable users to answer a large and open-ended set of research questions that aid in the development of ADAS and ADS components. These analytics tools facilitate the exploration of queries such as how often do certain scenarios occur in naturalistic driving, what is the distribution of key metrics during a particular scenario, or what is the relative composition of various crash datasets? Novel visual analytics principles such as video on demand have been implemented to accelerate the sense-making loop for the user. / Doctor of Philosophy / Naturalistic driving studies collect data from participants driving their own vehicles over an extended period. These studies offer unique perspectives in understanding driving behavior by capturing routine and rare events. Two important aspects of understanding driving behavior are longitudinal acceleration, which indicates how people speed up or slow down, and lateral acceleration, which shows how people take turns. In this dissertation, millions of miles of driving data were analyzed to create an open access acceleration database representing the driving profiles of thousands of drivers. These profiles are useful to understand and model human driving behavior, which is essential for developing advanced vehicle systems and smart roadway infrastructure. The acceleration database was used to quantify the effect of various roadway properties, driver demographics, vehicle classification, and environmental factors on acceleration driving behavior. The acceleration database was also used to define distinct driving styles and their relationship to driving risk. A set of interactive analytics tools was developed that leverage naturalistic driving data by enabling users to ask a large set of questions and facilitate open-ended analysis. Novel visualization and data presentation techniques were developed to help users extract deeper insight about driving behavior faster than previously exiting tools. These tools will aid in the development and testing of automated driving systems and advanced driver assistance systems.

It has been found that traveling on the roadways at night is an inherently more dangerous task than driving during the daylight hours. Driving is primarily a visual task, and there are certain situations at night in which vision and safety may be compromised. The effects of glare produced by the headlamps of oncoming vehicles have become an interesting problem to many lighting researchers. Depending upon the opposing lighting design (beam distribution and intensity) and the lighting conditions inside the vehicle, oncoming headlamps can be both visually discomforting and disabling to drivers at night. In recent years, the newer High Intensity Discharge (HID) headlamps have raised some concern because of their increased light output and brighter appearance as opposed to traditional Halogen headlamps. The objective of this study was to evaluate the discomfort and disability glare produced by different oncoming headlamps under two driver light adaptation levels. This study took place on the Smart Road at the Virginia Tech Transportation Institute. During the Discomfort Glare portion, participants drove an experimental vehicle at 20mph past the oncoming headlamps and were asked to rate their overall discomfort with the subjective deBoer scale. The Disability Glare portion involved drivers detecting a static pedestrian in the roadway while approaching each different set of glare headlamps. It was hypothesized that there would be significant differences in detection distance and discomfort glare rating across the different glare headlamp and adaptation level combinations. It was also hypothesized that age would have a significant effect on detection distance, and the subjective ratings. The results of this study revealed many significant main effects and interactions for the discomfort and disability glare portions. The main effect of glare source was the only significant factor for discomfort glare. The main effects of age, glare source and pedestrian location were all significant for the disability portion. In addition, the interaction of pedestrian location and glare source was also significant. Overall, there was no clear relationship between subjective discomfort ratings and objective disability measures. The conclusions of this research will be valuable to the consumer as well as the manufacturers and designers of future headlamps in revealing how glare can affect drivers on the road at night. This information can help guide new designs to maximize forward visibility while minimizing glare. / Master of Science