Psychology

Mentors: Yusuke Yamani (Psychology), Kun Xie (Civil & Environmental Engineering), Sherif Ishak (Civil & Environmental Engineering)

Multi-vehicle crashes consist of 61% of traffic fatalities in the U.S, and thus it is crucial to understand how drivers interact, negotiate, and respond to other road users. However, pre-programmed ambient traffic in a standard driving simulator may not respond in a realistic way to the driver's actions, and most driving simulators do not allow researchers to directly study the complexity of multiple vehicle crashes. Distributed simulation involves multiple drivers interacting in a single virtual reality environment similar to multi-player video gaming.

Distributed driving simulation has been used to examine pedestrian-to-driver, driver-to-driver, and pedestrian-to-automated vehicle interactions. Yet, distributed driving simulation technology has not been applied to the realm of driving training programs designed to improve the higher order cognitive skills required for road safety. One such skill is latent hazard anticipation, or the ability to anticipate hazards before they materialize. Desktop training modules for latent hazard anticipation has improved performance in young drivers, but even this improved performance is not yet perfect. One possible reason why trained drivers are still unable to anticipate all latent hazards is due to young drivers' inability to comprehend the road environment from the perspective of other road users. For example, if an experienced driver is planning to turn onto a road, but they know that a tall hedge is blocking oncoming traffic's view of their car, they may inch out slowly into the road to ensure that other drivers see them and slow down or stop if necessary.

The objective of this study is to examine whether experience in distributed simulation scenarios, both from the perspective of the driver encountering the hazard and from the driver who constitutes the hazard, improves latent hazard anticipation performance to a greater extent than existing desktop training programs.

Learning Gain: Students will learn how to program latent hazard anticipation scenarios in a driving simulator. In addition, students will gain expertise in running participants, reducing driving behavior data, and analyzing the results.

Mentor: Yusuke Yamani (Psychology)

Advanced Air Mobility (AAM) is conceptualized as the technological development of air transportation systems that enable transportation of goods and passengers within urban and rural regions (e.g., package delivery drones, air-taxis). It is anticipated that AAM operations will incorporate high levels of automation, potentially shifting the human operator's role from an active controller to a passive monitor. The increasingly automated systems in AAM operations will require human operators to sustain attention to perceptually demanding tasks for prolonged periods of time. However, as human operators sustain attention over time, attentional performance degrades, a phenomenon referred to as vigilance decrement. Although previous work on human performance examined vigilance decrement in perceptually demanding tasks, there is scarce work that directly examines the attentional process involved in human-automation teaming. Better understanding of the underlying psychological mechanism responsible for safe operation of air vehicles is necessary for developing an effective training program for future operators of the AAM technology.

REU students involved in this project will examine the effect of time on human-automation teaming in the AAM environment by conducting a human-subjects experiment using the newly developed Human-Autonomy Teaming Task Battery (HATTB) software. As shown in Figure 2, REU students will develop a study utilizing the multi-agent planning (MAP) task in the HATTB to collect human performance data. Upon completion, REU students will analyze human performance data and write a final report.

Learning Gain: The REU student will learn to design an experiment and collect human performance data from a newly developed software application. Also, students will be trained to perform statistical analysis using R programming and write an effective scientific paper.

Mentor: Jing Chen (Psychology)

It is essential for autonomous vehicles (AVs) to communicate with the human driver. For example, the automation may issue warnings about a hazard it has noticed and needs the driver's assistance to take over control of the vehicle to maintain safe driving behavior. Much effort has been made to develop the technologies that enable this type of warning (e.g., detection algorithms). Of equal importance are the related human factors issues (e.g., how to present these warnings effectively based on human characteristics). The warning system should be designed to support safe human-AV interaction. This project will focus on auditory warnings, which can effectively attract a driver's attention while performing visually demanding driving tasks. An auditory warning can be either speech-based containing sematic information or non-speechbased (e.g., a tone). A prior NSF-funded project by Co-PI Chen found that speech-based brief, distinctive sounds (spearcons) yielded better warning recognition than text-to-speech and tone-based warnings (i.e., auditory icons). Based on this prior project, the current project aims to examine how these different types of auditory warnings affect drivers' behavior and performance when interacting with AVs. It is known that autonomous driving may reduce drivers' situation awareness, which may consequently affect drivers' responses to different warnings. In this project, we will use simulated autonomous driving tasks, during which participants will need to respond to various warnings in different forms: text-to-speech, spearcons, and auditory icons. Participant response times to these warnings will be measured, as well as the nature of their responses (i.e., safe vs. unsafe).

Learning Gain: The REU students will gain knowledge of different types of auditory warnings, how to create different audio files as experimental stimuli, implementation of basic programming language in a STISIM driving simulator, as well as usage of proper statistical methods and tools. Moreover, students will be trained on scientific writing, especially on performing literature search based on a topic of interest and on structuring a logically sound manuscript.

Mentor: Michelle Kelley (Psychology)

Since September 11, 2001, over 2.77 million U.S. military members have served on 5.4 million deployments to Iraq, Afghanistan, and surrounding areas. Although combat situations put military members at risk for post-traumatic stress disorder (PTSD), combat also places service members at risk for a distinct form of trauma, moral injury. Combat often places service members in situations that do not present clear-cut actions (e.g., Should I fire at a child soldier?). Although service member's actions may be sanctioned, these actions may shatter their personal beliefs about their own morality and humanity. Many service members are resilient to or able to assimilate these experiences; however, others report inner conflict as they struggle to accept or make meaning from their experiences. This conflict is theorized to result in moral injury; that is, guilt, shame, anger, etc., symptoms. Moral injury is believed to result in excessive alcohol use. Our lab has shown high rates (74.4% for men; 66.0% women) of probable alcohol use disorder among recent-era veterans, and that moral injury symptoms and PTSD are associated with hazardous alcohol use. In the present study, we will collect cross-sectional data on mental health and risky driving behavior and other potentially relevant demographic data (e.g., sex, age, racial/ethnic background, rank, service branch service, miles driven/week, current driver's license, etc.) from recent-era community veterans. We will assess past month risky driving: (1) seat belt use; (2) speeding, and (3) drinking and driving and estimated blood alcohol content. Based on Litz et al.'s (2009) model, we will test whether moral injury symptoms and PTSD are associated with risky driving behaviors, controlling for covariates. We will also control for PTSD to examine the unique effects of moral injury on risky driving.

Learning Gain: The REU students will become familiar with mental health symptoms in recent-era veterans, how to recruit participants, appropriate data cleaning, management, and analytic techniques and interpretation. Students will be trained in scientific writing (e.g., literature searches and drafting a logically sound manuscript for peer review).

Mentor: Abby Braitman (Psychology)

In the U.S, young drivers have the highest number of motor vehicle crash deaths relative to the number of miles they drive. Moreover, young drivers are more likely to talk or text on cell phones when driving. Among drivers ages 19-24, 72% reported talking on cell phones when driving during the past month; 50% reported reading texts or e-mails, and 41% reported sending texts or e-mails. Two epidemiological studies found a four-fold risk of injury or property-damage crash for drivers talking on phones. It is estimated that driver phone use may account for 22% of all police-reported crashes. Although campaigns exist to try to motivate young drivers to stop texting and driving (e.g., "It Can Wait"), little is known about strategies young drivers actually use to curb this behavior (e.g., utilize apps that automatically respond to texts/calls, place the phone out of reach) or other distracted driving behaviors (e.g., set a playlist to prevent playing with the radio). Moreover, the Health Belief Model suggests that perceived seriousness and susceptibility, the target of these campaigns, is only one piece of the puzzle. We have generated a new measure of (a) strategies they actually use or would consider using to curb distracted driving behaviors; and (b) perceived barriers to using these strategies using focus groups, and collected data from young adult drivers. The proposed study will examine the factor structure of the scale and item inclusion, and examine reliability and validity of a final set of items. This will allow researchers to examine self-efficacy and perceived barriers for avoiding distracted driving, thus better predicting likelihood of behavior. This information can be used in future research to develop and adapt more effective programs to prevent distracted driving.

Learning Gain: REU students will learn how to code and analyze questionnaire data. The students will be trained in applying psychological theory (including gaining familiarity with the Health Belief Model), as well as psychometric data analysis, and will write a final report at the end of the project.

Mentor: Bryan Porter (Psychology)

Talking on mobile phones, texting, and listening to music or podcasts while wearing headphones has become ubiquitous on college campuses. These behaviors arguably can distract pedestrians while they make street crossings, placing them at risk for lack of attentive behavior to watch for vehicles failing to yield. In 2017, pedestrians were 16% of national traffic fatalities. Focusing on distracted behaviors that exacerbate risk for pedestrian-vehicle crashes is an important consideration. So is the focus on driver yielding/nonyielding behaviors. Both of these foci generate research questions amenable to a field, direct observation methodology. The protocol will be derived from Dr. Porter's previous work that considered the impact of rectangular rapid flash beacons on crossings/yieldings and the prevalence of pedestrian distractions. Data collectors will observe assigned campus intersections at different times of day/days of week. This work will be long-term follow-ups to the previous studies to provide updated data to campus planners. Key pedestrian variables include: crossing locations (in/out crosswalk); with or against signals; distractions (headphones; texting; eating); looking behavior (for vehicles before crossing); and demographics. Key driver variables include yielding vs. not yielding when expected; distractions (visible handheld mobile use); and demographics.

Learning Gain: Dr. Porter and his advanced student team will teach REU students about field protocols and methodological considerations and train the students to collect data for this project. The project allows students to add variables of interest or use the existing variables to create a personalized dataset producing results to present via conferences or publications.

Mentor: Jing Chen (Psychology)

A Vehicular Networked System consists of numerous autonomous vehicles that exchange information through Vehicle to Vehicle (V2V) communication. System safety and efficiency are often assured under the assumptions that these vehicular technologies are reliable. However, these assumptions are unrealistic in many practical situations. Therefore, there are increasing research interests in bringing humans into the loop to assist autonomous vehicle operations. It is well known that the V2V communication system is fairly unreliable in transmitting and receiving critical information from vehicles, as can be seen in terms of information loss or delay for a significant amount of time. This project will first investigate how the human driver reacts to different levels of temporal information-transmission failure in a V2V communication system. The measurements include (1) the trust level of the human driver on the use of automation technologies in the vehicles and (2) whether the human driver decides to continue using the automation. It is anticipated that the longer the failure of the automation technologies lasts, the lower the trust level that the human driver shows on the automation. The second part of this project is to establish a mathematical model that characterizes the underlying connections between human factors (e.g., trust level, decisions to retain automation) and the automation failure levels. Such a characterization will provide valuable online predictions of the human state that can be used to adaptively adjust the control and communication strategies for Vehicular Networked Systems.

Learning Gain: The REU students will gain the multi-disciplinary knowledge of engineering technology and psychology, especially in the area of vehicle control, communication, and human trust. Moreover, students will be trained on performing literature review, scientific writing as well as hands-on analytical engineering, and psychological skills to solve multi-disciplinary problems.

Mentor: Sherif Ishak (Civil Engineering)

Hurricanes induced severe damage to the areas they hit hours before they make landfall. During these situations, a reliable transportation route is key to maximizing the number of evacuees as well as for post-hurricane rescue efforts. While traffic planning is an important part of a hazard mitigation plan, vehicle performance on roads especially in such harsh environments is a crucial factor that merits attention in the planning process. The goal of this project is to lay a foundation for future research into vehicle performance on roads during harsh hurricane and tropical storm conditions. The study will utilize the new RDS-1000 (driving simulator) and RDS-100 (Desktop Simulator) acquired at ODU. The driving simulator has a single seat cab with three degrees of freedom motion system, virtual dash and center stack displays, and a library of residential, urban, rural, commercial, industrial, highway, intersection and traffic signal control; autonomous, interactive ambient traffic; extensive, interactive scripted vehicle activity; variable roadway friction and weather effects; and data collection definition. The dynamics of the driving simulator can be modified within the SimCreator proprietary software tool which represents a graphical user interface that allows placement and connection of various components including extensive, scripted vehicle activity in C/C++ code components. This will provide a virtual environment that imitates real life scenarios) to reproduce the wind loadings experienced by vehicles during gusty hurricane wind events. This proposed research is exploratory to investigate the effect of wind force conditions on driving behavior. More specifically, the project will investigate how to modify the parameters of the driving simulator to replicate vehicle performance of a passenger vehicle and explore how to convert wind forces to gusty two dimensional wind loadings on vehicles using the driving simulator. An extension of this work can be applied to testing the effect of storm surge and roadway flooding on the risk associated with driving under these conditions.

Learning Gain: The REU students will learn how to develop and program simulated driving scenarios using the driving simulator at ODU. The students will be trained how to build and modify existing built-in scenarios to perform the project work. This involves learning how to do programming with JavaScript and how to perform simulation experiments with human subjects.

Mentor: Norou Diawara (Mathematics and Statistics)

Seat belt use is the most effective safety in a car crash. However, the percent of drivers wearing the seat belt changes. In rural, less dense areas, the seat belt use behavior is different of that in large and medium size cities. There is a myth that for large vehicles the seat belt use is less important. Other features related to the seat belt use include the types of roads, the weather and the travelling speeds. Is the driver more conscious of the seat belt use when a passenger is present or absent? Is there a gender difference associated with the seat belt use? In order to answer such questions and save lives, data on the seat belt use must be analyzed with the proper methodologies. As a binary outcome, the seat belt use must be linked to selected features (such as car type, gender and locations). The analysis can be formulated in two parts: the visualization and the modeling. In doing visualization, graphs and plots that describe the relationships will be presented. Among the methods, generalized linear models will be explored. To inform on the performance, the receiver operator characteristic curve is also evaluated. The dependence of the features under hierarchical structure is proposed. The hurdle and zero inflation models will also be implemented. We will construct spatial patterns where the features are nested within clusters and networks are formed. The project will review many of the latest modeling techniques for seat belt use, a binary data that has been collected over space and time in the last ten years.

Learning Gain: the REU students will learn how to load and manipulate large data in SAS and R, select variables, visualize and analyze binary data. They will review the design and sampling collection. They will perform model comparisons with programming and coding tools. The study will be a way to use data for informed decision makings. The methods learned can be applied to many other research areas, such as in healthcare and disease control. The analysis will include segmentation of geographical behaviors and model adaptations, all written in a final report.

Mentor: Kun Xie (Civil & Environmental Engineering)

The risk of having a secondary crash in the presence of an earlier crash can be six times higher than that without an earlier crash. Reducing the risk of secondary crashes is a key goal for effective traffic incident management. However, only a few countermeasures have been established in practice to achieve this goal. With the proliferation connected vehicle (CV) technologies, it is highly likely that CVs will soon be able to communicate with each other through the ad-hoc wireless vehicular network. Information sharing among vehicles can change driving behaviors to improve safety. For instance, drivers who receive a crash alert would drive with less aggressiveness and more awareness when approaching the crash site, and this may reduce the risk of secondary crashes. This study aims to investigate how the CV's crash alerts can affect the drivers' aggressiveness and awareness in various scenarios differing in weather and traffic conditions. This study will utilize a new driving simulator at ODU, which has three degrees of freedom motion system, center stack (used for alert displays), and a library of various driving scenarios. The impact of when and how to disseminate crash alerts on driving behaviors will also be studied. The outcomes from this study can provide useful insights into the impact of CVs on driving behaviors and assist the development of proactive measures to mitigate secondary crashes.

Learning Gain: The REU students will be trained to develop virtual driving scenarios using the driving simulator at ODU. They will also learn how to collect data on vehicle motion and driving behaviors and quantitative approaches to analyze data.

Mentor: Xiao Yang (Psychology)

Road accidents are a leading cause of death in U.S. among people under age 55, and road traffic crashes cost U.S. $871 billion. Attentional lapses among road users are a major cause of road accidents. It is a general belief that combining any other task (e.g., texting, phone conversations, and music listening) with driving will affect driving performance. However, recent studies showed that multitasking could improve driving performance. Those conflicted findings call for a better understanding of attentional processes during driving.

Physiological measures have been increasingly used to understand drivers' attention. Event-related potential (ERP) that is derived from electroencephalogram provides attention indicators. Particularly, the ERP P300 wave has been proposed to reflect the reciprocity of attentional resources. Moreover, high frequency heart rate variability (HF HRV) that is derived from electrocardiography is thought to reflect cognitive control and mental workload and has been studied in relation to cognitive performance and human errors. Given the noninvasive nature and high temporal resolution of P300 and HF HRV, these physiological indices are promising in research on attention during driving.

The objective of the present project is to investigate the relationship between physiological indices of attention (P300 and HF HRV) and driving performance. This project will include two parts. Part 1 is to establish a multimodal assessment system using standardized laboratory cognitive tasks. Specifically, P300 and HF HRV will be examined during a dual task paradigm, which will be further analyzed in relation to task performance. Part 2 is to utilize wearable device to record P300 and HF HRV in a driving simulator. The validity of the two physiological measures in the simulator will be assessed, and the relationship between the physiological indices and driving behavioral indices (e.g., brake reaction time, time to collision, and standard deviation of lateral position) will be examined.

Learning Gain: Students will obtain hands-on experiences of recording physiological measures and driving performance metrics. Further, students will learn physiological data processing and potentially use computational modeling to analyze behavioral data. In addition, students will gain experience in running experiments, conducting statistical analyses, and reporting research findings.