While driver distraction has been extensively studied in laboratory and empirical field studies, the prevalence of driver distraction on our nation's highways and the relative crash risk is unknown. It has recently become technologically feasible to conduct unobtrusive large-scale naturalistic driving studies as the costs and size of computer equipment and sensor technology have both dramatically decreased. A large-scale naturalistic driving study was conducted using 100 instrumented vehicles (80 privately-owned and 20 leased vehicles). This data collection effort was conducted in the Washington DC metropolitan area on a variety of urban, suburban, and rural roadways over a span of 12-13 months. Five channels of video and kinematic data were collected on 69 crashes and 761 near-crashes during the course of this data collection effort. The analyses conducted here are the first to establish direct relationships between driving inattention and crash and near-crash involvement. Relative crash risk was calculated using both crash and near-crash data as well as normal, baseline driving data, for various sources of inattention. Additional analyses investigated the environmental conditions drivers choose to engage in secondary tasks or drive fatigued, assessed whether questionnaire data were indicative of an individual's propensity to engage in inattentive driving, and examined the impact of driver's eyes off the forward roadway. The results indicated that driving inattention was a contributing factor in 78% of all crashes and 65% of all near-crashes. Odds ratio calculations indicated that fatigued drivers have a 4 times higher crash risk than alert drivers. Drivers engaging in visually and/or manually complex tasks are at 7 times higher crash risk than alert drivers. There are specific environmental conditions in which engaging in secondary tasks or driving fatigued is deemed to be more dangerous, including intersections, wet roadways, undivided highways, curved roadways, and driving at dusk. Short, brief glances away from the forward roadway for the purpose of scanning the roadway environment (e.g., mirrors and blind spots) are safe and decrease crash risk, whereas such glances that total more than 2 seconds away from the forward roadway are dangerous and increase crash risk by 2 times over that of more typical driving. / Ph. D.

This study was conducted in an attempt to improve drowsiness detection in automobiles by examining velocity-related measures. These measures were also included in multiple regression-generated drowsiness detection algorithms to determine their contribution to detection accuracy. In addition, the effects of cruise control and an auditory secondary task on the level of drowsiness and driving performance were examined. Twelve volunteers from the Blacksburg, Virginia area were used as subjects. In the early morning hours after sleep deprivation, subjects drove a moving base automobile simulator, during which time physiological and performance measures were gathered. Data analysis revealed that velocity-related measures can be good indicators of drowsiness when subjects are without external stimulation, but otherwise, these measures are fairly weak indicators of drowsiness. Similarly, the addition of velocity-related measures to drowsiness detection algorithms proved to be quite modest. Finally, there was no significant main effect of either cruise control or secondary task on drowsiness or driving performance. / M.S.

This study was undertaken to determine which variables and combination of variables could be used for the prediction of on-the-road drowsiness. Numerous driver-vehicle performance measures and secondary task performance measures were collected so that the predictability of several definitional measures of drowsiness could be tested. Twelve volunteer subjects were employed in the algorithm development phase of this study. All subjects were from the driver population in the Blacksburg, Virginia area. The participants were sleep deprived and drove a moving base simulator late at night in order to increase the likelihood that they would experience drowsiness while driving. After completion of data collection, numerous algorithms were developed using multiple regression and discriminant analysis methods. Another twelve volunteer subjects were subsequently employed in the algorithm validation phase of this study. Similar physiological and driving performance measures were collected during both phases of the study. All subjects were from the same driver population. All subjects were run under similar conditions as those in the algorithm development phase. Algorithms that appeared promising which were developed in the first phase of study were validated by applying them to the new data in an attempt to predict drowsiness on a new subject pool. It was found that drowsiness could be detected on a new subject pool and that the rate of correct predictions was quite high. There was no general decrease in predictive power of the drowsiness detection algorithms when applied to new data. Results showed that an accuracy rate of over 90 percent could be accomplished when output from the detection algorithms were classified into categories of "Awake," "Questionable," and "Drowsy." / 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

Few attempts have been made to use physical and physiological driver characteristics to predict driver drowsiness. As a result, a reliable drowsy driver detection system has yet to be devised. Thus, the primary objectives of this research were to determine whether driving characteristics and response variables could be used to detect eyelid closure associated with drowsiness, and. to provide ‘potential measures of driver· drowsiness. In. the study, eyelid closure was defined as the measurement standard of drowsiness. Eyelid closure, in studies conducted at Duke University, was a reliable measure of drowsiness. A computer simulated nighttime driving task introduced 90 minutes of typical highway driving to twenty driver/subjects seated ixx a moving-base driving simulator. Each driver/subject drove under two conditions--rested and after 19 hours of being awake. During the 90 minutes of driving, two types of low-level stimuli, steering wheel torque and front wheel displacement, were applied to the simulation. Responses to these stimuli as well as driving I measures from the intervals between stimuli were analyzed for variations associated with eyelid closure. Seventeen dependent variables were investigated. / Ph. D. / incomplete_metadata

Research shows that anger has a negative impact on cognition due to the rumination effect and in the context of driving, anger negatively impacts situation awareness, driving performance, and road safety. In-vehicle agents are capable of mitigating the effects of anger and subsequent effects on driving behavior. Language is another important aspect that influences information processing and human behavior during social interactions. This thesis aims to explore the effects of the language of in-vehicle agents on angry drivers' situation awareness, driving performance, and subjective perception. The three conditions explored are the native language agent condition (Hindi or Chinese), secondary language agent condition (English), and no agent condition. Results indicate that driving performance is better in the case of the native language agent condition when compared to the no agent condition. Higher levels of situational awareness were affected by the agent condition, favoring the native language condition over the secondary language condition. The participants preferred native language agents over the other conditions and the perceived workload was higher in the no-agent condition than the native agent condition. Drivers also expressed the need to control the state of the in-vehicle agent. The study results have practical design implications and the results are expected to help foster future work in this domain. / Master of Science / People are deeply influenced by emotions. Anger while driving is shown to negatively impact people's perception and understanding of what is going on in the driving context and prediction about what will happen. As a result, this influences driving performance and road safety. Intelligent agents (such as Siri or Alexa) built into vehicles can help regulate the emotions of the drivers and can positively impact driving performance. Language is another important aspect that influences human behavior during social interactions. The current thesis aims to leverage the positive impacts of in-vehicle agents and language to design in-vehicle agent interactions capable of mitigating the negative effects of anger to ensure better driving performance and increased situation awareness. The three conditions explored are the native language agent condition (Hindi or Chinese), secondary language agent condition (English), and no agent condition. The effects on angry drivers' situation awareness, driving performance, and subjective perception are studied. Results indicate that the driving performance is better in the case of the native language agent condition when compared to the no agent condition. Participants preferred native language agents over the other conditions. People's understanding and prediction capability in the driving context was better in the native agent condition over the other conditions. The study results have practical design implications in designing in-vehicle agent interfaces and the results are expected to help foster future work.

Increasingly, many of today’s vehicles offer Society of Automotive Engineers (SAE) partially automated driving (PAD) and a limited number of SAE conditionally automated vehicles (CAD) are being developed. Vehicles with PAD systems support the driver through longitudinal and lateral control inputs. However, during PAD the driver must be prepared to take control of the vehicle at any time, requiring them to monitor the environment and PAD system. In contrast, during CAD the driver is not required to monitor the environment or system but must take control when prompted by the system. Given the ability of CAD vehicles to operate in PAD and manual driving, it is important to consider drivers’ mode awareness, that is, their ability to follow the state of the automated system and predict the implications of this status for vehicle control and monitoring responsibilities. In addition, since CAD does not require drivers to keep their visual or attentional resources on the driving task or environment, drivers are allowed to perform secondary tasks (i.e., non-driving related tasks (NDRTs)). Given that drivers will freely choose what types of tasks they do during CAD it is important to build an understanding of whether drivers will choose to engage in NDRTs in the CAD state, and drivers’ ability to perform NDRTs during CAD. To investigate driver’s mode awareness after transitions between modes, their willingness to engage in NDRTs, and their ability to perform scheduled smartphone NDRTs, an on-road experiment was conducted using the Wizard-of-Oz (WoZ) method to simulate a vehicle capable of Assisted Driving (similar to PAD) and Automated Driving (similar to CAD). A total of 36 drivers completed the on-road experiment, and experienced stable periods of manual driving, Assisted driving, and Automated driving, as well as transitions between these modes. After each transition, participants’ mode awareness was measured. Drivers’ performance of NDRTs and behavioral adaptation during Automated Driving was assessed by asking them to complete scheduled tasks on their smartphones. To measure driver willingness to engage in unscripted NDRTs during automated driving, participants were allowed to freely choose to engage in smartphone NDRTs between the scheduled tasks. It was hypothesized that drivers’ mode awareness of Assisted and Automated Driving and their willingness to engage and perform NDRTs during Automated Driving would increase with system exposure over the five planned activation periods of Automated Driving. Results from a mixed-model ANOVA showed that participants’ mode awareness of their role in Automated Driving statistically significantly increased from the first activation to the subsequent activations. There was no statistically significant effect of activation period on drivers’ willingness to engage in NDRTs, as measured by the mean percentage of time drivers chose to engage in NDRTs during Automated Driving, or driver’s ability to perform tasks, as measured by the mean task completion time of the experimenter administered smartphone NDRTs. The mean number of participants who chose to engage in an NDRT (73.8%) and the percentage of time spent on NDRTs per Automated Driving activation period (M=20.37%; SD=23.9), indicated that drivers were willing to engage in NDRTs during Automated Driving. In addition, drivers showed a high level of task performance, completing 95% of the scheduled NDRTs correctly. Altogether, these results suggest that drivers are willing to engage in and perform NDRTs during Automated Driving and that driver behavior during Automated Driving is consistent and stable during a two-hour exposure period. Finally, the findings indicate that requiring the participant to control the vehicle manually for a brief period prior to transitioning to a level of automation that allows the driver to take their visual and attentional resources away from the roadway environment results in statistically significantly less NDRT engagement compared to when participants transition directly to this level of automation. Overall, the findings from this study have methodological and potential system design implications that can help guide the future research on and design of automated driving systems. / M.S. / Increasingly, many of today’s vehicles offer automated driving technology (i.e., Assisted Driving) that support the driver through steering, braking, and accelerating the vehicle. However, during this level of automation the driver must be prepared to take control of the vehicle, requiring them to monitor the environment and the automated driving system. In addition, a limited number of vehicles offer automated driving technology (i.e., Automated Driving) that controls the vehicle and does not require the driver to monitor the environment or system, however, the driver must take control when prompted by the system. Vehicles capable of Automated Driving can also operate in Assisted and manual driving modes. Given the ability of Automated Driving vehicles to operate in Assisted and manual driving, it is important to consider driver’s ability to follow and predict the behavior of the automated system. In addition, since Automated Driving does not require drivers to keep their eyes or mind on driving or monitoring the road, drivers are allowed to perform secondary tasks. Since drivers are free to choose what types of tasks they do during Automated Driving, it is important to understand whether drivers will choose to engage in Secondary tasks, and their ability to perform these tasks during Automated Driving. To investigate driver’s mode awareness after transitions between modes, their willingness to engage in tasks, and their ability to perform scheduled smartphone tasks, an on-road experiment was conducted using the Wizard-of-Oz (WoZ) method. The WoZ method uses a concealed human to simulate an automated computer system, in this case an automated driving system. A total of 36 drivers completed the on-road experiment. The participants experienced periods of manual driving, Assisted driving, and Automated driving, as well as transitions between these modes. After each transition, participants’ knowledge of who/what was controlling the vehicle and the driver’s role in the current automated mode was measured. Drivers’ performance of tasks during Automated Driving was assessed by asking them to complete scheduled tasks on their smartphones. To measure driver willingness to engage in tasks during automated driving, participants were allowed to freely choose to engage in smartphone tasks between the scheduled tasks. It was hypothesized that drivers’ mode awareness of Assisted and Automated Driving and their willingness to engage and perform NDRTs during Automated Driving would increase with system exposure over the five planned activation periods of Automated Driving. Results showed that participants’ ability to identify their role in Automated Driving increased from the first time they experienced the system to the subsequent times. There was no change in drivers’ willingness to engage in tasks or drivers’ ability to perform tasks as they gained more experience with the Automated Driving system. However, the level of task engagement indicated that drivers were immediately willing to engage in tasks during Automated Driving. Drivers also showed a high-level of task performance. Taken together, these findings indicate that drivers are willing to engage in and perform non-driving related tasks during Automated Driving. These findings can help guide future research focused on automated systems and the design of automated driving systems.

Motor vehicle crashes are one of the leading causes of death in the United States. Traffic safety research targets at understanding the cause of crash, preventing the crash, and mitigating crash severity. This dissertation focuses on the driver-related traffic safety issues, in particular, on developing and implementing contemporary statistical modeling techniques on driving risk research on Naturalistic Driving Study data. The dissertation includes 5 chapters. In Chapter 1, I introduced the backgrounds of traffic safety research and naturalistic driving study. In Chapter 2, the state-of-practice statistical methods were implemented on individual driver risk assessment using NDS data. The study showed that critical-incident events and driver demographic characteristics can serve as good predictors for identifying risky drivers. In Chapter 3, I developed and evaluated a novel Bayesian random exposure method for Poisson regression models to account for situations where the exposure information needs to be estimated. Simulation studies and real data analysis on Cellphone Pilot Analysis study data showed that, random exposure models have significantly better model fitting performances and higher parameter coverage probabilities as compared to traditional fixed exposure models. The advantage is more apparent when the values of Poisson regression coefficients are large. In Chapter 4, I performed comprehensive simulation-based performance analyses to investigate the type-I error, power and coverage probabilities on summary effect size in classical meta-analysis models. The results shed some light for reference on the prospective and retrospective performance analysis in meta-analysis research. In Chapter 5, I implemented classical- and Bayesian-approach multi-group hierarchical models on 100-Car data. Simulation-based retrospective performance analyses were used to investigate the powers and parameter coverage probabilities among different hierarchical models. The results showed that under fixed-effects model context, complex secondary tasks are associated with higher driving risk. / Ph. D.

Användandet av simulatorer i utbildningar ökar mer och mer. Simulatorer har använts inom pilotutbildningar och inom medicinsk utbildning länge och det finns mycket forskning som stödjer deras effektivitet. Nu har simulatorerna blivit mer tillgängliga i och med den tekniska utvecklingen och har börjat användas för förarutbildningar. Däremot saknas samma gedigna vetenskapliga stöd som finns för pilotutbildningar och medicinsk utbildning. Det finns visst underlag för utbildning i riskmedvetenhet men inte så många andra färdigheter. Syftet med studien var att undersöka hur effektiv en simulator är vid utbildning av förare i Eco-driving. Till studien rekryterades 20 elever från Yrkesakademin som utbildas för behörighet C, tung lastbil. Studien var av mellangruppsdesign där experimentgruppen tränade Eco-drivingfärdigheter och data över bränsleförbrukning och hastighet samlades in. Kontrollgruppen fick en teoretisk utbildning i Eco-driving i form av en inspelad video. Experimentgruppen hade en signifikant förbättring av bränsleförbrukning men inte kontrollgruppen. Detta stödjer effektiviteten av simulatorbaserad utbildning av Eco-driving. Resultaten är även uppmuntrande till träning av liknande färdigheter som bland annat är av betydelse för trafiksäkerhet. Dessutom finns det goda möjligheter att minska kostnaderna vid förarutbildningar samtidigt som eleverna lär sig bättre. / The use of of simulators in education is increasing. The aviation and medical education have a long history of implementing simulator training and education. With a strong body of scientific research that validates their use in education. As the technical development has increased, the availability of affordable simulators has increased their use in driver education. Unfortunately the research is not as strong as with the aviation or medical education. There are some support that simulator-based education can improve hazard perception but not so many other skills. Therefore I want to examine the effectiveness of a simulator in teaching Eco-driving skills to drivers. 20 students from Yrkesakademin were recruited as they were learning to drive trucks. The study is of between group design where the experimental group practiced Eco-driving skills in the simulator. Data were collected of the participants fuel consumption and speed. The control group were shown a video lecture on Eco-driving. The experimental group did significant improve while the control group did not. These results support the effectiveness of simulator-based education of Eco-driving skills. It also is encouraging for similar driving skills that can have a significant effect on traffic safety. While there is encouraging evidence for reducing the cost of driver education at the same time the students learning is enhanced.

Previous research has shown that cognitive deficits arising from neurological impairment can impact on driving performance. The diverse nature of cognitive, perceptual and behavioural impairments experienced by drivers with neurological impairment and the resulting impact on driving ability has been the subject of extensive research involving the use of psychometric off-road measures, road safety statistics, actual on-road driving assessments and self-report. This research has shown that some drivers can compensate for limitations in their driving skills but this is dependent upon realistic self-appraisal of driving abilities. Few studies have investigated the role of self-awareness of driving abilities on on-road driving performance in persons with neurological impairment. Aims: To investigate the relationship between self-awareness of driving related abilities in neurologically impaired drivers and on-road driving performance. Participants: Retrospective data were collated on 79 participants who were referred for Occupational Therapy driving assessment, comprising 24 with Closed Head Injury (CHI) (mean age 24.67 + 5.57 yrs), 30 with Cerebrovascular Accident (CVA) (mean age 61.00 + 9.08 yrs) and 25 with 'Other' diagnosis (mean age 50.64 + 21.14 yrs). All participants held a current driver's licence or learner's permit Results: Five predictor variables were significantly associated with the on-road driving assessment outcome including three demographic variables:- diagnosis (2(2)= 7.69, p = 0.021), time since injury/illness onset (2(2)= 6.40, p = 0.041), and mileage (2(2)= 5.84, p = 0.05); and two self-awareness variables:- reaction time (2(2)= 8.04, p = 0.018), and impulse control (2(2)= 13.47, p = 0.001). Logistic regression yielded a final best model containing two predictor variables (2(4) = 20.81, p = 0.000), including diagnosis (p = 0.02) and self-awareness of impulse control (p = 0.01). Discussion and Conclusion: Participants who over-estimated their driving abilities were more likely to fail a driving assessment or require driving rehabilitation than participants who under-estimated or accurately predicted their performance and participants with a diagnosis of CVA were more likely to fail or require driving rehabilitation than those with a CHI or 'Other' diagnosis.