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<p dir="ltr">As automated vehicle (AV) systems become increasingly more intelligent, understanding the complex interplay between drivers' trust in these systems and their resulting behavior is paramount for the successful integration of autonomous technologies into the transportation landscape. Currently, the effects of displaying AV system-certainty information, concerning its navigability around obstacles, on drivers' trust, decision-making, and behavior is underexplored. This thesis seeks to address this research gap and evaluate a set of dynamic and continuous human-machine interfaces (HMIs) that present self-assessed system-certainty information to drivers of AVs. A simulated driving study was conducted wherein participants were exposed to four different linear and curvilinear AV system-certainty patterns when their AV approached a construction zone. The certainty patterns represented the vehicle’s confidence in safely avoiding the construction. Using this information, drivers needed to decide whether or not to take over from the vehicle. The AV’s reliability and system-certainty were not directly proportional to one another. During the study, drivers' trust, workload, takeover decisions and performance, eye movement behavior, and heart?rate measures were captured to comprehensively understand of the factors influencing drivers' interactions with automated vehicles. Overall, participants took over in 41.3% of the drives. Results suggest that the communication of different system-certainty trends had a significant effect on drivers’ takeover response times and gaze behavior, but did not affect their trust in the system nor their workload. Ultimately, the results of this work can be used to inform the design of in vehicle interfaces in future autonomous vehicles, aiming to enhance safety and driver acceptance. By elucidating the intricate relationship between drivers' trust and behavior, this study provides valuable insights for both researchers and developers, contributing to the ongoing discourse on the human factors associated with the integration of autonomous technologies into the transportation ecosystem.</p>

<p>Statistics showing that in-vehicle driver distraction is a major contributing cause in road accidents is presented. Driver distraction is defined building on the driving theory by Gibson and Crooks. The idea to use driver distraction countermeasures as a way of mitigating the effects of the driver distraction problem is then introduced. A requirement list is formulated with ten requirements that distraction countermeasures should meet. A simplification of regarding distraction as a gaze direction problem makes way for designing an experiment to evaluate two driver distraction countermeasures in which new eye- tracking technology plays a key role. The experiment also makes use of a simulator, a surrogate in-vehicle information system as a distractor, and thirty subjects. The most important dependent measures were in-vehicle glance time and a steering wheel reaction time measure. The evaluated countermeasures – a blue flash at middle of the road position and a kinesthetic brake pulse – could, however, not be shown to meet the most important of the requirements formulated. The lack of effect of the countermeasures in the experiment may either depend on their actual inefficiency or on methodological shortcomings of the experiment. These alternatives are discussed. It is speculated that the biggest problems with the possible lack of actual efficiency have to do with that the theoretical basis for using a flash did not transfer to the driving setting, and that the brake pulse used was too weak. The methodological problems have to do with the non-validated dependent measures used, missing data, nuisance warnings, insufficient distractors, non-precise hypotheses, and difficulties with separating the effect of the countermeasures from the psychological force to look on the road.</p>

Identifying and understanding the relationships between observed driving behavior over long-term periods and corresponding crash involvement rates is paramount to enhancing safety improvement programs and providing useful insights for transportation safety engineers, policy markers, insurance industries, and the public. Unlike previous data collection methods, recent advancement in mobile computing and accuracy of global positioning systems (GPS) allow researchers to monitor driving activities of large fleets of vehicles, for long-time study periods, at great detail. This study investigates the driving patterns of drivers who have and who have not experienced crashes during a 14-month study period using the longitudinally collected GPS data during a six-month Commute Atlanta study. This investigation allows an empirical investigation to assess whether drivers with recent crash experiences exhibit different driving or activity patterns (travel mileage, travel duration, speed, acceleration, speed stability duration, frequency of unfamiliar roadway activities, frequency of turn movement activities, and previous crash location exposures). This study also discusses various techniques of implementing GPS data streams in safety analyses. Finally, this study provides useful guidance for researchers who plan to evaluate the relationships between driver driving behavior and crash risk with large sample data and proposes driving behavior activity exposure metrics of individual drivers for possible safety surrogate measures as well as for driver re-training and education programs.

Traffic oscillations, or simply stop-and-go waves, are a common phenomenon arising in congested traffic but still not well understood. This phenomenon causes broad adverse impacts to safety risk, fuel efficiency and greenhouse emission. To eliminate or reduce those impacts, understanding the cause and propagation mechanism is essential. This dissertation studied driving behavior in traffic oscillations with the objective to uncover the formation and propagation mechanism of traffic oscillations. This study establishes a behavioral car-following model, the Asymmetric Behavioral model, based on empirical trajectory data that is able to reproduce the spontaneous formation and ensuing propagation of traffic oscillations in congested traffic. By analyzing individual drivers' car-following behavior throughout oscillation cycles it is found that this behavior is consistent across drivers and can be captured by a simple model. The statistical analysis of the model's parameters reveals that driver' behavior during oscillation (i.e., reaction to oscillation) is strongly correlated with driver behavior before oscillations and it varies with the development stage of the oscillation. Simulation of the model shows that it is able to produce characteristics of traffic oscillations consistently with empirical observations. This study also unveils the generation mechanism of the traffic hysteresis phenomenon arising in traffic oscillations using the Asymmetric Behavioral model. It is found that the occurrence of traffic hysteresis is closely correlated with driver behavior when experiencing traffic oscillations. In the growth and fully-developed stage of traffic oscillations, drivers behave differently, which results in different distribution of hysteresis patterns. This research makes it possible to unveil new management and control strategies of traffic oscillations to improve traffic operation and to quantify the environmental and safety impacts of traffic oscillations. For example, it can be used to estimate the increase of greenhouse emission and decrease of fuel efficiency imposed by traffic oscillations. It can also be used to study the increase of accident rate.

abstract: In this research work, a novel control system strategy for the robust control of an unmanned ground vehicle is proposed. This strategy is motivated by efforts to mitigate the problem for scenarios in which the human operator is unable to properly communicate with the vehicle. This novel control system strategy consisted of three major components: I.) Two independent intelligent controllers, II.) An intelligent navigation system, and III.) An intelligent controller tuning unit. The inner workings of the first two components are based off the Brain Emotional Learning (BEL), which is a mathematical model of the Amygdala-Orbitofrontal, a region in mammalians brain known to be responsible for emotional learning. Simulation results demonstrated the implementation of the BEL model to be very robust, efficient, and adaptable to dynamical changes in its application as controller and as a sensor fusion filter for an unmanned ground vehicle. These results were obtained with significantly less computational cost when compared to traditional methods for control and sensor fusion. For the intelligent controller tuning unit, the implementation of a human emotion recognition system was investigated. This system was utilized for the classification of driving behavior. Results from experiments showed that the affective states of the driver are accurately captured. However, the driver's affective state is not a good indicator of the driver's driving behavior. As a result, an alternative method for classifying driving behavior from the driver's brain activity was explored. This method proved to be successful at classifying the driver's behavior. It obtained results comparable to the common approach through vehicle parameters. This alternative approach has the advantage of directly classifying driving behavior from the driver, which is of particular use in UGV domain because the operator's information is readily available. The classified driving mode was used tune the controllers' performance to a desired mode of operation. Such qualities are required for a contingency control system that would allow the vehicle to operate with no operator inputs. / Dissertation/Thesis / Doctoral Dissertation Engineering 2015

Statistics showing that in-vehicle driver distraction is a major contributing cause in road accidents is presented. Driver distraction is defined building on the driving theory by Gibson and Crooks. The idea to use driver distraction countermeasures as a way of mitigating the effects of the driver distraction problem is then introduced. A requirement list is formulated with ten requirements that distraction countermeasures should meet. A simplification of regarding distraction as a gaze direction problem makes way for designing an experiment to evaluate two driver distraction countermeasures in which new eye- tracking technology plays a key role. The experiment also makes use of a simulator, a surrogate in-vehicle information system as a distractor, and thirty subjects. The most important dependent measures were in-vehicle glance time and a steering wheel reaction time measure. The evaluated countermeasures – a blue flash at middle of the road position and a kinesthetic brake pulse – could, however, not be shown to meet the most important of the requirements formulated. The lack of effect of the countermeasures in the experiment may either depend on their actual inefficiency or on methodological shortcomings of the experiment. These alternatives are discussed. It is speculated that the biggest problems with the possible lack of actual efficiency have to do with that the theoretical basis for using a flash did not transfer to the driving setting, and that the brake pulse used was too weak. The methodological problems have to do with the non-validated dependent measures used, missing data, nuisance warnings, insufficient distractors, non-precise hypotheses, and difficulties with separating the effect of the countermeasures from the psychological force to look on the road.

The identification of roadway sections with a higher than expected number of crashes is usually based on long term crash frequency data. In situations where historical crash data are limited or not available, surrogate safety measures, based on characteristics such as road geometries, traffic volume, and speed variation are often considered. Most of existing crash prediction models relate safety to speed variation at a specific point on the roadway. However, such point-specific explanatory variables do not capture the effect of speed consistency along the roadway. This study developed several measures based on the speed profiles along road segments to estimate the crash frequency on urban streets. To collect speed profile data, second-by-second speed data were obtained from more than 460 GPS-equipped vehicles participating in the Commute Atlanta Study over the 2004 calendar year. A series of speed data filters have been developed to identify likely free-flow speed data. The quantified relationships between surrogate measures and crash frequency are developed using regression tree and generalized linear modeling (GLM) approaches. The results indicate that safety characteristics of roadways are likely a function of the roadway classification. Two crash prediction models with different set of explanatory variables were developed for higher and lower classification roadways. The findings support the potential use of the profile-based measures to evaluate the safety of road network as the deployment of GPS-equipped vehicles become more prevalent.

This work is a result of the current state of the art and the measurement of drivers' reactions and their behavior through the eyetracker during the passage through pedestrian crossings on which the pedestrian crossed. The subject of the measurement was the assessment of the design of the pedestrian crossing and its subsequent influence on the reactions of drivers and their behavior.

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