Traffic congestion and safety has become a major issue in the modern world's commute. Congestion has been causing people to travel billions of hours more and to purchase billions of gallons of fuel extra which account to congestion cost of billions of dollars. Autonomous driving vehicles have been one solution to this problem because of their huge impact on efficiency, pollution, and human safety. Also, extensive research has been carried out on control design of vehicular platoons because a further improvement in traffic throughput while not compromising the safety is possible when the vehicles in the platoon are provided with better predictive abilities. Motion control is a key area of autonomous driving research that handles moving parts of vehicles in a deliberate and controlled manner. A widely worked on problem in motion control concerned with time parameterized reference tracking is trajectory tracking. Having an efficient and effective tracking algorithm embedded in the autonomous driving system is the key for better performance in terms of resources consumed and tracking error. Many tracking control algorithms in literature rely on an accurate model of the vehicle and often, it can be an intimidating task to come up with an accurate model taking into consideration various conditions like friction, heat effects, ageing processes etc. And typically, control algorithms rely on periodic execution of the tasks that update the control actions, but such updates might not be required, which result in unnecessary actions that waste resources. The main focus of this work is to design an intermittent model-free optimal control algorithm in order to enable autonomous vehicles to track trajectories at high-speeds. To obtain a solution which is model-free, a Q-learning setup with an actor-network to approximate the optimal intermittent controller and a critic network to approximate the optimal cost, resulting in the appropriate tuning laws is considered. / Master of Science / A risen research effort in the area of autonomous vehicles has been witnessed in the past few decades because these systems improve safety, comfort, transport time and energy consumption which are some of the main issues humans are facing in the modern world’s highway systems. Systems like emergency braking, automatic parking, blind angle vehicle detection are creating a safer driving environment in populated areas. Advanced driver assistance systems (ADAS) are what such kind of systems are known as. An extension of these partially automated ADAS are vehicles with fully automated driving abilities, which are able to drive by themselves without any human involvement. An extensively proposed approach for making traffic throughput more efficient on existing highways is to assemble autonomous vehicles into platoons. Small intervehicle spacing and many vehicles constituting each platoon formation improve the traffic throughput significantly. Lately, the advancements in computational capabilities, in terms of both algorithms and hardware, communications, and navigation and sensing devices contributed a lot to the development of autonomous systems (both single and multiagent) that operate with high reliability in uncertain/dynamic operating conditions and environments. Motion control is an important area in the autonomous vehicles research. Trajectory-tracking is a widely studied motion control scenario which is about designing control laws that force a system to follow some time-dependent reference path and it is important to have an effective and efficient trajectory-tracking control law in an autonomous vehicle to reduce the resources consumed and tracking error. The goal of this work is to design an intermittent model-free trajectory tracking control algorithm where there is no need of any mathematical model of the vehicle system being controlled and which can reduce the controller updates by allowing the system to evolve in an open loop fashion and close the loop only when an user defined triggering condition is satisfied. The approach is energy efficient in that the control updates are limited to instances when they are needed rather than unnecessary periodic updates. Q-learning which is a model-free reinforcement learning technique is used in the trajectory tracking motion control algorithm to make the vehicles track their respective reference trajectories without any requirement of their motion model, the knowledge of which is generally needed when dealing with a motion control problem. The testing of the designed algorithm in simulations and experiments is presented in this work. The study and development of a vehicle platform in order to perform the experiments is also discussed. Different motion control and sensing techniques are presented and used. The vehicle platform is shown to track a reference trajectory autonomously without any human intervention, both in simulations and experiments, proving the effectiveness of the proposed algorithm.

Driver distraction has become a major concern for the U.S. Department of Transportation (US DOT).  Performance decrements are typically the result of driver distraction because attentional resources are limited, which are limited; fatigue and drowsiness limit attentional resources further.  The purpose of the current research is to gain an understanding of the relationship between mobile device use (MDU), fatigue, through driving time and time on duty, and drowsiness, through time of day and amount of sleep, for commercial motor vehicle drivers.  A re-analysis of naturalistic driving data was used to obtain information about the factors, MDU, safety-critical events (SCE), and normal driving epochs.  Odds ratios were used to calculate SCE risk for 6 mobile device use subtasks and each of the factors, which were divided into smaller bins of hours for more specific information.  A generalized linear mixed model and chi-square test were used to assess MDU for each factor and the associated bins.  Results indicated visually demanding subtasks were associated with an increase in SCE risk, but conversation on a hands-free cell phone decreased SCE risk.  There was an increase in SCE risk for visual manual subtasks for all bins in which analyses were possible.  Drivers had a higher proportion of MDU in the early morning (circadian low period) than all other times of day that were analyzed.  These results will be used to create recommended training and evaluate policy and technology and will help explain the relationship between MDU, fatigue, and drowsiness. / Master of Science

Growth trends in vehicle transportation for the year 1989 showed that truck travel has increased from 400 billion vehicle miles of travel to 600 billion vehicle miles from 1980 to 1989, a staggering 50% increase. If this trend continues, then truck travel will reach 800 billion vehicle miles by the end of the year 2000. This increase in truck travel poses operational and safety problems for both passenger vehicles and trucks. To improve the existing highway facilities for trucks as well as to determine the design standards for new truck facilities, an understanding of the relationship between truck accidents and highway geometry is required. A number of models have been developed in the past but none of them consider all of the geometric features of the highway which are crucial for truck travel and the causation of truck accidents. The objectives of this study were to identify the roadway variables that affect truck accidents and to develop mathematical models which would determine truck involvement rates, per mile, per year. Data from the Highway Safety Information System (HSIS) was used in this analysis. The HSIS is a new data base developed by FHWA which contains accident, roadway and traffic data from five States. Models for truck accidents on Interstates, 2 lane rural roads, and for over turning accidents on Interstates were developed. The models indicate that truck accidents are primarily affected by horizontal curvature and vertical gradient albeit their values are different for Interstates and 2 lane rural roads. The number of truck accidents decreases on 2 lane rural roads as the shoulder width increases, and the model indicates that gradient has no effect on truck accidents on these roads and this, may be due to the inadequacy of the data. The Interstate model indicates that the higher the degree of curvature and the percentage of gradient, the greater the number of truck accident, as well as overturning truck accident involvement rates. / Master of Science

Variable speed limits (VSL) are dynamic traffic management systems designed to increase the efficiency and safety of highways. While the macroscopic performance of VSL systems is well explored in the existing literature, there is a need to further understand the microscopic behavior of vehicles driving in VSL zones. Specifically, driver compliance to advisory VSL systems is quantified based on a driving-simulation experiment and introduced into a broader microscopic behavior model. Statistical analysis indicates that VSL compliance can be predicted based upon several VSL design parameters. The developed two-state microscopic model is calibrated to driving-simulation trajectory data. A calibrated VSL microscopic model can be utilized for new VSL control and macroscopic performance studies, adding an increased dimension of realism to simulation work. As an example, the microscopic model is implemented within VISSIM (overriding the default car-following model) and utilized for a safety-mobility performance assessment of an incident-responsive VSL control algorithm implemented in a MATLAB COM interface. Examination of the multi-objective optimization frontier reveals an inverse relationship between safety and mobility under different control algorithm parameters. Engineers are thus faced with a decision between performing multi-objective optimization and selecting a dominant VSL control objective (e.g. maximizing safety versus mobility performance). / Master of Science

This research effort aims to create a new car-following model that accounts for the effects of emotion on driver behavior. This research effort is divided into eight research milestones: (1) the development of a segmentation and clustering algorithm to perform new investigations into driver behavior; (2) the finding that driver behavior is different between drivers, between car-following periods, and within a car-following period; (3) the finding that there are patterns in the distribution of driving behaviors; (4) the finding that driving states can result in different driving actions and that the same driving action can be the result of multiple driving states; (5) the finding that the performance of car-following models can be improved by calibration to state-action clusters; (6) the development of a psychophysiological driving simulator study; (7) the finding that the distribution of driving behavior is affected by emotional states; and (8) the development of a car-following model that incorporates the influence of emotions. / Ph. D.

This study was performed with the goal of delineating how drivers' interactions with an Automated Driving System were affected by a Driver Monitoring System (DMS), which provided alerts to the driver when he or she became inattentive to the driving environment. There were two specific research questions. The first was centered on addressing how drivers' trust and satisfaction with an Automated Driving System was affected by a DMS. The second was centered on addressing how drivers' abilities to detect changes in the driving environment that required intervention were affected by the presence of a DMS. Data were collected from fifty-six drivers during a test-track experiment with an Automated Driving System prototype that was equipped with a DMS. DMS attention prompt conditions were treated as the independent variable and trust, satisfaction, and driver performance during the experimenter triggered lane drifts were treated as dependent variables. The findings of this investigation suggested that drivers who receive attention prompts from a DMS have lower levels of trust and satisfaction with the Automated Driving System compared to drivers who do not receive attention prompts from a DMS. While the DMS may result in lower levels of trust and satisfaction, the DMS may help drivers detect changes in the driving environment that require attention. Specifically, drivers who received attention prompts after 7 consecutive seconds of inattention were 5 times more likely to react to a lane drift with no alert compared to drivers who did not receive attention prompts at all. / Master of Science

Most phenomena in engineering fields involve physical variables that can potentially be predicted using simple or complex mathematical models. However, traffic engineers and researchers are faced with a complex challenge since they have to deal with the human element. For instance, it can be stated that the biggest challenge facing researchers in the area of car-following theory relates to accounting for the human-in-the-loop while modeling the longitudinal motion of the vehicles. In fact, a major drawback of existing car-following models is that the human-in-the-loop is not modeled explicitly. This is specifically important since the output from car-following models directly impacts several other factors and measures of effectiveness, such as vehicle emissions and fuel consumption levels. The main contribution of this research relates to modeling and incorporating, in an explicit and independent manner, the human-in-the-loop component in car-following theory in such a way that it can be either activated or deactivated depending on if a human driver is in control of the vehicle. That would ensure that a car-following model is able to reflect the different control and autonomy levels that a vehicle could be operated under. Besides that, this thesis offers a better understanding of how humans behave and differ from each other. In fact, through the implementation of explicit parameters representing the human-in-the-loop element, the heterogeneity of human behavior, in terms of driving patterns and styles, is captured. To achieve its contributions, the study starts by modifying the maximum acceleration vehicle-dynamics model by explicitly incorporating parameters that aim to model driver behavior in its expression making it suitable for the representation of typical acceleration behavior. The modified variant of the model is demonstrated to have a flexible shape that allows it to model different types of variations that drivers can generate, and to be superior to other similar models in that it predicts more accurate acceleration levels in all domains. The resulting model is then integrated in the Rakha-Pasumarthy-Adjerid car-following model, which uses a steady-state formulation along with acceleration and collision avoidance constraints to model the longitudinal motion of vehicles. The validation of the model using a naturalistic dataset found that the modified formulation successfully integrated the human behavior component in the model and that the new formulation decreases the modeling error. Thereafter, this dissertation proposes a new car-following model, which we term the Fadhloun-Rakha model. Even though structurally different, the developed model incorporates the key components of the Rakha-Pasumarthy-Adjerid model in that it uses the same steady state formulation, respects vehicle dynamics, and uses very similar collision-avoidance strategies to ensure safe following distances between vehicles. Besides offering a better fit to empirical data, the Fadhloun-Rakha model is inclusive of the following characteristics: (1) it models the driver throttle and brake pedal input; (2) it captures driver variability; (3) it allows for shorter than steady-state following distances when following faster leading vehicles; (4) it offers a much smoother acceleration profile; and (5) it explicitly captures driver perception and control inaccuracies and errors. Through a quantitative and qualitative evaluation using naturalistic data, the new model is demonstrated to outperform other state-of-the-practice car-following models. In fact, the model is proved to result in a significant decrease in the modeling error, and to generate trajectories that are highly consistent with the observed car-following behavior. The final part of this study investigates a case in which the driver is excluded and the vehicles are operating in a connected environment. This section aims to showcase a scenario in which the human-in-the-loop is deactivated through the development of a platooning strategy that governs the motion of connected cooperative multi-vehicle platoons. / Doctor of Philosophy / Even though the study of the longitudinal motion of vehicles spanned over several decades leading to the development of more precise and complex car-following models, an important aspect was constantly overlooked in those models. In fact, due to the complexity of modeling the human-in-the-loop, the vehicle and the driver were almost always assumed to represent a single entity. More specifically, ignoring driver behavior and integrating it to the vehicle allowed avoiding to deal with the challenges related to modeling human behavior. The difficulty of mathematically modeling the vehicle and the driver as two independent components rather than one unique system is due to two main reasons. First, there are numerous car models and types that make it difficult to determine the different parameters impacting the performance of the vehicle as they differ from vehicle to vehicle. Second, different driving patterns exist and the fact that they are mostly dependent on human behavior and psychology makes them very difficult to replicate mathematically. The research presented in this thesis provides a comprehensive investigation of the human-in-the-loop component in car-following theory leading to a better understanding of the human-vehicle interaction. This study was initiated due to the noticeable overlooking of driver behavior in the existing literature which, as a result, fails to capture the effect of human control and perception errors.

As autonomy in vehicles increases, the role of the driver will diminish, moving on to more non-driving related tasks. We are at a juncture at which cars have the ability to drive themselves, but only if the driver is ready to take over control of the vehicle when required (e.g., Tesla autopilot). Therefore, it is important that adequate alerts are used to warn drivers in various contexts to take control back from these semi-automated vehicles. Considerable research has been conducted to design the safest alerts for the takeover transition. However, more systematic research is still required to accurately predict driver responses to different parameters of the alerts. Also, takeover research has not considered drivers' states (e.g., emotions). Anger is one of the emotions that has been shown to impair driver judgment and performance. There is limited research on how anger might influence takeover performance in semi-automated driving. This study aimed to investigate the influence of anger on takeover reaction time and safety by comparing angry and neutral drivers. Additionally, the effects of increased perceived urgency of auditory alarms on takeover reaction time were measured. Data from this research was used to help test mathematical driver behavior modeling using the QN-MHP cognitive architecture. Using a motion-based simulator, 36 participants performed takeovers in semi-automated vehicle on a 3-lane highway. Between takeovers, participants performed a secondary task (i.e., online game) on a tablet. There were no significant differences in takeover reaction time between angry and neutral drivers. However, angry drivers drove faster which can lead to dangerous collisions. Angry drivers took longer to change lanes with lower steering wheel angles. Neutral drivers' slower speeds and higher steering wheel angles indicated that they initiated the lane change earlier, and thus, made safer lane changes. As expected, higher frequency and more repetitions of the auditory takeover displays led to faster takeover reaction times. QN-MHP model predictions of takeover reaction times resulted in a 68.92% correlation with the empirical data collected. The results of this study suggest that angry drivers perform riskier than neutral drivers when taking over control of a semi-automated vehicle. This study is expected to make a significant contribution to research on the influence of emotion, specifically, anger on takeover performance in semi-automated vehicles as well as takeover display design. / Master of Science / Over the last decade, there has been an increasing shift towards the automation of cars. But, this is only made possible in situations where the driver is ready to take over control of the vehicle when required (e.g., Tesla autopilot). Therefore, it is important to use the right alert sounds to warn drivers to take control back from their self-driving cars. There has been a lot of research in designing the safest alerts for taking over control of the vehicle. However, research has not considered the driver's emotions while taking over control of their vehicle. Anger has been shown to be one of the emotions that can impair driver judgment and performance. Limited research has been performed to measure how anger can influence takeover performance. This study compared how angry drivers are different from non-angry (neutral) drivers in their takeover reaction time and safety. Additionally, the effects of a more urgent sounding alert on reaction time were also measured. The data from this research help to validate the predictions of a mathematical model of driver behavior. Thirty-six participants performed takeovers in a self-driving car simulator. While they were driving in the simulator, they also played a game on a tablet. The results showed that angry drivers and neutral drivers took the same time to takeover. But, angry drivers drove faster which can lead to dangerous collisions. Angry drivers took longer to change lanes with lower steering wheel angles. Neutral drivers started changing lanes earlier because they drove slower and turned more. This meant they drove safer than angry drivers. A more urgent sounding alert led to faster takeover reaction times from both drivers. The mathematical model predictions of takeover reaction time were nearly 70% close to the actual data collected. The results of this study suggest that angry drivers perform worse takeovers than neutral drivers. The findings will help design safer alerts in self-driving cars and also contribute to the design of self-driving cars that consider the drivers' emotional states.

In this study, we investigated whether modifying augmented reality head-up display (AR HUD) graphics’ perceptual form influences spatial learning of the environment. We employed a 2x2 between-subjects design in which twenty-four participants were counterbalanced by gender. We used a fixed base, medium-fidelity driving simulator at the COGENT lab at Virginia Tech. Two different navigation cues systems were compared: world-relative and screen-relative. The world-relative condition placed an artificial post sign at the corner of an approaching intersection containing a real landmark. The screen-relative condition displayed turn directions using a screen-fixed traditional arrow located directly ahead of the participant on the right or left side on the HUD. We captured empirical data regarding changes in driving behaviors, glance behaviors, spatial knowledge acquisition (measured in terms of landmark and route knowledge), reported workload, and usability of the interface. Results showed that both screen-relative and world-relative AR head-up display interfaces have similar impact on the levels of spatial knowledge acquired; suggesting that world-relative AR graphics may be used for navigation with no comparative reduction in spatial knowledge acquisition. Even though our initial assumption that the conformal AR HUD interface would draw drivers’ attention to a specific part of the display was correct, this type of interface was not helpful to increase spatial knowledge acquisition. This finding contrasts a common perspective in the AR community that conformal, world-relative graphics are inherently more effective than screen-relative graphics. We suggest that simple, screen-fixed designs may indeed be effective in certain contexts. Finally, eye-tracking analyses showed fundamental differences in the way participants visually interacted with different AR HUD interfaces; with conformal-graphics demanding more visual attention from drivers. We showed that the distribution of visual attention allocation was that the world-relative condition was typically associated with fewer glances in total, but glances of longer duration. / M.S. / As humans, we develop mental representations of our surroundings as we move through and learn about our environment. When navigating via car, developing robust mental representations (spatial knowledge) of the environment is crucial in situations where technology fails, or we need to find locations not included in a navigation system’s database. Over-reliance on traditional in-vehicle navigation devices has been shown to negatively impact our ability to navigate based on our own internal knowledge. Recently, the automotive industry has been developing new in-vehicle devices that have the potential to promote more active navigation and potentially enhance spatial knowledge acquisition. Vehicles with augmented reality (AR) graphics delivered via head-up displays (HUDs) present navigation information directly within drivers’ forward field of view, allowing drivers to gather information needed without looking away from the road. While this AR navigation technology is promising, the nuances of interface design and its impacts on drivers must be further understood before AR can be widely and safely incorporated into vehicles. In this work, we present a user study that examines how screen-relative and world-relative AR HUD interface designs affect drivers’ spatial knowledge acquisition. Results showed that both screen-relative and world-relative AR head-up display interfaces have similar impact on the levels of spatial knowledge acquired; suggesting that world-relative AR graphics may be used for navigation with no comparative reduction in spatial knowledge acquisition. However, eye-tracking analyses showed fundamental differences in the way participants visually interacted with different AR HUD interfaces; with conformal-graphics demanding more visual attention from drivers

A living vehicle sets forth the ability for a lifestyle not of a static place, but as part of the interstate system built into the American landscape. A living vehicle provides the ability to craft a lifestyle around mobility, and will support the situation of living on the road for extended periods of time with many potential benefits over traditional travel. First and foremost, a living vehicle gives the individual the ability to travel large spans with relative ease. A living vehicle's architecture will also provide the interior environment that supports the necessities and tasks of daily life similar to a house. This enables the individual to complete tasks in their living vehicle, as they traditionally would in their house, with the options available in the living vehicle to self drive and wirelessly charge all while remaining on the road. / Master of Architecture / A living vehicle sets forth the ability for a lifestyle not of a static place, but as part of the interstate system built into the American landscape. A living vehicle provides the ability to craft a lifestyle around mobility, and will support the situation of living on the road for extended periods of time with many potential benefits over traditional travel. First and foremost, a living vehicle gives the individual the ability to travel large spans with relative ease. A living vehicle's architecture will also provide the interior environment that supports the necessities and tasks of daily life similar to a house. This enables the individual to complete tasks in their living vehicle, as they traditionally would in their house, with the options available in the living vehicle to self drive and wirelessly charge all while remaining on the road.