Matching Items (4)

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Human-centric detection and mitigation approach for various levels of cell phone-based driver distractions

Description

Driving a vehicle is a complex task that typically requires several physical interactions and mental tasks. Inattentive driving takes a driver’s attention away from the primary task of driving, which

Driving a vehicle is a complex task that typically requires several physical interactions and mental tasks. Inattentive driving takes a driver’s attention away from the primary task of driving, which can endanger the safety of driver, passenger(s), as well as pedestrians. According to several traffic safety administration organizations, distracted and inattentive driving are the primary causes of vehicle crashes or near crashes. In this research, a novel approach to detect and mitigate various levels of driving distractions is proposed. This novel approach consists of two main phases: i.) Proposing a system to detect various levels of driver distractions (low, medium, and high) using a machine learning techniques. ii.) Mitigating the effects of driver distractions through the integration of the distracted driving detection algorithm and the existing vehicle safety systems. In phase- 1, vehicle data were collected from an advanced driving simulator and a visual based sensor (webcam) for face monitoring. In addition, data were processed using a machine learning algorithm and a head pose analysis package in MATLAB. Then the model was trained and validated to detect different human operator distraction levels. In phase 2, the detected level of distraction, time to collision (TTC), lane position (LP), and steering entropy (SE) were used as an input to feed the vehicle safety controller that provides an appropriate action to maintain and/or mitigate vehicle safety status. The integrated detection algorithm and vehicle safety controller were then prototyped using MATLAB/SIMULINK for validation. A complete vehicle power train model including the driver’s interaction was replicated, and the outcome from the detection algorithm was fed into the vehicle safety controller. The results show that the vehicle safety system controller reacted and mitigated the vehicle safety status-in closed loop real-time fashion. The simulation results show that the proposed approach is efficient, accurate, and adaptable to dynamic changes resulting from the driver, as well as the vehicle system. This novel approach was applied in order to mitigate the impact of visual and cognitive distractions on the driver performance.

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Agent

Created

Date Created
  • 2017

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Feasibility of energy harvesting using a piezoelectric tire

Description

While the piezoelectric effect has been around for some time, it has only recently caught interest as a potential sustainable energy harvesting device. Piezoelectric energy harvesting has been developed for

While the piezoelectric effect has been around for some time, it has only recently caught interest as a potential sustainable energy harvesting device. Piezoelectric energy harvesting has been developed for shoes and panels, but has yet to be integrated into a marketable bicycle tire. For this thesis, the development and feasibility of a piezoelectric tire was done. This includes the development of a circuit that incorporates piezoceramic elements, energy harvesting circuitry, and an energy storage device. A single phase circuit was designed using an ac-dc diode rectifier. An electrolytic capacitor was used as the energy storage device. A financial feasibility was also done to determine targets for manufacturing cost and sales price. These models take into account market trends for high performance tires, economies of scale, and the possibility of government subsidies. This research will help understand the potential for the marketability of a piezoelectric energy harvesting tire that can create electricity for remote use. This study found that there are many obstacles that must be addressed before a piezoelectric tire can be marketed to the general public. The power output of this device is miniscule compared to an alkaline battery. In order for this device to approach the power output of an alkaline battery the weight of the device would also become an issue. Additionally this device is very costly compared to the average bicycle tire. Lastly, this device is extreme fragile and easily broken. In order for this device to become marketable the issues of power output, cost, weight, and durability must all be successfully overcome.

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Agent

Created

Date Created
  • 2012

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Network maintenance and capacity management with applications in transportation

Description

This research develops heuristics to manage both mandatory and optional network capacity reductions to better serve the network flows. The main application discussed relates to transportation networks, and flow cost

This research develops heuristics to manage both mandatory and optional network capacity reductions to better serve the network flows. The main application discussed relates to transportation networks, and flow cost relates to travel cost of users of the network. Temporary mandatory capacity reductions are required by maintenance activities. The objective of managing maintenance activities and the attendant temporary network capacity reductions is to schedule the required segment closures so that all maintenance work can be completed on time, and the total flow cost over the maintenance period is minimized for different types of flows. The goal of optional network capacity reduction is to selectively reduce the capacity of some links to improve the overall efficiency of user-optimized flows, where each traveler takes the route that minimizes the traveler’s trip cost. In this dissertation, both managing mandatory and optional network capacity reductions are addressed with the consideration of network-wide flow diversions due to changed link capacities.

This research first investigates the maintenance scheduling in transportation networks with service vehicles (e.g., truck fleets and passenger transport fleets), where these vehicles are assumed to take the system-optimized routes that minimize the total travel cost of the fleet. This problem is solved with the randomized fixed-and-optimize heuristic developed. This research also investigates the maintenance scheduling in networks with multi-modal traffic that consists of (1) regular human-driven cars with user-optimized routing and (2) self-driving vehicles with system-optimized routing. An iterative mixed flow assignment algorithm is developed to obtain the multi-modal traffic assignment resulting from a maintenance schedule. The genetic algorithm with multi-point crossover is applied to obtain a good schedule.

Based on the Braess’ paradox that removing some links may alleviate the congestion of user-optimized flows, this research generalizes the Braess’ paradox to reduce the capacity of selected links to improve the efficiency of the resultant user-optimized flows. A heuristic is developed to identify links to reduce capacity, and the corresponding capacity reduction amounts, to get more efficient total flows. Experiments on real networks demonstrate the generalized Braess’ paradox exists in reality, and the heuristic developed solves real-world test cases even when commercial solvers fail.

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Created

Date Created
  • 2017

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What doesnt kill me [untitled]: predictors of posttraumatic growth among traumatic brain injury survivors of motor vehicle accidents

Description

Decades of research and empirical studies support the belief that traumatic life events lead to a multitude of negative outcomes (Tedeschi & Calhoun, 1996), however, new research suggests that some

Decades of research and empirical studies support the belief that traumatic life events lead to a multitude of negative outcomes (Tedeschi & Calhoun, 1996), however, new research suggests that some survivors of trauma experience significant psychological growth, known as posttraumatic growth (PTG) (Tedeschi, Park, & Calhoun, 1998). The current study focused on the trauma of a traumatic brain injury (TBI) and its relation to the development of PTG. A TBI is both a psychological trauma and a type of acquired brain injury that occurs when physical injury causes damage to the brain (National Institutes of Health [NIH], 2013). Empirical studies examining TBIs and PTG are minimal. The current study focused on survivors who have sustained a TBI from a motor vehicle accident to help control for contextual factors of the injury that are known to affect outcomes. The aim of this study was to elucidate the physical, sociodemographic, contextual, and psychological factors that helped predict the development of PTG among a population of TBI survivors. In addition, another aim of this study was to gain a better understanding of the relationship between PTG and posttraumatic stress disorder (PTSD) symptomatology. Cross-sectional data from self-identified TBI survivors of motor vehicle accidents (n = 155) were used to construct a model of prediction of PTG. Preliminary analyses revealed a reliability issue with the measure that assessed participants’ personality, and these variables were not used in planned analyses. Results revealed that the majority of participants were female, Caucasian, highly educated, and unemployed. Overall, the sample indicated significant injury severity, disability, and lower than average mental and physical functioning. The final model accounted for approximately 15% of the variance in PTG and significant predictors included: gender, time since injury, and the interaction between PTSD symptoms and time since injury. The findings of this research can help inform treatment programs and rehabilitation services as well as funding that can aim to improve outcomes from survivors of TBI. Study limitations included the use of cross-sectional data, a homogenous and unrepresentative sample of TBI survivors, recruitment concerns, and low reliability observed in one of the integral measures of the study.

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Agent

Created

Date Created
  • 2016