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This study presents the results of an evaluation of the 24/7 Sobriety Program in Cass County, North Dakota, looking specifically at participants’ likelihood of receiving a conviction of Driving Under the Influence (DUI) both during and after exiting the program. Data was collected of participants who have been enrolled in the program from the start of the program in 2010 through 2018 and matched to public criminal records searches of each participant. Several analyses were run to determine whether substance choice (alcohol vs. drugs), gender (male vs. female), and duration in program influence a participant’s likelihood to recidivate. Findings for each measure are presented including potential changes that could be made, as well as, limitations of the study.

The aim of this thesis is the design of a motion driving simulator for real-time simulation of driving experience. The design takes inspiration from an analysis of existing solutions. An emphasis was put on driver ergonomics in positions typical for a sports car, passenger car and truck, then on variability of motion system and simplicity of the whole design. The dynamics of motion system is also a subject of research. The computational model has been created to test the optimal position of moving arms. Another aim of the study is to create an interface between hardware and software, to confirm the function of the control device using a simple simulation model.

abstract: Vehicular automation and autonomy are emerging fields that are growing at an exponential rate, expected to alter the very foundations of our transportation system within the next 10-25 years. A crucial interaction has been born out this new technology: Human and automated drivers operating within the same environment. Despite the well- known dangers of automobiles and driving, autonomous vehicles and their consequences on driving environments are not well understood by the population who will soon be interacting with them every day. Will an improvement in the understanding of autonomous vehicles have an effect on how humans behave when driving around them? And furthermore, will this improvement in the understanding of autonomous vehicles lead to higher levels of trust in them? This study addressed these questions by conducting a survey to measure participant’s driving behavior and trust when in the presence of autonomous vehicles. Participants were given several pre-tests to measure existing knowledge and trust of autonomous vehicles, as well as to see their driving behavior when in close proximity to autonomous vehicles. Then participants were presented with an educational intervention, detailing how autonomous vehicles work, including their decision processes. After examining the intervention, participants were asked to repeat post-tests identical to the ones administered before the intervention. Though a significant difference in self-reported driving behavior was measure between the pre-test and post- test, there was no significant relation found between improvement in scores on the education intervention knowledge check and driving behavior. There was also no significant relation found between improvement in scores on the education intervention knowledge check and the change in trust scores. These findings can be used to inform autonomous vehicle and infrastructure design as well as future studies of the effects of autonomous vehicles on human drivers in experimental settings. / Dissertation/Thesis / Masters Thesis Human Systems Engineering 2019

Two major objectives of organisations are to achieve firm performance and to maintain a competitive advantage; strategies to achieve these objectives differ widely. Research at the entrepreneurship and marketing interface investigates the application of both dimensions on firm activities, processes and behaviour to achieve different performance parameters. In the field of entrepreneurial marketing research two key approaches are discussed: a market-driven and a market-driving approach. Market-driven approaches, though applied by many organisations, are less successful in allowing organisations to outperform others and create long-term competitive advantage. Market-driving, on the other hand, is considered to contribute to enduring competitive advantage. Current research indicates that the construct of market driving and the factors that influence it are not well understood. The purpose of this study is therefore to measure market driving and determine firm-internal factors that influence an organisation’s market-driving ability in the South African healthcare industry. In this research, constructs drawn from the literature study were used to formulate the conceptual framework and statistical model. The empirical part of the study used a fully structured telephonic questionnaire and the respondents were managers in organisations in the South African healthcare industry. Data analysis employed structural equation modelling. The results indicate that market driving can reliably be measured by three activities: market sensing; influencing customer preferences; and alliance formation. Entrepreneurial behaviour, strategic orientation and entrepreneurial capital have a more positive impact on market-driving ability than corporate entrepreneurial management. The study demonstrated that market-driving ability significantly benefits firm performance and relative competitive strength. The study provides a solid basis for future research in the field. Moreover, the results of the study can be applied by organisations in a three-step process. First, organisations can assess their current level of market driving. Second, they can assess influencing factors, and finally identify areas for improvement. Through continuous reassessment organisations can work on their market-driving ability to achieve their organisational objectives. / Thesis (PhD)--University of Pretoria, 2011. / Business Management / unrestricted

The ability to look outward from your vehicle and assess dangerous peer behavior is typically a trivial task for humans, but not always. Distracted driving is an issue that has been seen on our roadways ever since cars have been invented, but even more so after the wide spread use of cell phones. This thesis introduces a new system for monitoring the surrounding vehicles with outside facing cameras that detect in real time if the vehicle being followed is engaging in distracted behavior. This system uses techniques from image processing, signal processing, and machine learning. It’s ability to pick out drivers with dangerous behavior is shown to be accurate with a hit count of 87.5%, and with few false positives. It aims to help make either the human driver or the machine driver more aware and assist with better decision making.

BACKGROUND- Motor vehicle crashes pose a serious public health problem. Many serious crashes are due to faulty driving by unfit operators, including several categories of neurological patients. Unfortunately, there seems to be little agreement among health professionals, driving experts, and state government on how to advise these individuals. REVIEW SUMMARY- This article reviews the question of driving in neurological patients. Decisions on driver fitness should be based on empirical observations of performance and not on criteria of age or medical diagnosis, which alone are unreliable predictors. Relevant data can be collected either on a road test or off-road, using different probes of vision and cognition, in the setting of a Department of Motor Vehicles office or medical clinic. The use of a driving simulator is also feasible. The predictive value of these performance assessments is a topic of active research. CONCLUSION- Understanding how performance data from off-road and on-road observations correlate with real-life crash risk is a key step toward developing safe, fair, and accurate means of predicting driver fitness. One potential benefit is the prevention of injury, and another is the preservation of mobility and independence of individuals whose licenses are being unduly revoked because of old age or illness.

Trusting autonomous vehicles is seen as crucial for their dissemination. However, research on autonomous driving so far is restricted by using closed training courses or simulators and by comparing behaviour and evaluation while driving oneself (a manual car) with being driven (by an autonomous car). In the current study, we investigated passengers’ eye movements, categorized as safety-relevant or not safety-relevant, and trust ratings while being driven, once manually and once by an autonomous car, in real traffic as well as in a simulator. As some of the effects observed in the field experiment might have been caused by driving style, driving style was additionally varied in the simulator. Fixations in safety-relevant regions (e.g., on the road and steering wheel) were observed more frequently during safety critical driving situations than during regular driving. More safety-relevant fixations for the autonomous compared to the manual driving mode were observed particularly in the field. Trust ratings were affected by driving mode mainly in the simulator: Here, being driven autonomously led to a lower reported trust than believing to be driven by a human driver. Driving style showed to affect trust ratings, but not gaze behaviour in the simulator experiment. Correlations between gazing into safety relevant regions and trust ratings were of smaller descriptive size than in recent investigations on drivers, suggesting that gazing into safety-relevant regions as objective alternative to trust ratings may not be as exhaustive for passengers as for drivers.

The remarkable increase in the use of sensors in our daily lives has provided an increasing number of opportunities for decision-making and automation of events. Opportunities for decision-making have further risen with the advent of smart technology and the omnipresence of sensors. Various methods have been devised to detect different events in a driving environment using smart-phones as they provide two main advantages: they remove the need to have dedicated hardware in vehicles and they are widely accessible. Rewarding safe driving has always been an important issue for insurance companies. With this intention, they are now moving toward implementing plans that consider current driving usage (Usage-based-drive plans) in contrast with traditional history-based-plans. The detection of driving events is important in insurance telematics for this purpose. Events such as acceleration and turning are good examples of important information. The sensors are capable of detecting whether a car is accelerating or braking, while through fusing services we can detect other events like speeding or the occurrence of a severe weather phenomenon that can affect driving. This thesis aims to look at the telematics from a new angle that employs smart-phones as the sensing platform. We proposed a new hybrid classification algorithm that detects acceleration-based events with an F1-score of 0.9304 and turn events with an F1-score of 0.9038. We further performed a case study on measuring the performance of driving utilizing various measures. This index can be used by a wide range of benefactors such as the insurance and transportation industries.