Towards Learning Feasible Hierarchical Decision-Making Policies in Urban Autonomous Driving

Modern learning-based algorithms, powered by advanced deep structured neural nets, have multifacetedly facilitated automated driving platforms, spanning from scene characterization and perception to low-level control and state estimation schemes. Nonetheless, urban autonomous driving is regarded as a challenging application for machine learning (ML) and artificial intelligence (AI) since the learnt driving policies must handle complex multi-agent driving scenarios with indeterministic intentions of road participants. In the case of unsignalized intersections, automating the decision-making process at these safety-critical environments entails comprehending numerous layers of abstractions associated with learning robust driving behaviors to allow the vehicle to drive safely and efficiently. Based on our in-depth investigation, we discern that an efficient, yet safe, decision-making scheme for navigating real-world unsignalized intersections does not exist yet. The state-of-the-art schemes lacked practicality to handle real-life complex scenarios as they utilize Low-fidelity vehicle dynamic models which makes them incapable of simulating the real dynamic motion in real-life driving applications. In addition, the conservative behavior of autonomous vehicles, which often overreact to threats which have low likelihood, degrades the overall driving quality and jeopardizes safety. Hence, enhancing driving behavior is essential to attain agile, yet safe, traversing maneuvers in such multi-agent environments. Therefore, the main goal of conducting this PhD research is to develop high-fidelity learning-based frameworks to enhance the autonomous decision-making process at these safety-critical environments. We focus this PhD dissertation on three correlated and complementary research challenges. In our first research challenge, we conduct an in-depth and comprehensive survey on the state-of-the-art learning-based decision-making schemes with the objective of identifying the main shortcomings and potential research avenues. Based on the research directions concluded, we propose, in Problem II and Problem III, novel learning-based frameworks with the objective of enhancing safety and efficiency at different decision-making levels. In Problem II, we develop a novel sensor-independent state estimation for a safety-critical system in urban driving using deep learning techniques. A neural inference model is developed and trained via deep-learning training techniques to obtain accurate state estimates using indirect measurements of vehicle dynamic states and powertrain states. In Problem III, we propose a novel hierarchical reinforcement learning-based decision-making architecture for learning left-turn policies at four-way unsignalized intersections with feasibility guarantees. The proposed technique involves an integration of two main decision-making layers; a high-level learning-based behavioral planning layer which adopts soft actor-critic principles to learn high-level, non-conservative yet safe, driving behaviors, and a motion planning layer that uses low-level Model Predictive Control (MPC) principles to ensure feasibility of the two-dimensional left-turn maneuver. The high-level layer generates reference signals of velocity and yaw angle for the ego vehicle taking into account safety and collision avoidance with the intersection vehicles, whereas the low-level planning layer solves an optimization problem to track these reference commands considering several vehicle dynamic constraints and ride comfort

Integrating the Principles of Evidence Based Medicine and Evidence Based Public Health: Impact on the Quality of Patient Care and Hospital Readmission Rates in Jordan

Introduction: Hospital readmissions impose not only an extra burden on health care systems but impact patient health outcomes. Identifying modifiable behavioural risk factors that are possible causes of potentially avoidable readmissions can lower readmission rates and healthcare costs. Methods: Using the core principles of evidence based medicine and public health, the purpose of this study was to develop a heuristic guide that could identify what behavioural risk factors influence hospital readmissions through adopting various methods of analysis including regression models, t-tests, data mining, and logistic regression. This study was a retrospective cohort review of internal medicine patients admitted between December 1, 2012 and December 31, 2013 at King Abdullah University Hospital, in Jordan. Results: 29% of all hospitalized patients were readmitted during the study period. Among all readmissions, 44% were identified as potentially avoidable. Behavioural factors including smoking, unclear follow-up and discharge planning, and being non-compliant with treatment regimen as well as discharge against medical advice were all associated with increased risk of avoidable readmissions. Conclusion: Implementing evidence based health programs that focus on modifiable behavioural risk factors for both patients and clinicians would yield a higher response in terms of reducing potentially avoidable readmissions, and could reduce direct medical costs

Nursing Mothers' Experiences of Musculoskeletal Pain Attributed to Poor Posture During Breastfeeding: A Mixed Methods Study

Background: Breastfeeding has various benefits for infants and mothers. However, if not performed in the correct posture, prolonged breastfeeding could cause musculoskeletal-related symptoms such as shoulder, neck, and upper back pain. In Jordan, nursing mothers do not have access to a breastfeeding midwifery team, a breastfeeding dietician, or a breastfeeding nurse for advice and education. The primary aim of this study was to explore nursing mothers' experiences of breastfeeding-related musculoskeletal pain; secondary aims were to explore nursing mothers' awareness of recommended breastfeeding postures and their experience of education and advice about breastfeeding postures.Methods: A cross-sectional mixed methods study was conducted with nursing mothers in Jordan who have breastfed their babies for 6 months or more following normal delivery, using an online survey questionnaire and semi-structured interviews. Participants were recruited through general practitioner clinics.Results: Four hundred ninety-three nursing mothers submitted the online questionnaire, and 12 interviews were completed. Nursing mothers reported experiencing nonspecific pain in lower back, neck, shoulder, and hand, attributed to breastfeeding. Pain in these joints affected mood, sleep, working ability, and quality of life by limiting activities of daily living. Findings showed that the majority of Jordanian nursing mothers did not receive education about safe or optimal breastfeeding positioning from health professionals.Conclusions: Nursing mothers in Jordan are not receiving appropriate education or advice about optimal postures for breastfeeding and have reported experiencing musculoskeletal pain, attributed to breastfeeding, that interferes with activities of daily living and affects quality of life. Postural education and advice should be provided to nursing mothers to prevent or avoid development of musculoskeletal pain