Matching Items (3)
Filtering by
- All Subjects: psychology
- All Subjects: Africa, Sub-Saharan
Description
The Autonomous Vehicle (AV), also known as self-driving car, promises to be a game changer for the transportation industry. This technology is predicted to drastically reduce the number of traffic fatalities due to human error [21].
However, road driving at any reasonable speed involves some risks. Therefore, even with high-tech AV algorithms and sophisticated sensors, there may be unavoidable crashes due to imperfection of the AV systems, or unexpected encounters with wildlife, children and pedestrians. Whenever there is a risk involved, there is the need for an ethical decision to be made [33].
While ethical and moral decision-making in humans has long been studied by experts, the advent of artificial intelligence (AI) also calls for machine ethics. To study the different moral and ethical decisions made by humans, experts may use the Trolley Problem [34], which is a scenario where one must pull a switch near a trolley track to redirect the trolley to kill one person on the track or do nothing, which will result in the deaths of five people. While it is important to take into account the input of members of a society and perform studies to understand how humans crash during unavoidable accidents to help program moral and ethical decision-making into self-driving cars, using the classical trolley problem is not ideal, as it is unrealistic and does not represent moral situations that people face in the real world.
This work seeks to increase the realism of the classical trolley problem for use in studies on moral and ethical decision-making by simulating realistic driving conditions in an immersive virtual environment with unavoidable crash scenarios, to investigate how drivers crash during these scenarios. Chapter 1 gives an in-depth background into autonomous vehicles and relevant ethical and moral problems; Chapter 2 describes current state-of-the-art online tools and simulators that were developed to study moral decision-making during unavoidable crashes. Chapters 3 focuses on building the simulator and the design of the crash scenarios. Chapter 4 describes human subjects experiments that were conducted with the simulator and their results, and Chapter 5 provides conclusions and avenues for future work.
However, road driving at any reasonable speed involves some risks. Therefore, even with high-tech AV algorithms and sophisticated sensors, there may be unavoidable crashes due to imperfection of the AV systems, or unexpected encounters with wildlife, children and pedestrians. Whenever there is a risk involved, there is the need for an ethical decision to be made [33].
While ethical and moral decision-making in humans has long been studied by experts, the advent of artificial intelligence (AI) also calls for machine ethics. To study the different moral and ethical decisions made by humans, experts may use the Trolley Problem [34], which is a scenario where one must pull a switch near a trolley track to redirect the trolley to kill one person on the track or do nothing, which will result in the deaths of five people. While it is important to take into account the input of members of a society and perform studies to understand how humans crash during unavoidable accidents to help program moral and ethical decision-making into self-driving cars, using the classical trolley problem is not ideal, as it is unrealistic and does not represent moral situations that people face in the real world.
This work seeks to increase the realism of the classical trolley problem for use in studies on moral and ethical decision-making by simulating realistic driving conditions in an immersive virtual environment with unavoidable crash scenarios, to investigate how drivers crash during these scenarios. Chapter 1 gives an in-depth background into autonomous vehicles and relevant ethical and moral problems; Chapter 2 describes current state-of-the-art online tools and simulators that were developed to study moral decision-making during unavoidable crashes. Chapters 3 focuses on building the simulator and the design of the crash scenarios. Chapter 4 describes human subjects experiments that were conducted with the simulator and their results, and Chapter 5 provides conclusions and avenues for future work.
ContributorsKankam, Immanuella (Author) / Berman, Spring (Thesis advisor) / Johnson, Kathryn (Committee member) / Yong, Sze Zheng (Committee member) / Arizona State University (Publisher)
Created2019
Description
This study investigated work-family conflict and related phenomena reported by female teachers in primary and secondary schools in Kenya. Specifically, it sought to first identify general work and family stressors and profession specific stressors, and how these stressors influenced teachers’ work-family conflict (WFC) and burnout. Second, it investigated whether support from home and work reduced these teachers’ perceived work-family conflict and burnout. Third, it investigated the impact of marital status, number and ages of children, length of teaching experience, and school location (city vs town) on perceived work-family conflict (WFC).
In this study, 375 female teachers from Nairobi and three towns completed a survey questionnaire with both closed- and open-ended questions. Data analysis was conducted through descriptive and inferential statistics, and content analyses of qualitative data. There were five primary findings. (1) Teachers clearly identified and described stressors that led to work-family conflict: inability to get reliable support from domestic workers, a sick child, high expectations of a wife at home, high workloads at school and home, low schedule flexibility, and number of days teachers spend at school beyond normal working hours, etc.
(2) Work-family conflict experienced was cyclical in nature. Stressors influenced WFC, which led to adverse outcomes. These outcomes later acted as secondary stressors. (3) The culture of the school and school’s resources influenced the level of support that teachers received. The level of WFC support that teachers received depended on the goodwill of supervisors and colleagues.
(4) Work-family conflict contributed to emotional exhaustion, cynicism, and professional efficacy. Time and emotional investment in students’ parents was related to emotional exhaustion; time and emotional investment in students’ behavior, the number of years teaching experience, and number of children were related to professional efficacy. Support from teachers’ spouses enabled teachers to cope with cynicism.
(5) While marital status did not influence WFC, school location did; teachers in Nairobi experienced more WFC than those in small towns. The study highlighted the importance of culture in studies of work-family conflict, as some of the stressors and WFC experiences identified seemed unique to the Kenyan context. Finally, theoretical implications, policy recommendations, and further research directions are presented.
In this study, 375 female teachers from Nairobi and three towns completed a survey questionnaire with both closed- and open-ended questions. Data analysis was conducted through descriptive and inferential statistics, and content analyses of qualitative data. There were five primary findings. (1) Teachers clearly identified and described stressors that led to work-family conflict: inability to get reliable support from domestic workers, a sick child, high expectations of a wife at home, high workloads at school and home, low schedule flexibility, and number of days teachers spend at school beyond normal working hours, etc.
(2) Work-family conflict experienced was cyclical in nature. Stressors influenced WFC, which led to adverse outcomes. These outcomes later acted as secondary stressors. (3) The culture of the school and school’s resources influenced the level of support that teachers received. The level of WFC support that teachers received depended on the goodwill of supervisors and colleagues.
(4) Work-family conflict contributed to emotional exhaustion, cynicism, and professional efficacy. Time and emotional investment in students’ parents was related to emotional exhaustion; time and emotional investment in students’ behavior, the number of years teaching experience, and number of children were related to professional efficacy. Support from teachers’ spouses enabled teachers to cope with cynicism.
(5) While marital status did not influence WFC, school location did; teachers in Nairobi experienced more WFC than those in small towns. The study highlighted the importance of culture in studies of work-family conflict, as some of the stressors and WFC experiences identified seemed unique to the Kenyan context. Finally, theoretical implications, policy recommendations, and further research directions are presented.
ContributorsMuasya, Gladys (Author) / Martin, Judith (Thesis advisor) / Mongeau, Paul (Committee member) / Walumbwa, Fred (Committee member) / Arizona State University (Publisher)
Created2015
Description
Autonomous Vehicles (AVs), or self-driving cars, are poised to have an enormous impact on the automotive industry and road transportation. While advances have been made towards the development of safe, competent autonomous vehicles, there has been inadequate attention to the control of autonomous vehicles in unanticipated situations, such as imminent crashes. Even if autonomous vehicles follow all safety measures, accidents are inevitable, and humans must trust autonomous vehicles to respond appropriately in such scenarios. It is not plausible to program autonomous vehicles with a set of rules to tackle every possible crash scenario. Instead, a possible approach is to align their decision-making capabilities with the moral priorities, values, and social motivations of trustworthy human drivers.Toward this end, this thesis contributes a simulation framework for collecting, analyzing, and replicating human driving behaviors in a variety of scenarios, including imminent crashes. Four driving scenarios in an urban traffic environment were designed in the CARLA driving simulator platform, in which simulated cars can either drive autonomously or be driven by a user via a steering wheel and pedals. These included three unavoidable crash scenarios, representing classic trolley-problem ethical dilemmas, and a scenario in which a car must be driven through a school zone, in order to examine driver prioritization of reaching a destination versus ensuring safety. Sample human driving data in CARLA was logged from the simulated car’s sensors, including the LiDAR, IMU and camera. In order to reproduce human driving behaviors in a simulated vehicle, it is necessary for the AV to be able to identify objects in the environment and evaluate the volume of their bounding boxes for prediction and planning. An object detection method was used that processes LiDAR point cloud data using the PointNet neural network architecture, analyzes RGB images via transfer learning using the Xception convolutional neural network architecture, and fuses the outputs of these two networks. This method was trained and tested on both the KITTI Vision Benchmark Suite dataset and a virtual dataset exclusively generated from CARLA. When applied to the KITTI dataset, the object detection method achieved an average classification accuracy of 96.72% and an average Intersection over Union (IoU) of 0.72, where the IoU metric compares predicted bounding boxes to those used for training.
ContributorsGovada, Yashaswy (Author) / Berman, Spring (Thesis advisor) / Johnson, Kathryn (Committee member) / Marvi, Hamidreza (Committee member) / Arizona State University (Publisher)
Created2020