Matching Items (2)
158843-Thumbnail Image.png
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

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
165938-Thumbnail Image.png
Description

This paper will demonstrate that the Agile development process helps to ensure incremental work on an Unreal Engine game project is achieved by presenting a product produced in Unreal Engine along with my experience in utilizing Scrum to facilitate the game’s development. Section 2 discusses project goals and motivations for

This paper will demonstrate that the Agile development process helps to ensure incremental work on an Unreal Engine game project is achieved by presenting a product produced in Unreal Engine along with my experience in utilizing Scrum to facilitate the game’s development. Section 2 discusses project goals and motivations for using Agile, using Unreal Engine, and for the choice of genre in the final product. Section 3 contextualizes these goals by presenting the history of Unreal Engine, the novel applications of Unreal Engine, and the use of Unreal Engine in the development of Heady Stuff. Section 4 presents findings from the project’s development by describing my use of Agile and by presenting the steps taken in learning Unreal Engine. Section 4 continues by highlighting important development considerations in the use of Blueprints, C++, and HLSL in Unreal Engine. The section ends with the presentation of project feedback, its incorporation in the final product, and the resources used to assist development. Section 5 compares the workflow, help resources, and applications of Unreal Engine with those of Unity, another highly popular game engine. Lastly, Section 6 performs a post-mortem on the overall development process by considering how well Agile development processes were upheld along with how much of the original plans in the Design Document was present in the final product. Additionally, the section presents the major challenges encountered during project development. These challenges will help in proposing possible best practices for game development in Unreal Engine.

ContributorsHreshchyshyn, Jacob (Author) / Acuna, Ruben (Thesis director) / Hentges, John (Committee member) / Barrett, The Honors College (Contributor) / Software Engineering (Contributor) / Computing and Informatics Program (Contributor)
Created2022-05