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I conducted this study to better understand the greater roadrunner’s (Geococcyx californianus) spatial use, nest site selection, and sexual morphometrics. Data were first collected from a roadrunner population in Fisher County, Texas. I trapped, measured, and removed feathers for sex confirmation through DNA PCR analysis. I then fit the roadrunners with a radio transmitter and released them at the capture site. I captured a total of 10 birds (1 male and 9 females) and triangulated the location of each bird 2-4 times per week from February to August 2011 during the roadrunner nesting season. The data produced a mean minimum convex polygon nesting range of 43.01-ha, a 50% core utilization distributions of 11.88-ha, and an overlap index of 33.05 percent. Habitat selection ratios showed a preference for ridge as well as grassland habitat and avoidance of bare ground and flatland habitats. The location data from west Texas was further analyzed to build a predictive logistic regression model to understand the significant site characteristics in roadrunner land use during the nesting season. It was determined that percent rock (-0.12, P = 0.0001) and percent litter (-0.05, P = 0.0052) were the best predictors in determining actual from random locations. Rock references open area for hunting, transportation as well as ridge habitat. Litter alludes to region below dense stands of shrubs and is used by roadrunners for evasion from threats and as shade for thermoregulation. The measurements from the roadrunners in west Texas as well as museum specimens from across their natural range were combined to develop an easy, inexpensive, and field-relevant sex identification model. The strongest model consisted of bill depth (0.79, P< 0.0001) and bill tip to the back of the head (0.05, P = 0.1573) which were both found to be larger in males. The final portion of this study concerned roadrunner nest site selection. Data were collected in Wilbarger County, Texas from May 2006 to October 2009. A predictive logistic regression model of nesting locations determined that mesquite nesting tree (-0.89, P = 0.0064), oval tree shape (1.58, P = 0.0118), mesquite density (-0.004, P = 0.0080), and topographic edge (1.37, P = 0.0027) were the best predictors in determining actual nest sites from random locations.

abstract: 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. / Dissertation/Thesis / Masters Thesis Mechanical Engineering 2020

This master thesis deals with the design and implementation of dynamic analyzer of parametrized contracts . In the first part of the thesis , the problematics of testing of parallel programs are discussed and issues when dealing with parallelism are described . Further , methods how to reveal concurrency bugs via dynamic analysis are described , in particular FastTrack and Contract validator. The second part of the thesis proposes an extension for RoadRunner framework and SearchBestie platform for contract validator with parameters.

Contracts for concurrency describe required atomicity of method sequences in concurrent programs. This work proposes a dynamic analyzer to verify programs written in Java against contracts for concurrency. The analyzer was designed to detect violations of parametric contracts with spoilers. The proposed analyzer was implemented as an extension to the RoadRunner framework. Support for accessing the method arguments and return values was added to RoadRunner as a part of the solution. The analyzer was fully implemented and verified on a set of testing programs.