Theses and Dissertations from UMD
Permanent URI for this communityhttp://hdl.handle.net/1903/2
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item Safe Navigation of Autonomous Vehicles in Structured Mixed-Traffic Environments(2023) Tariq, Faizan Muhammad; Baras, John S; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The primary driving force behind autonomous vehicle (AV) research is the prospect of enhancing road safety by preventing accidents caused by human errors. To that end, it seems rather improbable that AVs will replace all human-driven vehicles in the near future. The more plausible scenario is that AVs will gradually be introduced on public roads and highways in the presence of human-driven vehicles, leading to mixed-traffic scenarios. In addition to the existing challenges associated with autonomous driving stemming from various uncertainty factors associated with sensing, prediction, control, and computation, these situations pose further difficulties pertaining to the variability in human driving patterns. Therefore, to ensure widespread public acceptance of AVs, it is crucial to develop planning and decision-making algorithms, while benefiting from modern sensing, computation, and control methods, that can deliver safe, efficient, and reliable performance in mixed-traffic situations. Considering the need to cater to the behavior variability of human drivers, we address the joint decision-making and motion planning problem in structured environments with a multi-timescale navigation architecture. Specifically, we design algorithms for commonly encountered highway driving scenarios that require effective real-time decision-making, reliable motion prediction of on-road entities, behavior consideration of on-road agents, and attention to safety as well as passenger comfort. The specific problems addressed in this dissertation include bidirectional highway overtaking, highway maneuvering in traffic, and crash mitigation on highways. In the proposed framework, we first identify and exploit the different timescales involved in the navigation architecture. Then, we propose algorithmic modules while pursuing systematic complexity (data and computation) reduction at different timescales to gain immediate performance improvements in inference and action/response delay minimization. This leads to real-time situation assessment, computation, and action/control, allowing us to satisfy some of the key requirements for autonomous driving algorithms. Notably, the algorithms proposed in this dissertation ensure that the safety of the overall system is a fundamental constraint built into the system. Distinctive features of the proposed approaches include real-time operation capability, consideration for behavior variability of on-road agents, modularity in design, and optimality with respect to various metrics. The algorithms developed and implemented as part of this dissertation fundamentally rely upon the application of optimization techniques in a receding horizon fashion which allows for optimality in performance while explicitly accounting for actuation limits, vehicle dynamics, and safety. Even though the modularity of the proposed navigation framework allows for the incorporation of modern prediction and control methods, we develop various prediction modules for the trajectory prediction of on-road agents. We further benefit from risk evaluation methodologies to ensure robustness to behavior variability of human drivers on the road and handle collision-prone situations. In the spirit of emulating real-world situations, we place special emphasis on utilizing realistic driving simulations that capture the effects of communication delays between different modules, limitations in computation resources, and randomization of scenarios. For the developed algorithms, we evaluate the performance in comparative singular case studies as well as randomized Monte Carlo simulations with respect to several metrics to assess the efficacy of the developed methods.Item Efficient Algorithms for Coordinated Motion in Shared Spaces(2020) Dasler, Philip; Mount, David M; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The steady development of autonomous systems motivates a number of interesting algorithmic problems. These systems, such as self-driving cars, must contend with far more complex and dynamic environments than factory floor robots of the past. This dissertation seeks to identify optimization problems that are simple enough to analyze formally, yet realistic enough to contribute to the eventual design of systems extant in real-world, physical spaces. With that in mind, this work examines three problems in particular: automated vehicles and unregulated traffic crossings, a smart network for city-wide traffic flow, and an online organizational scheme for automated warehouses. First, the Traffic Crossing Problem is introduced, in which a set of n axis-aligned vehicles moving monotonically in the plane must reach their goal positions within a time limit and subject to a maximum speed limit. It is shown that this problem is NP-complete and efficient algorithms for two special cases are given. Next, motivated by a problem of computing a periodic schedule for traffic lights in an urban transportation network, the problem of Circulation with Modular Demands is introduced. A novel variant of the well-known minimum-cost circulation problem in directed networks, in this problem vertex demand values are taken from the integers modulo λ, for some integer λ≥2. This modular circulation problem is solvable in polynomial time when λ=2, but the problem is NP-hard when λ≥3. For this case, a polynomial time approximation algorithm is provided. Finally, a theoretical model for organizing portable storage units in a warehouse subject to an online sequence of access requests is proposed. Complicated by the unknown request frequencies of stored products, the warehouse must be arranged with care. Analogous to virtual-memory systems, the more popular and oft-requested an item is, the more efficient its retrieval should be. Two formulations are considered, dependent on the number of access points to which storage units must be brought, and algorithms that are O(1)-competitive with respect to an optimal algorithm are given. Additionally, in the case of a single access point, the solution herein is asymptotically optimal with respect to density.Item ADAPTIVE SAMPLING METHODS FOR TESTING AUTONOMOUS SYSTEMS(2018) Mullins, Galen Edward; Gupta, Satyandra K; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this dissertation, I propose a software-in-the-loop testing architecture that uses adaptive sampling to generate test suites for intelligent systems based upon identifying transitions in high-level mission criteria. Simulation-based testing depends on the ability to intelligently create test-cases that reveal the greatest information about the performance of the system in the fewest number of runs. To this end, I focus on the discovery and analysis of performance boundaries. Locations in the testing space where a small change in the test configuration leads to large changes in the vehicle's behavior. These boundaries can be used to characterize the regions of stable performance and identify the critical factors that affect autonomous decision making software. By creating meta-models which predict the locations of these boundaries we can efficiently query the system and find informative test scenarios. These algorithms form the backbone of the Range Adversarial Planning Tool (RAPT): a software system used at naval testing facilities to identify the environmental triggers that will cause faults in the safety behavior of unmanned underwater vehicles (UUVs). This system was used to develop UUV field tests which were validated on a hardware platform at the Keyport Naval Testing Facility. The development of test cases from simulation to deployment in the field required new analytical tools. Tools that were capable of handling uncertainty in the vehicle's performance, and the ability to handle large datasets with high-dimensional outputs. This approach has also been applied to the generation of self-righting plans for unmanned ground vehicles (UGVs) using topological transition graphs. In order to create these graphs, I had to develop a set of manifold sampling and clustering algorithms which could identify paths through stable regions of the configuration space. Finally, I introduce an imitation learning approach for generating surrogate models of the target system's control policy. These surrogate agents can be used in place of the true autonomy to enable faster than real-time simulations. These novel tools for experimental design and behavioral modeling provide a new way of analyzing the performance of robotic and intelligent systems, and provide a designer with actionable feedback.Item Comparison of Optic Flow in the Visible Light and Infrared Specturms(2008) Chinn, Michael William; Humbert, James S; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Insects use a method of Wide Field Integration (WFI) to navigate efficiently through unknown environments. Using these natural paradigms, various WFI based forms of navigation can be implemented based on electro-mechanical vision devices on robotic vehicles. However, under low light and/or suspended particles in the environment, these methods become less useful. One solution to this problem is to use infrared vision sensors rather than visible light sensors. This would allow insect-like navigation for autonomous vehicles under a variety of lighting conditions, including a total lack of visible light. The results show that, using infrared sensors, it is possible to navigate under a variety of lighting conditions, even where visible light sensors become ineffective.