Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
Displaying 1 - 2 of 2
Filtering by
- Creators: Yang, Yezhou
Description
To date, there is not a standardized method for consistently quantifying the performance of an automated driving system (ADS)-equipped vehicle (AV). The purpose of this dissertation is to contribute to a framework for such an approach referred to throughout as the operational safety assessment (OSA) methodology. Through this research, safety metrics are identified, researched, and analyzed to capture aspects of the operational safety of AVs, interacting with other salient objects. This dissertation outlines the approach for developing this methodology through a series of key steps including: (1) comprehensive literature review; (2) research and refinement of OSA metrics; (3) generation of MATLAB script for metric calculations; (4) generation of simulated events for analysis; (5) collection of real-world data for analysis; (6) review of OSA methodology results; and (7) discussion of future work to expand complexity, fidelity, and relevance aspects of the OSA methodology. The detailed literature review includes the identification of metrics historically used in both traditional and more recent evaluations of vehicle performance. Subsequently, the metric formulations are refined, and robust severity evaluations are proposed. A MATLAB script is then presented which was generated to calculate the metrics from any given source assuming proper formatting of the data. To further refine the formulations and the MATLAB script, a variety of simulated scenarios are discussed including car-following, intersection, and lane change situations. Additionally, a data collection activity is presented, leveraging the SMARTDRIVE testbed operated by Maricopa County Department of Transportation in Anthem, AZ to collect real-world data from an active intersection. Lastly, the efficacy of the OSA methodology with respect to the evaluation of vehicle performance for a set of scenarios is evaluated utilizing both simulated and real-world data. This assessment provides a demonstration of the ability and robustness of this methodology to evaluate vehicle performance for a given scenario. At the conclusion of this dissertation, additional factors including fidelity, complexity, and relevance are explored to contribute to a more comprehensive evaluation.
ContributorsComo, Steven Gerard (Author) / Wishart, Jeffrey (Thesis advisor) / Yang, Yezhou (Thesis advisor) / Chen, Yan (Committee member) / Favaro, Francesca (Committee member) / Arizona State University (Publisher)
Created2022
Description
Image Understanding is a long-established discipline in computer vision, which encompasses a body of advanced image processing techniques, that are used to locate (“where”), characterize and recognize (“what”) objects, regions, and their attributes in the image. However, the notion of “understanding” (and the goal of artificial intelligent machines) goes beyond factual recall of the recognized components and includes reasoning and thinking beyond what can be seen (or perceived). Understanding is often evaluated by asking questions of increasing difficulty. Thus, the expected functionalities of an intelligent Image Understanding system can be expressed in terms of the functionalities that are required to answer questions about an image. Answering questions about images require primarily three components: Image Understanding, question (natural language) understanding, and reasoning based on knowledge. Any question, asking beyond what can be directly seen, requires modeling of commonsense (or background/ontological/factual) knowledge and reasoning.
Knowledge and reasoning have seen scarce use in image understanding applications. In this thesis, we demonstrate the utilities of incorporating background knowledge and using explicit reasoning in image understanding applications. We first present a comprehensive survey of the previous work that utilized background knowledge and reasoning in understanding images. This survey outlines the limited use of commonsense knowledge in high-level applications. We then present a set of vision and reasoning-based methods to solve several applications and show that these approaches benefit in terms of accuracy and interpretability from the explicit use of knowledge and reasoning. We propose novel knowledge representations of image, knowledge acquisition methods, and a new implementation of an efficient probabilistic logical reasoning engine that can utilize publicly available commonsense knowledge to solve applications such as visual question answering, image puzzles. Additionally, we identify the need for new datasets that explicitly require external commonsense knowledge to solve. We propose the new task of Image Riddles, which requires a combination of vision, and reasoning based on ontological knowledge; and we collect a sufficiently large dataset to serve as an ideal testbed for vision and reasoning research. Lastly, we propose end-to-end deep architectures that can combine vision, knowledge and reasoning modules together and achieve large performance boosts over state-of-the-art methods.
Knowledge and reasoning have seen scarce use in image understanding applications. In this thesis, we demonstrate the utilities of incorporating background knowledge and using explicit reasoning in image understanding applications. We first present a comprehensive survey of the previous work that utilized background knowledge and reasoning in understanding images. This survey outlines the limited use of commonsense knowledge in high-level applications. We then present a set of vision and reasoning-based methods to solve several applications and show that these approaches benefit in terms of accuracy and interpretability from the explicit use of knowledge and reasoning. We propose novel knowledge representations of image, knowledge acquisition methods, and a new implementation of an efficient probabilistic logical reasoning engine that can utilize publicly available commonsense knowledge to solve applications such as visual question answering, image puzzles. Additionally, we identify the need for new datasets that explicitly require external commonsense knowledge to solve. We propose the new task of Image Riddles, which requires a combination of vision, and reasoning based on ontological knowledge; and we collect a sufficiently large dataset to serve as an ideal testbed for vision and reasoning research. Lastly, we propose end-to-end deep architectures that can combine vision, knowledge and reasoning modules together and achieve large performance boosts over state-of-the-art methods.
ContributorsAditya, Somak (Author) / Baral, Chitta (Thesis advisor) / Yang, Yezhou (Thesis advisor) / Aloimonos, Yiannis (Committee member) / Lee, Joohyung (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2018