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Description
Researchers and practitioners have widely studied road network traffic data in different areas such as urban planning, traffic prediction and spatial-temporal databases. For instance, researchers use such data to evaluate the impact of road network changes. Unfortunately, collecting large-scale high-quality urban traffic data requires tremendous efforts because participating vehicles must install Global Positioning System(GPS) receivers and administrators must continuously monitor these devices. There have been some urban traffic simulators trying to generate such data with different features. However, they suffer from two critical issues (1) Scalability: most of them only offer single-machine solution which is not adequate to produce large-scale data. Some simulators can generate traffic in parallel but do not well balance the load among machines in a cluster. (2) Granularity: many simulators do not consider microscopic traffic situations including traffic lights, lane changing, car following. This paper proposed GeoSparkSim, a scalable traffic simulator which extends Apache Spark to generate large-scale road network traffic datasets with microscopic traffic simulation. The proposed system seamlessly integrates with a Spark-based spatial data management system, GeoSpark, to deliver a holistic approach that allows data scientists to simulate, analyze and visualize large-scale urban traffic data. To implement microscopic traffic models, GeoSparkSim employs a simulation-aware vehicle partitioning method to partition vehicles among different machines such that each machine has a balanced workload. The experimental analysis shows that GeoSparkSim can simulate the movements of 200 thousand cars over an extensive road network (250 thousand road junctions and 300 thousand road segments).
ContributorsFu, Zishan (Author) / Sarwat, Mohamed (Thesis advisor) / Pedrielli, Giulia (Committee member) / Sefair, Jorge (Committee member) / Arizona State University (Publisher)
Created2019
Description
Polar ice masses can be valuable indicators of trends in global climate. In an effort to better understand the dynamics of Arctic ice, this project analyzes sea ice concentration anomaly data collected over gridded regions (cells) and builds graphs based upon high correlations between cells. These graphs offer the opportunity to use metrics such as clustering coefficients and connected components to isolate representative trends in ice masses. Based upon this analysis, the structure of sea ice graphs differs at a statistically significant level from random graphs, and several regions show erratically decreasing trends in sea ice concentration.
ContributorsWallace-Patterson, Chloe Rae (Author) / Syrotiuk, Violet (Thesis director) / Colbourn, Charles (Committee member) / Montgomery, Douglas (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor)
Created2013-05
Description
Automated driving systems (ADS) have come a long way since their inception. It is clear that these systems rely heavily on stochastic deep learning techniques for perception, planning, and prediction, as it is impossible to construct every possible driving scenario to generate driving policies. Moreover, these systems need to be trained and validated extensively on typical and abnormal driving situations before they can be trusted with human life. However, most publicly available driving datasets only consist of typical driving behaviors. On the other hand, there is a plethora of videos available on the internet that capture abnormal driving scenarios, but they are unusable for ADS training or testing as they lack important information such as camera calibration parameters, and annotated vehicle trajectories. This thesis proposes a new toolbox, DeepCrashTest-V2, that is capable of reconstructing high-quality simulations from monocular dashcam videos found on the internet. The toolbox not only estimates the crucial parameters such as camera calibration, ego-motion, and surrounding road user trajectories but also creates a virtual world in Car Learning to Act (CARLA) using data from OpenStreetMaps to simulate the estimated trajectories. The toolbox is open-source and is made available in the form of a python package on GitHub at https://github.com/C-Aniruddh/deepcrashtest_v2.
ContributorsChandratre, Aniruddh Vinay (Author) / Fainekos, Georgios (Thesis advisor) / Ben Amor, Hani (Thesis advisor) / Pedrielli, Giulia (Committee member) / Arizona State University (Publisher)
Created2022
Description
Generative Adversarial Networks (GANs) have emerged as a powerful framework for generating realistic and high-quality data. In the original ``vanilla'' GAN formulation, two models -- the generator and discriminator -- are engaged in a min-max game and optimize the same value function. Despite offering an intuitive approach, vanilla GANs often face stability challenges such as vanishing gradients and mode collapse. Addressing these common failures, recent work has proposed the use of tunable classification losses in place of traditional value functions. Although parameterized robust loss families, e.g. $\alpha$-loss, have shown promising characteristics as value functions, this thesis argues that the generator and discriminator require separate objective functions to achieve their different goals. As a result, this thesis introduces the $(\alpha_{D}, \alpha_{G})$-GAN, a parameterized class of dual-objective GANs, as an alternative approach to the standard vanilla GAN. The $(\alpha_{D}, \alpha_{G})$-GAN formulation, inspired by $\alpha$-loss, allows practitioners to tune the parameters $(\alpha_{D}, \alpha_{G}) \in [0,\infty)^{2}$ to provide a more stable training process. The objectives for the generator and discriminator in $(\alpha_{D}, \alpha_{G})$-GAN are derived, and the advantages of using these objectives are investigated. In particular, the optimization trajectory of the generator is found to be influenced by the choice of $\alpha_{D}$ and $\alpha_{G}$. Empirical evidence is presented through experiments conducted on various datasets, including the 2D Gaussian Mixture Ring, Celeb-A image dataset, and LSUN Classroom image dataset. Performance metrics such as mode coverage and Fréchet Inception Distance (FID) are used to evaluate the effectiveness of the $(\alpha_{D}, \alpha_{G})$-GAN compared to the vanilla GAN and state-of-the-art Least Squares GAN (LSGAN). The experimental results demonstrate that tuning $\alpha_{D} < 1$ leads to improved stability, robustness to hyperparameter choice, and competitive performance compared to LSGAN.
ContributorsOtstot, Kyle (Author) / Sankar, Lalitha (Thesis advisor) / Kosut, Oliver (Committee member) / Pedrielli, Giulia (Committee member) / Arizona State University (Publisher)
Created2023
Description
The notion of the safety of a system when placed in an environment with humans and other machines has been one of the primary concerns of practitioners while deploying any cyber-physical system (CPS). Such systems, also called safety-critical systems, need to be exhaustively tested for erroneous behavior. This generates the need for coming up with algorithms that can help ascertain the behavior and safety of the system by generating tests for the system where they are likely to falsify. In this work, three algorithms have been presented that aim at finding falsifying behaviors in cyber-physical Systems. PART-X intelligently partitions while sampling the input space to provide probabilistic point and region estimates of falsification. PYSOAR-C and LS-EMIBO aims at finding falsifying behaviors in gray-box systems when some information about the system is available. Specifically, PYSOAR-C aims to find falsification while maximizing coverage using a two-phase optimization process, while LS-EMIBO aims at exploiting the structure of a requirement to find falsifications with lower computational cost compared to the state-of-the-art. This work also shows the efficacy of the algorithms on a wide range of complex cyber-physical systems. The algorithms presented in this thesis are available as python toolboxes.
ContributorsKhandait, Tanmay Bhaskar (Author) / Pedrielli, Giulia (Thesis advisor) / Fainekos, Georgios (Thesis advisor) / Gopalan, Nakul (Committee member) / Arizona State University (Publisher)
Created2022
Description
In recent years, the development of Control Barrier Functions (CBF) has allowed safety guarantees to be placed on nonlinear control affine systems. While powerful as a mathematical tool, CBF implementations on systems with high relative degree constraints can become too computationally intensive for real-time control. Such deployments typically rely on the analysis of a system's symbolic equations of motion, leading to large, platform-specific control programs that do not generalize well. To address this, a more generalized framework is needed. This thesis provides a formulation for second-order CBFs for rigid open kinematic chains. An algorithm for numerically computing the safe control input of a CBF is then introduced based on this formulation. It is shown that this algorithm can be used on a broad category of systems, with specific examples shown for convoy platooning, drone obstacle avoidance, and robotic arms with large degrees of freedom. These examples show up to three-times performance improvements in computation time as well as 2-3 orders of magnitude in the reduction in program size.
ContributorsPietz, Daniel Johannes (Author) / Fainekos, Georgios (Thesis advisor) / Vrudhula, Sarma (Thesis advisor) / Pedrielli, Giulia (Committee member) / Pavlic, Theodore (Committee member) / Arizona State University (Publisher)
Created2022
Description
With the development of modern technological infrastructures, such as social networks or the Internet of Things (IoT), data is being generated at a speed that is never before seen. Analyzing the content of this data helps us further understand underlying patterns and discover relationships among different subsets of data, enabling intelligent decision making. In this thesis, I first introduce the Low-rank, Win-dowed, Incremental Singular Value Decomposition (SVD) framework to inclemently maintain SVD factors over streaming data. Then, I present the Group Incremental Non-Negative Matrix Factorization framework to leverage redundancies in the data to speed up incremental processing. They primarily tackle the challenges of using factorization models in the scenarios with streaming textual data. In order to tackle the challenges in improving the effectiveness and efficiency of generative models in this streaming environment, I introduce the Incremental Dynamic Multiscale Topic Model framework, which identifies multi-scale patterns and their evolutions within streaming datasets. While the latent factor models assume the linear independence in the latent factors, the generative models assume the observation is generated from a set of latent variables with various distributions. Furthermore, some models may not be accessible or their underlying structures are too complex to understand, such as simulation ensembles, where there may be thousands of parameters with a huge parameter space, the only way to learn information from it is to execute real simulations. When performing knowledge discovery and decision making through data- and model-driven simulation ensembles, it is expensive to operate these ensembles continuously at large scale, due to the high computational. Consequently, given a relatively small simulation budget, it is desirable to identify a sparse ensemble that includes the most informative simulations, while still permitting effective exploration of the input parameter space. Therefore, I present Complexity-Guided Parameter Space Sampling framework, which is an intelligent, top-down sampling scheme to select the most salient simulation parameters to execute, given a limited computational budget. Moreover, I also present a Pivot-Guided Parameter Space Sampling framework, which incrementally maintains a diverse ensemble of models of the simulation ensemble space and uses a pivot guided mechanism for future sample selection.
ContributorsChen, Xilun (Author) / Candan, K. Selcuk (Thesis advisor) / Davulcu, Hasan (Committee member) / Pedrielli, Giulia (Committee member) / Sapino, Maria Luisa (Committee member) / Tong, Hanghang (Committee member) / Arizona State University (Publisher)
Created2019
Description
Cyber-Physical Systems (CPS) are becoming increasingly prevalent around the world. Co-simulation of cyber and physical components has shown to be an effective way towards the development of time-sensitive and reliable CPS. Correctly combining continuous models with discrete models for co-simulation can often be challenging. In this thesis, the Functional Markup Interface (FMI) is used to develop an adapter called DEVS-FMI for the DEVS-Suite simulator. The adapter, implemented using JavaFMI 2.0, allows any Functional Mock-Up Unit (FMU) to be co-simulated with a Discrete Event System Specification (DEVS) model. This approach enables taking advantage of the parallel DEVS formalism to model cyber systems and using Modelica to model physical systems. An FMU serves as a slave simulator while the DEVS-Suite serves as a master simulator. The Four-Variable model is used as a guide to define the requirements for the inputs and outputs of actuator and sensor devices used in cyber and physical systems. The input and output data as non-functional abstractions of the sensor and actuator devices. Select cyber and physical parts of an electric scooter are chosen, modeled, simulated, and evaluated using the integrated OpenModelica and the DEVS-Suite simulators. Closely related research is briefly examined and expanding this work with support for implicit state-changes for continuous models and distributed co-simulation is noted.
ContributorsLin, Xuanli (Author) / Sarjoughian, Hessam S (Thesis advisor) / Pedrielli, Giulia (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2021
Description
The use of spatial data has become very fundamental in today's world. Ranging from fitness trackers to food delivery services, almost all application records users' location information and require clean geospatial data to enhance various application features. As spatial data flows in from heterogeneous sources various problems arise. The study of entity matching has been a fervent step in the process of producing clean usable data. Entity matching is an amalgamation of various sub-processes including blocking and matching. At the end of an entity matching pipeline, we get deduplicated records of the same real-world entity. Identifying various mentions of the same real-world locations is known as spatial entity matching. While entity matching received significant interest in the field of relational entity matching, the same cannot be said about spatial entity matching. In this dissertation, I build an end-to-end Geospatial Entity Matching framework, GEM, exploring spatial entity matching from a novel perspective. In the current state-of-the-art systems spatial entity matching is only done on one type of geometrical data variant. Instead of confining to matching spatial entities of only point geometry type, I work on extending the boundaries of spatial entity matching to match the more generic polygon geometry entities as well. I propose a methodology to provide support for three entity matching scenarios across different geometrical data types: point X point, point X polygon, polygon X polygon. As mentioned above entity matching consists of various steps but blocking, feature vector creation, and classification are the core steps of the system. GEM comprises an efficient and lightweight blocking technique, GeoPrune, that uses the geohash encoding mechanism to prune away the obvious non-matching spatial entities. Geohashing is a technique to convert a point location coordinates to an alphanumeric code string. This technique proves to be very effective and swift for the blocking mechanism. I leverage the Apache Sedona engine to create the feature vectors. Apache Sedona is a spatial database management system that holds the capacity of processing spatial SQL queries with multiple geometry types without compromising on their original coordinate vector representation. In this step, I re-purpose the spatial proximity operators (SQL queries) in Apache Sedona to create spatial feature dimensions that capture the proximity between a geospatial entity pair. The last step of an entity matching process is matching or classification. The classification step in GEM is a pluggable component, which consumes the feature vector for a spatial entity pair and determines whether the geolocations match or not. The component provides 3 machine learning models that consume the same feature vector and provide a label for the test data based on the training. I conduct experiments with the three classifiers upon multiple large-scale geospatial datasets consisting of both spatial and relational attributes. Data considered for experiments arrives from heterogeneous sources and we pre-align its schema manually. GEM achieves an F-measure of 1.0 for a point X point dataset with 176k total pairs, which is 42% higher than a state-of-the-art spatial EM baseline. It achieves F-measures of 0.966 and 0.993 for the point X polygon dataset with 302M total pairs, and the polygon X polygon dataset with 16M total pairs respectively.
ContributorsShah, Setu Nilesh (Author) / Sarwat, Mohamed (Thesis advisor) / Pedrielli, Giulia (Committee member) / Boscovic, Dragan (Committee member) / Arizona State University (Publisher)
Created2021
Description
Complex systems appear when interaction among system components creates emergent behavior that is difficult to be predicted from component properties. The growth of Internet of Things (IoT) and embedded technology has increased complexity across several sectors (e.g., automotive, aerospace, agriculture, city infrastructures, home technologies, healthcare) where the paradigm of cyber-physical systems (CPSs) has become a standard. While CPS enables unprecedented capabilities, it raises new challenges in system design, certification, control, and verification. When optimizing system performance computationally expensive simulation tools are often required, and search algorithms that sequentially interrogate a simulator to learn promising solutions are in great demand. This class of algorithms are black-box optimization techniques. However, the generality that makes black-box optimization desirable also causes computational efficiency difficulties when applied real problems. This thesis focuses on Bayesian optimization, a prominent black-box optimization family, and proposes new principles, translated in implementable algorithms, to scale Bayesian optimization to highly expensive, large scale problems. Four problem contexts are studied and approaches are proposed for practically applying Bayesian optimization concepts, namely: (1) increasing sample efficiency of a highly expensive simulator in the presence of other sources of information, where multi-fidelity optimization is used to leverage complementary information sources; (2) accelerating global optimization in the presence of local searches by avoiding over-exploitation with adaptive restart behavior; (3) scaling optimization to high dimensional input spaces by integrating Game theoretic mechanisms with traditional techniques; (4) accelerating optimization by embedding function structure when the reward function is a minimum of several functions. In the first context this thesis produces two multi-fidelity algorithms, a sample driven and model driven approach, and is implemented to optimize a serial production line; in the second context the Stochastic Optimization with Adaptive Restart (SOAR) framework is produced and analyzed with multiple applications to CPS falsification problems; in the third context the Bayesian optimization with sample fictitious play (BOFiP) algorithm is developed with an implementation in high-dimensional neural network training; in the last problem context the minimum surrogate optimization (MSO) framework is produced and combined with both Bayesian optimization and the SOAR framework with applications in simultaneous falsification of multiple CPS requirements.
ContributorsMathesen, Logan (Author) / Pedrielli, Giulia (Thesis advisor) / Candan, Kasim (Committee member) / Fainekos, Georgios (Committee member) / Gel, Esma (Committee member) / Montgomery, Douglas (Committee member) / Zabinsky, Zelda (Committee member) / Arizona State University (Publisher)
Created2021