Matching Items (34)
Description
Autonomous Vehicles (AVs) have the potential to significantly evolve transportation. AVs are expected to make transportation safer by avoiding accidents that happen due to human errors. When AVs become connected, they can exchange information with the infrastructure or other Connected Autonomous Vehicles (CAVs) to efficiently plan their future motion and therefore, increase the road throughput and reduce energy consumption. Cooperative algorithms for CAVs will not be deployed in real life unless they are proved to be safe, robust, and resilient to different failure models. Since intersections are crucial areas where most accidents happen, this dissertation first focuses on making existing intersection management algorithms safe and resilient against network and computation time, bounded model mismatches and external disturbances, and the existence of a rogue vehicle. Then, a generic algorithm for conflict resolution and cooperation of CAVs is proposed that ensures the safety of vehicles even when other vehicles suddenly change their plan. The proposed approach can also detect deadlock situations among CAVs and resolve them through a negotiation process. A testbed consisting of 1/10th scale model CAVs is built to evaluate the proposed algorithms. In addition, a simulator is developed to perform tests at a large scale. Results from the conducted experiments indicate the robustness and resilience of proposed approaches.
ContributorsKhayatian, Mohammad (Author) / Shrivastava, Aviral (Thesis advisor) / Fainekos, Georgios (Committee member) / Ben Amor, Heni (Committee member) / Yang, Yezhou (Committee member) / Lou, Yingyan (Committee member) / Iannucci, Bob (Committee member) / Arizona State University (Publisher)
Created2021
Description
Models that learn from data are widely and rapidly being deployed today for real-world use, and have become an integral and embedded part of human lives. While these technological advances are exciting and impactful, such data-driven computer vision systems often fail in inscrutable ways. This dissertation seeks to study and improve the reliability of machine learning models from several perspectives including the development of robust training algorithms to mitigate the risks of such failures, construction of new datasets that provide a new perspective on capabilities of vision models, and the design of evaluation metrics for re-calibrating the perception of performance improvements. I will first address distribution shift in image classification with the following contributions: (1) two methods for improving the robustness of image classifiers to distribution shift by leveraging the classifier's failures into an adversarial data transformation pipeline guided by domain knowledge, (2) an interpolation-based technique for flagging out-of-distribution samples, and (3) an intriguing trade-off between distributional and adversarial robustness resulting from data modification strategies. I will then explore reliability considerations for \textit{semantic vision} models that learn from both visual and natural language data; I will discuss how logical and semantic sentence transformations affect the performance of vision--language models and my contributions towards developing knowledge-guided learning algorithms to mitigate these failures. Finally, I will describe the effort towards building and evaluating complex reasoning capabilities of vision--language models towards the long-term goal of robust and reliable computer vision models that can communicate, collaborate, and reason with humans.
ContributorsGokhale, Tejas (Author) / Yang, Yezhou (Thesis advisor) / Baral, Chitta (Thesis advisor) / Ben Amor, Heni (Committee member) / Anirudh, Rushil (Committee member) / Arizona State University (Publisher)
Created2023
Description
Robot motion and control remains a complex problem both in general and inthe field of machine learning (ML). Without ML approaches, robot controllers are
typically designed manually, which can take considerable time, generally requiring
accounting for a range of edge cases and often producing models highly constrained
to specific tasks. ML can decrease the time it takes to create a model while simultaneously allowing it to operate on a broader range of tasks. The utilization of neural
networks to learn from demonstration is, in particular, an approach with growing
popularity due to its potential to quickly fit the parameters of a model to mimic
training data.
Many such neural networks, especially in the realm of transformer-based architectures, act more as planners, taking in an initial context and then generating a
sequence from that context one step at a time. Others hybridize the approach, predicting a latent plan and conditioning immediate actions on that plan. Such approaches may limit a model’s ability to interact with a dynamic environment, needing to replan to fully update its understanding of the environmental context. In this
thesis, Language-commanded Scene-aware Action Response (LanSAR) is proposed as
a reactive transformer-based neural network that makes immediate decisions based
on previous actions and environmental changes. Its actions are further conditioned
on a language command, serving as a control mechanism while also narrowing the
distribution of possible actions around this command. It is shown that LanSAR successfully learns a strong representation of multimodal visual and spatial input, and
learns reasonable motions in relation to most language commands. It is also shown
that LanSAR can struggle with both the accuracy of motions and understanding the
specific semantics of language commands
ContributorsHardy, Adam (Author) / Ben Amor, Heni (Thesis advisor) / Srivastava, Siddharth (Committee member) / Pavlic, Theodore (Committee member) / Arizona State University (Publisher)
Created2024
Description
Aquifers host the largest accessible freshwater resource in the world. However, groundwater reserves are declining in many places. Often coincident with drought, high extraction rates and inadequate replenishment result in groundwater overdraft and permanent land subsidence. Land subsidence is the cause of aquifer storage capacity reduction, altered topographic gradients which can exacerbate floods, and differential displacement that can lead to earth fissures and infrastructure damage. Improving understanding of the sources and mechanisms driving aquifer deformation is important for resource management planning and hazard mitigation.
Poroelastic theory describes the coupling of differential stress, strain, and pore pressure, which are modulated by material properties. To model these relationships, displacement time series are estimated via satellite interferometry and hydraulic head levels from observation wells provide an in-situ dataset. In combination, the deconstruction and isolation of selected time-frequency components allow for estimating aquifer parameters, including the elastic and inelastic storage coefficients, compaction time constants, and vertical hydraulic conductivity. Together these parameters describe the storage response of an aquifer system to changes in hydraulic head and surface elevation. Understanding aquifer parameters is useful for the ongoing management of groundwater resources.
Case studies in Phoenix and Tucson, Arizona, focus on land subsidence from groundwater withdrawal as well as distinct responses to artificial recharge efforts. In Christchurch, New Zealand, possible changes to aquifer properties due to earthquakes are investigated. In Houston, Texas, flood severity during Hurricane Harvey is linked to subsidence, which modifies base flood elevations and topographic gradients.
Poroelastic theory describes the coupling of differential stress, strain, and pore pressure, which are modulated by material properties. To model these relationships, displacement time series are estimated via satellite interferometry and hydraulic head levels from observation wells provide an in-situ dataset. In combination, the deconstruction and isolation of selected time-frequency components allow for estimating aquifer parameters, including the elastic and inelastic storage coefficients, compaction time constants, and vertical hydraulic conductivity. Together these parameters describe the storage response of an aquifer system to changes in hydraulic head and surface elevation. Understanding aquifer parameters is useful for the ongoing management of groundwater resources.
Case studies in Phoenix and Tucson, Arizona, focus on land subsidence from groundwater withdrawal as well as distinct responses to artificial recharge efforts. In Christchurch, New Zealand, possible changes to aquifer properties due to earthquakes are investigated. In Houston, Texas, flood severity during Hurricane Harvey is linked to subsidence, which modifies base flood elevations and topographic gradients.
ContributorsMiller, Megan Marie (Author) / Shirzaei, Manoochehr (Thesis advisor) / Reynolds, Stephen (Committee member) / Tyburczy, James (Committee member) / Semken, Steven (Committee member) / Werth, Susanna (Committee member) / Arizona State University (Publisher)
Created2018
Description
The movement between tectonic plates is accommodated through brittle (elastic) displacement on the plate boundary faults and ductile permanent deformation on the fault borderland. The elastic displacement along the fault can occur in the form of either large seismic events or aseismic slip, known as fault creep. Fault creep mainly occurs at the deep ductile portion of the crust, where the temperature is high. Nonetheless, aseismic creep can also occur on the shallow brittle portion of the fault segments that are characterized by frictionally weak material, elevated pore fluid pressure, or geometrical complexity. Creeping segments are assumed to safely release the accumulated strain(Kodaira et al., 2004; Rice, 1992)(Kodaira et al., 2004; Rice, 1992)(Kodaira et al., 2004; Rice, 1992)(Kodaira et al., 2004; Rice, 1992)(Kodaira et al., 2004; Rice, 1992) on the fault and also impede propagation of the seismic rupture. The rate of aseismic slip on creeping faults, however, might not be steady in time and instead consist of successive periods of acceleration and deceleration, known as slow slip events (SSEs). SSEs, which aseismically release the strain energy over a period of days to months, rather than the seconds to minutes characteristic of a typical earthquake, have been interpreted as earthquake precursors and as possible triggering factor for major earthquakes. Therefore, understanding the partitioning of seismic and aseismic fault slip and evolution of creep is fundamental to constraining the fault earthquake potential and improving operational seismic hazard models. Thanks to advances in tectonic geodesy, it is now possible to detect the fault movement in high spatiotemporal resolution and develop kinematic models of the creep evolution on the fault to determine the budget of seismic and aseismic slip.
In this dissertation, I measure the decades-long time evolution of fault-related crustal deformation along the San Andrea Fault in California and the northeast Japan subduction zone using space-borne geodetic techniques, such as Global Positioning System (GPS) and Interferometric Synthetic Aperture Radar (InSAR). The surface observation of deformation combined with seismic data set allow constraining the time series of creep distribution on the fault surface at seismogenic depth. The obtained time-dependent kinematic models reveal that creep in both study areas evolves through a series of SSEs, each lasting for several months. Using physics-based models informed by laboratory experiments, I show that the transient elevation of pore fluid pressure is the driving mechanism of SSEs. I further investigate the link between SSEs and evolution of seismicity on neighboring locked segments, which has implications for seismic hazard models and also provides insights into the pattern of microstructure on the fault surface. I conclude that while creeping segments act as seismic rupture barriers, SSEs on these zones might promote seismicity on adjacent seismogenic segments, thus change the short-term earthquake forecast.
In this dissertation, I measure the decades-long time evolution of fault-related crustal deformation along the San Andrea Fault in California and the northeast Japan subduction zone using space-borne geodetic techniques, such as Global Positioning System (GPS) and Interferometric Synthetic Aperture Radar (InSAR). The surface observation of deformation combined with seismic data set allow constraining the time series of creep distribution on the fault surface at seismogenic depth. The obtained time-dependent kinematic models reveal that creep in both study areas evolves through a series of SSEs, each lasting for several months. Using physics-based models informed by laboratory experiments, I show that the transient elevation of pore fluid pressure is the driving mechanism of SSEs. I further investigate the link between SSEs and evolution of seismicity on neighboring locked segments, which has implications for seismic hazard models and also provides insights into the pattern of microstructure on the fault surface. I conclude that while creeping segments act as seismic rupture barriers, SSEs on these zones might promote seismicity on adjacent seismogenic segments, thus change the short-term earthquake forecast.
ContributorsKhoshmanesh, Mostafa (Author) / Shirzaei, Manoochehr (Thesis advisor) / Arrowsmith, Ramon (Committee member) / Garnero, Edward (Committee member) / Tyburczy, James (Committee member) / Whipple, Kelin (Committee member) / Arizona State University (Publisher)
Created2018
Description
Reinforcement learning (RL) is a powerful methodology for teaching autonomous agents complex behaviors and skills. A critical component in most RL algorithms is the reward function -- a mathematical function that provides numerical estimates for desirable and undesirable states. Typically, the reward function must be hand-designed by a human expert and, as a result, the scope of a robot's autonomy and ability to safely explore and learn in new and unforeseen environments is constrained by the specifics of the designed reward function. In this thesis, I design and implement a stateful collision anticipation model with powerful predictive capability based upon my research of sequential data modeling and modern recurrent neural networks. I also develop deep reinforcement learning methods whose rewards are generated by self-supervised training and intrinsic signals. The main objective is to work towards the development of resilient robots that can learn to anticipate and avoid damaging interactions by combining visual and proprioceptive cues from internal sensors. The introduced solutions are inspired by pain pathways in humans and animals, because such pathways are known to guide decision-making processes and promote self-preservation. A new "robot dodge ball' benchmark is introduced in order to test the validity of the developed algorithms in dynamic environments.
ContributorsRichardson, Trevor W (Author) / Ben Amor, Heni (Thesis advisor) / Yang, Yezhou (Committee member) / Srivastava, Siddharth (Committee member) / Arizona State University (Publisher)
Created2018
Description
The dynamic Earth involves feedbacks between the solid crust and both natural and anthropogenic fluid flows. Fluid-rock interactions drive many Earth phenomena, including volcanic unrest, seismic activities, and hydrological responses. Mitigating the hazards associated with these activities requires fundamental understanding of the underlying physical processes. Therefore, geophysical monitoring in combination with modeling provides valuable tools, suitable for hazard mitigation and risk management efforts. Magmatic activities and induced seismicity linked to fluid injection are two natural and anthropogenic processes discussed in this dissertation.
Successful forecasting of the timing, style, and intensity of a volcanic eruption is made possible by improved understanding of the volcano life cycle as well as building quantitative models incorporating the processes that govern rock melting, melt ascending, magma storage, eruption initiation, and interaction between magma and surrounding host rocks at different spatial extent and time scale. One key part of such models is the shallow magma chamber, which is generally directly linked to volcano’s eruptive behaviors. However, its actual shape, size, and temporal evolution are often not entirely known. To address this issue, I use space-based geodetic data with high spatiotemporal resolution to measure surface deformation at Kilauea volcano. The obtained maps of InSAR (Interferometric Synthetic Aperture Radar) deformation time series are exploited with two novel modeling schemes to investigate Kilauea’s shallow magmatic system. Both models can explain the same observation, leading to a new compartment model of magma chamber. Such models significantly advance the understanding of the physical processes associated with Kilauea’s summit plumbing system with potential applications for volcanoes around the world.
The unprecedented increase in the number of earthquakes in the Central and Eastern United States since 2008 is attributed to massive deep subsurface injection of saltwater. The elevated chance of moderate-large damaging earthquakes stemming from increased seismicity rate causes broad societal concerns among industry, regulators, and the public. Thus, quantifying the time-dependent seismic hazard associated with the fluid injection is of great importance. To this end, I investigate the large-scale seismic, hydrogeologic, and injection data in northern Texas for period of 2007-2015 and in northern-central Oklahoma for period of 1995-2017. An effective induced earthquake forecasting model is developed, considering a complex relationship between injection operations and consequent seismicity. I find that the timing and magnitude of regional induced earthquakes are fully controlled by the process of fluid diffusion in a poroelastic medium and thus can be successfully forecasted. The obtained time-dependent seismic hazard model is spatiotemporally heterogeneous and decreasing injection rates does not immediately reduce the probability of an earthquake. The presented framework can be used for operational induced earthquake forecasting. Information about the associated fundamental processes, inducing conditions, and probabilistic seismic hazards has broad benefits to the society.
Successful forecasting of the timing, style, and intensity of a volcanic eruption is made possible by improved understanding of the volcano life cycle as well as building quantitative models incorporating the processes that govern rock melting, melt ascending, magma storage, eruption initiation, and interaction between magma and surrounding host rocks at different spatial extent and time scale. One key part of such models is the shallow magma chamber, which is generally directly linked to volcano’s eruptive behaviors. However, its actual shape, size, and temporal evolution are often not entirely known. To address this issue, I use space-based geodetic data with high spatiotemporal resolution to measure surface deformation at Kilauea volcano. The obtained maps of InSAR (Interferometric Synthetic Aperture Radar) deformation time series are exploited with two novel modeling schemes to investigate Kilauea’s shallow magmatic system. Both models can explain the same observation, leading to a new compartment model of magma chamber. Such models significantly advance the understanding of the physical processes associated with Kilauea’s summit plumbing system with potential applications for volcanoes around the world.
The unprecedented increase in the number of earthquakes in the Central and Eastern United States since 2008 is attributed to massive deep subsurface injection of saltwater. The elevated chance of moderate-large damaging earthquakes stemming from increased seismicity rate causes broad societal concerns among industry, regulators, and the public. Thus, quantifying the time-dependent seismic hazard associated with the fluid injection is of great importance. To this end, I investigate the large-scale seismic, hydrogeologic, and injection data in northern Texas for period of 2007-2015 and in northern-central Oklahoma for period of 1995-2017. An effective induced earthquake forecasting model is developed, considering a complex relationship between injection operations and consequent seismicity. I find that the timing and magnitude of regional induced earthquakes are fully controlled by the process of fluid diffusion in a poroelastic medium and thus can be successfully forecasted. The obtained time-dependent seismic hazard model is spatiotemporally heterogeneous and decreasing injection rates does not immediately reduce the probability of an earthquake. The presented framework can be used for operational induced earthquake forecasting. Information about the associated fundamental processes, inducing conditions, and probabilistic seismic hazards has broad benefits to the society.
ContributorsZhai, Guang (Author) / Shirzaei, Manoochehr (Thesis advisor) / Garnero, Edward (Committee member) / Clarke, Amanda (Committee member) / Tyburczy, James (Committee member) / Li, Mingming (Committee member) / Arizona State University (Publisher)
Created2018
Description
This dissertation is focused on developing an algorithm to provide current state estimation and future state predictions for biomechanical human walking features. The goal is to develop a system which is capable of evaluating the current action a subject is taking while walking and then use this to predict the future states of biomechanical features.
This work focuses on the exploration and analysis of Interaction Primitives (Amor er al, 2014) and their relevance to biomechanical prediction for human walking. Built on the framework of Probabilistic Movement Primitives, Interaction Primitives utilize an EKF SLAM algorithm to localize and map a distribution over the weights of a set of basis functions. The prediction properties of Bayesian Interaction Primitives were utilized to predict real-time foot forces from a 9 degrees of freedom IMUs mounted to a subjects tibias. This method shows that real-time human biomechanical features can be predicted and have a promising link to real-time controls applications.
This work focuses on the exploration and analysis of Interaction Primitives (Amor er al, 2014) and their relevance to biomechanical prediction for human walking. Built on the framework of Probabilistic Movement Primitives, Interaction Primitives utilize an EKF SLAM algorithm to localize and map a distribution over the weights of a set of basis functions. The prediction properties of Bayesian Interaction Primitives were utilized to predict real-time foot forces from a 9 degrees of freedom IMUs mounted to a subjects tibias. This method shows that real-time human biomechanical features can be predicted and have a promising link to real-time controls applications.
ContributorsClark, Geoffrey Mitchell (Author) / Ben Amor, Heni (Thesis advisor) / Si, Jennie (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2018
Description
This thesis presents a family of adaptive curvature methods for gradient-based stochastic optimization. In particular, a general algorithmic framework is introduced along with a practical implementation that yields an efficient, adaptive curvature gradient descent algorithm. To this end, a theoretical and practical link between curvature matrix estimation and shrinkage methods for covariance matrices is established. The use of shrinkage improves estimation accuracy of the curvature matrix when data samples are scarce. This thesis also introduce several insights that result in data- and computation-efficient update equations. Empirical results suggest that the proposed method compares favorably with existing second-order techniques based on the Fisher or Gauss-Newton and with adaptive stochastic gradient descent methods on both supervised and reinforcement learning tasks.
ContributorsBarron, Trevor (Author) / Ben Amor, Heni (Thesis advisor) / He, Jingrui (Committee member) / Levihn, Martin (Committee member) / Arizona State University (Publisher)
Created2019
Description
Automated driving systems are in an intensive research and development stage, and the companies developing these systems are targeting to deploy them on public roads in a very near future. Guaranteeing safe operation of these systems is crucial as they are planned to carry passengers and share the road with other vehicles and pedestrians. Yet, there is no agreed-upon approach on how and in what detail those systems should be tested. Different organizations have different testing approaches, and one common approach is to combine simulation-based testing with real-world driving.
One of the expectations from fully-automated vehicles is never to cause an accident. However, an automated vehicle may not be able to avoid all collisions, e.g., the collisions caused by other road occupants. Hence, it is important for the system designers to understand the boundary case scenarios where an autonomous vehicle can no longer avoid a collision. Besides safety, there are other expectations from automated vehicles such as comfortable driving and minimal fuel consumption. All safety and functional expectations from an automated driving system should be captured with a set of system requirements. It is challenging to create requirements that are unambiguous and usable for the design, testing, and evaluation of automated driving systems. Another challenge is to define useful metrics for assessing the testing quality because in general, it is impossible to test every possible scenario.
The goal of this dissertation is to formalize the theory for testing automated vehicles. Various methods for automatic test generation for automated-driving systems in simulation environments are presented and compared. The contributions presented in this dissertation include (i) new metrics that can be used to discover the boundary cases between safe and unsafe driving conditions, (ii) a new approach that combines combinatorial testing and optimization-guided test generation methods, (iii) approaches that utilize global optimization methods and random exploration to generate critical vehicle and pedestrian trajectories for testing purposes, (iv) a publicly-available simulation-based automated vehicle testing framework that enables application of the existing testing approaches in the literature, including the new approaches presented in this dissertation.
One of the expectations from fully-automated vehicles is never to cause an accident. However, an automated vehicle may not be able to avoid all collisions, e.g., the collisions caused by other road occupants. Hence, it is important for the system designers to understand the boundary case scenarios where an autonomous vehicle can no longer avoid a collision. Besides safety, there are other expectations from automated vehicles such as comfortable driving and minimal fuel consumption. All safety and functional expectations from an automated driving system should be captured with a set of system requirements. It is challenging to create requirements that are unambiguous and usable for the design, testing, and evaluation of automated driving systems. Another challenge is to define useful metrics for assessing the testing quality because in general, it is impossible to test every possible scenario.
The goal of this dissertation is to formalize the theory for testing automated vehicles. Various methods for automatic test generation for automated-driving systems in simulation environments are presented and compared. The contributions presented in this dissertation include (i) new metrics that can be used to discover the boundary cases between safe and unsafe driving conditions, (ii) a new approach that combines combinatorial testing and optimization-guided test generation methods, (iii) approaches that utilize global optimization methods and random exploration to generate critical vehicle and pedestrian trajectories for testing purposes, (iv) a publicly-available simulation-based automated vehicle testing framework that enables application of the existing testing approaches in the literature, including the new approaches presented in this dissertation.
ContributorsTuncali, Cumhur Erkan (Author) / Fainekos, Georgios (Thesis advisor) / Ben Amor, Heni (Committee member) / Kapinski, James (Committee member) / Shrivastava, Aviral (Committee member) / Arizona State University (Publisher)
Created2019