Consensus Confererence on Autonomous Vehicles: Case Study

Library Student Mini Grant Award Year: 2017-2018https://deepblue.lib.umich.edu/bitstream/2027.42/146527/1/CCAVCaseStudy.pd

Supercurrent conservation in the lattice Wess-Zumino model with Ginsparg-Wilson fermions

We study supercurrent conservation for the four-dimensional Wess-Zumino model formulated on the lattice. The formulation is one that has been discussed several times, and uses Ginsparg-Wilson fermions of the overlap (Neuberger) variety, together with an auxiliary fermion (plus superpartners), such that a lattice version of U(1)_R symmetry is exactly preserved in the limit of vanishing bare mass. We show that the almost naive supercurrent is conserved at one loop. By contrast we find that this is not true for Wilson fermions and a canonical scalar action. We provide nonperturbative evidence for the nonconservation of the supercurrent in Monte Carlo simulations.Comment: 19 pages, 5 figure

Quantum Monte Carlo Methods and Extensions for the 2D Hubbard Model

This thesis will describe efforts to enhance our ability to simulate the 2D Hubbard model. Chapter 2 provides a background on the main computational techniques used throughout the work, including quantum Monte Carlo (QMC), dynamical mean field theory (DMFT), the dynamical cluster approximation (DCA), the continuous time auxiliary field algorithm (CTAUX), and the Maximum Entropy Method (MEM) for numeric analytic continuation. Chapter 3 presents new work on applying Twisted Boundary Conditions to the DCA framework. This method is applied in a effort to access thermodynamic (i.e. large system) information about the Hubbard model without the immense computational expense required to simulate large lattices directly. Chapter 4 describes efforts to study the extended 2D Hubbard model, which re-introduces non-local interactions between electrons that drive the formation of charge ordered phases. The work includes a thorough analysis of the phase diagram of the model away from half-filling, as well as analysis of the effect of non-local interactions on anti-ferromagnetic fluctuations and competition between the charge order and AFM states. Chapter 5 includes a derivation of the dual fermions diagrammatic expansion and the dual fermions ladder approximation used to compute corrections to single site DMFT calculations. The method is applied to the 2D Hubbard model in order to study the evolution of its spectral function, Fermi surface, and momentum dependent mass renormalization as the temperature, interaction strength, and doping are changed. The great advantage of this method is that we are able to access arbitrary momenta throughout the Brillouin zone, in contrast to the limitation to a few cluster momenta that characterize cluster methods like DCA.PHDPhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/151444/1/oryx_1.pd

Multi-timescale Sensor Fusion and Control

Networked autonomous systems are a rapidly expanding area of research and development across academic, commercial, and military endeavors. Significant challenges exist in extending traditional detection and estimation methods to such distributed systems of sensors when we relax assumptions on full communications connectivity and global observability of the network. Global observability can be interpreted as a persistent coverage of all degrees of freedom associated with a object\u27s feature vector-this can be satisfied by a combination of physical diversity of homogeneous sensors and/or diversity across sensing domains for heterogeneous sensors, and the role of resource allocation across the network is to determine configurations, and reconfigurations, of platforms that achieve said diversity. In a general heterogeneous sensor network, persistent global observability across the entire area of operations requires control decisions at a much longer timescale than the feature estimate updates that provide locally full rank observability. In this paper, we temporally separate the long-timescale resource allocation control process from the parameter estimation through the use of a decentralized Partially Observable Markov Decision Process (POMDP) control model that employs consensus estimates on object features as observations and benchmark this multi-timescale approach against centralized Linear Quadratic Gaussian (LQG) control for a fully connected network with simultaneous estimation and control updates

Kernel Based Method for Distributed Derived Feature Tracking in High Dimensions

Modern sensing systems are increasingly heterogeneous and decentralized. These systems require new methods for efficiently combining data across distributed networks when centralized data fusion centers are impractical due to communications limitations. Consensus and innovation algorithms are a class of algorithms for fusing sensor data over distributed networks without the need for full connectivity to a centralized system. We present a novel method for combining a consensus and innovation framework with kernel density estimation to track complex non-observable features of targets over a high dimensional space. The goal of our method is to track multiple targets over time while categorizing their long term behavior. Instantaneous features of the targets are used both as tracking tools and combined over time to establish higher-order features of the targets\u27 long term behavior. We assume that the communication bandwidth in the network is low, and that real-time identification of specific long term behaviors, such as a pattern of suspicious activity, is a priority. We compare the capabilities and limitations of our method with common modern tracking methods including particle filtering and multi-hypothesis testing. Results are given for an example scenario of a heterogeneous set of sensors identifying a suspicious target vehicle from traffic data. The instantaneous measured features include the location, color, speed, and fuel consumption

Flexible Architecture for Testing Connected Vehicles in Realistic Traffic

Connected vehicles have the potential to transform the way we commute and travel in a multitude of ways. Vehicles will cooperate and coordinate with each other to solve problems appropriate for the environment in which they are operating. In this paper, we focus on the development of test equipment that includes the infrastructure and vehicles to measure and record all of the information necessary to quantify the performance of cooperative driving algorithms in realistic scenarios. The system allows tests to include real vehicles on the track and virtual vehicles in a digital twin. Real and virtual vehicles interact through the road-side units and test facility network, allowing each test vehicle to receive messages from virtual vehicles as well as the infrastructure. Messages transmitted from the test vehicles are received in the digital twin, allowing the real vehicle to interact with virtual vehicles. This provides the capability to test algorithms in congested traffic without the expense and risk of conducting tests with many cars. The system is shown to allow for real-time operation of connected vehicles in closed loop operation using industry standard networks, along with a protocol for centralized traffic management, which is not currently standardized. Tests have been performed at highway speeds. The architecture has a low barrier to entry application programming interface for its vehicle to infrastructure network that utilizes the Robotic Operating System interface. The paper describes the development and integration of components and protocols, characterization of the network performance, methods for recording data referenced to a single clock, and demonstration of the repeatability of measurements made on test vehicles. The discussion at the end of the paper looks at current research on the impact of cooperative driving algorithms on energy efficiency and traffic flow