Efficient Automated Driving Strategies Leveraging Anticipation and Optimal Control

Automated vehicles and advanced driver assistance systems bring computation, sensing, and communication technologies that exceed human abilities in some ways. For example, automated vehicles may sense a panorama all at once, do not suffer from human impairments and distractions, and could wirelessly communicate precise data with neighboring vehicles. Prototype and commercial deployments have demonstrated the capability to relieve human operators of some driving tasks up to and including fully autonomous taxi rides in some areas. The ultimate impact of this technology’s large-scale market penetration on energy efficiency remains unclear, with potential negative factors like road use by empty vehicles competing with positive ones like automatic eco-driving. Fundamentally enabled by historic and look-ahead data, this dissertation addresses the use of automated driving and driver assistance to optimize vehicle motion for energy efficiency. Facets of this problem include car following, co-optimized acceleration and lane change planning, and collaborative multi-agent guidance. Optimal control, especially model predictive control, is used extensively to improve energy efficiency while maintaining safe and timely driving via constraints. Techniques including chance constraints and mixed integer programming help overcome uncertainty and non-convexity challenges. Extensions of these techniques to tractor trailers on sloping roads are provided by making use of linear parameter-varying models. To approach the wheel-input energy eco-driving problem over generally shaped sloping roads with the computational potential for closed-loop implementation, a linear programming formulation is constructed. Distributed and collaborative techniques that enable connected and automated vehicles to accommodate their neighbors in traffic are also explored and compared to centralized control. Using simulations and vehicle-in-the-loop car following experiments, the proposed algorithms are benchmarked against others that do not make use of look-ahead information

Collisionless Plasma Turbulence: Insights from Magnetohydrodynamic and Hall Magnetohydrodynamic Simulations and Observations of the Earth's Magnetosphere

Turbulence is a ubiquitous phenomenon that occurs throughout the universe, in both neutral fluids and plasmas. For collisionless plasmas, kinetic effects, which alter the nonlinear dynamics and result in small-scale dissipation, are still not well understood in the context of turbulence. This work uses direct numerical simulations (DNS) and observations of Earth's magnetosphere to study plasma turbulence. Long-time relaxation in magnetohydrodynamic (MHD) turbulence is examined using DNS with particular focus on the role of magnetic and cross helicity and symmetries of the initial con-figurations. When strong symmetries are absent or broken through perturbations, flows evolve towards states predicted by statistical mechanics with an energy minimization principle, which features two main regimes; one magnetic helicity dominated and one with quasi-equipartition of kinetic and magnetic energy. The role of the Hall effect, which contributes to the dynamics of collisionless plasmas, is also explored numerically. At scales below the ion inertial length, a transition to a magnetically dominated state, associated with advection becoming subdominant to dissipation, occurs. Real-space current, vorticity, and electric fields are examined. Strong current structures are associated with alignment between the current and magnetic field, which may be important in collisionless plasmas where field-aligned currents can be unstable. Turbulence within bursty bulk ow braking events, thought to be associated with near-Earth magnetotail reconnection, are then studied using the THEMIS spacecraft. It is proposed that strong field-aligned currents associated with turbulent intermittency destabilize into double layers, providing a collisionless dissipation mechanism for the turbulence. Plasma waves may also radiate from the region, removing energy from the turbulence and potentially depositing it in the aurora. Finally, evidence for turbulence in the Kelvin-Helmholtz instability (KHI) on the Earth's magnetopause is found using data from the Magnetospheric Multiscale (MMS) mission. With MMS, spatial properties, including spatial intermittency and anisotropy, can be examined along with temporal properties and ion and electron velocity spectra can be examined observationally into the kinetic scales. Quasi-two-dimensional anisotropy perpendicular to the magnetic field is found. Field-aligned current instabilities and wave radiation may also be relevant in the KHI

CEAS/AIAA/ICASE/NASA Langley International Forum on Aeroelasticity and Structural Dynamics 1999

The proceedings of a workshop sponsored by the Confederation of European Aerospace Societies (CEAS), the American Institute of Aeronautics and Astronautics (AIAA), the National Aeronautics and Space Administration (NASA), Washington, D.C., and the Institute for Computer Applications in Science and Engineering (ICASE), Hampton, Virginia, and held in Williamsburg, Virginia June 22-25, 1999 represent a collection of the latest advances in aeroelasticity and structural dynamics from the world community. Research in the areas of unsteady aerodynamics and aeroelasticity, structural modeling and optimization, active control and adaptive structures, landing dynamics, certification and qualification, and validation testing are highlighted in the collection of papers. The wide range of results will lead to advances in the prediction and control of the structural response of aircraft and spacecraft

Federated Machine Learning in Edge Computing

Machine Learning (ML) is transforming the way that computers are used to solve problems in computer vision, natural language processing, scientific modelling, and much more. The rising number of devices connected to the Internet generate huge quantities of data that can be used for ML purposes. Traditionally, organisations require user data to be uploaded to a single location (i.e., cloud datacentre) for centralised ML. However, public concerns regarding data-privacy are growing, and in some domains such as healthcare, there exist strict laws governing the access of data. The computational power and connectivity of devices at the network edge is also increasing: edge computing is a paradigm designed to move computation from the cloud to the edge to reduce latency and traffic. Federated Learning (FL) is a new and swiftly-developing field that has huge potential for privacy-preserving ML. In FL, edge devices collaboratively train a model without users sharing their personal data with any other party. However, there exist multiple challenges for designing useful FL algorithms, including: the heterogeneity of data across participating clients; the low computing power, intermittent connectivity and unreliability of clients at the network edge compared to the datacentre; and the difficulty of limiting information leakage whilst still training high-performance models. This thesis proposes new methods for improving the process of FL in edge computing and hence making it more practical for real-world deployments. First, a novel approach is designed that accelerates the convergence of the FL model through adaptive optimisation, reducing the time taken to train a model, whilst lowering the total quantity of information uploaded from edge clients to the coordinating server through two new compression strategies. Next, a Multi-Task FL framework is proposed that allows participating clients to train unique models that are tailored to their own heterogeneous datasets whilst still benefiting from FL, improving model convergence speed and generalisation performance across clients. Then, the principle of decreasing the total work that clients perform during the FL process is explored. A theoretical analysis (and subsequent experimental evaluation) suggests that this approach can reduce the time taken to reach a desired training error whilst lowering the total computational cost of FL and improving communication-efficiency. Lastly, an algorithm is designed that applies adaptive optimisation to FL in a novel way, through the use of a statistically-biased optimiser whose values are kept fixed on clients. This algorithm can leverage the convergence guarantees of centralised algorithms, with the addition of FL-related error-terms. Furthermore, it shows excellent performance on benchmark FL datasets whilst possessing lower computation and upload costs compared to competing adaptive-FL algorithms

A super-set of Patterson-Wiedemann functions – Upper bounds and possible nonlinearities

Construction of Boolean functions on an odd number of variables with nonlinearity exceeding the bent concatenation bound is one of the most difficult combinatorial problems within the domain of Boolean functions. This problem also has deep implications in coding theory and cryptology. Patterson and Wiedemann demonstrated instances of such functions back in 1983. For more than three decades efforts have been channeled into obtaining such instances. For the first time, in this paper we explore nontrivial upper bounds on nonlinearity for such classes of functions that are invariant not only under several group actions but also for larger sets of functions than what have been considered so far. Further, we present tight upper bounds on the nonlinearity in several cases. To support our claims, we present computational results for functions on n variables, where n is an odd composite integer in the interval [9, 39]. In particular, our results for n = 15 and 21 are of immediate interest given recent research results in this domain. In addition to the upper bounds, we also discover the nonlinearities that can actually be achieved above the bent concatenation bound for such a class of functions. Finally, we obtain all possible values in the absolute Walsh spectra of the functions considered