Lu Zhao

Graduation Semester and Year

2023

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Electrical Engineering

Department

Electrical Engineering

First Advisor

Yan Wan

Abstract

ABSTRACT: Multiagent systems (MAS) are ubiquitous in modern systems and have found broad applications, such as in intelligent transportation systems (ITS). Environment, communication and decision are among the essential components of MAS. The realistic environment in which MAS operates is usually stochastic, and its modeling, identification and estimation are important to consider. The communication in MAS is also critical for decisions. For example, in ITS, to improve travel efficiency and reduce traffic accidents, scheduling schemes for Vehicle-to-Everything (V2X) communication need to be developed. MAS decisions also need to be robust to uncertainties. For example, in mixed-traffic autonomous driving, the decisions for autonomous vehicles need to take into consideration human drivers’ uncertain behaviors to avoid crash and ensue safe driving. This dissertation contributes to the MAS research in the aforementioned three aspects: environment, communication and decision. In the first thrust of the dissertation, we capture the stochastic spatiotemporal environment in which the MAS operates using a discrete-time stochastic model, namely the influence model (IM). The identifiability and estimation of IMs with reduced computation for real MAS applications are thoroughly studied in this dissertation, considering, first, a specific network topology (i.e., the uniform completely connected homogeneous networks), second, general homogeneous and heterogeneous networks, and finally, partially observed IMs (POIMs). Compared with using the standard master Markov chain approach for estimation, our proposed approaches are much more computationally efficient. In addition, per the authors’ knowledge, our work is the first in the literature that studies the identifiability of heterogeneous IMs and heterogeneous POIMs. In the second thrust of the dissertation, we study sub-6 GHz assisted mmWave scheduling and design a distributed V2X communication scheduling scheme with multiples head nodes for long highway traffic. The long highway is divided into contiguous and non-overlapping sections, and a head node within each section collects mmWave link requests, runs the scheduler and coordinates with each other to achieve conflict-free schedules. A decomposition-based approximate solution is developed to address the intra-section computational scalability. Two coordination schemes are designed to address the inter-section communication scalability, and to achieve an overall conflict-free transmission schedule with low control overhead. In the third thrust, we propose a stochastic hierarchical game (SHG) to support safe and efficient autonomous driving decision under uncertain intentions in mixed-traffic scenarios. First, a random mobility model (RMM) is developed to capture the uncertain intentions of MAS, including the random switching behavior. Then, an efficient sampling-based uncertainty evaluation technique, named the multivariate probabilistic collocation method integrated with an orthogonal fractional factorial design (MPCM-OFFD) is leveraged to solve the SHG with reduced computation by using a limited number of sample scenarios while guaranteeing the safety.

Keywords

Multiagent systems (MAS), Stochastic environment, Communication scheduling, Game theoretic-based decision-making

Disciplines

Electrical and Computer Engineering | Engineering

Comments

Degree granted by The University of Texas at Arlington

Download

Available for download on Friday, August 01, 2025

Share

COinS