This thesis presents disturbance estimators and controllers for autonomous vehicles. In particular, it focuses on a longitudinal distance controller and a lateral lane keeping controller. First, in order to estimate road bank angle as a disturbance term in the lane keeping controller, a kinematic relationship between road shape and sensor measurements was proposed. Utilizing longitudinal and lateral vehicle dynamics, longitudinal road gradient and lateral road bank angle were estimated simultaneously using the Unscented Kalman Filter (UKF) approach. Second, a lane keeping controller associated with the road bank angle estimator was proposed. For the controller, a steady state dynamic vehicle model was derived to describe lateral vehicle dynamics. A Receding Horizon Sliding Control (RHSC) approach was implemented to guarantee simple formulation and easy constraint consideration for the receding horizon technique. For the longitudinal control systems, the front vehicle's future motion was considered as a disturbance term in a longitudinal distance controller for the ego vehicle. To predict the motion, a new car-following model was proposed. To extract the current front vehicle driver's driving style, a driver aggressivity factor was derived and estimated in real-time through the UKF approach. Adopting a base car-following model and an aggressivity factor estimator on the front vehicle, the front vehicle's future motion sequence was propagated. Furthermore, as a distance controller associated with the front vehicle's future motion, a Fuel Eciency Adaptive Cruise Control (ACC) was presented. A new fuel consumption model was included in the optimization problem in order to improve fuel eciency. The nonlinear Model Predictive Control approach was applied to the controller, and the front vehicle's future motion was considered in the prediction horizon. Two disturbance estimators for longitudinal and lateral motion were veried under simulation and real vehicle tests in real-time. The lane keeping controller was proven to have better performance with the bank angle estimator on public roads. Furthermore, for a distance controller, fuel economy using a Fuel Eciency ACC has been veried in simulation.

Self-driving vehicles have attracted a lot of interest due to their potential to significantly reduce traffic fatalities and transform people's lives. The reducing costs of advanced sensing technologies and the increasing capabilities of embedded computing hardware have enabled the commercialization of highly automated driving features. However, the reliable operation of autonomous vehicles is still a challenge and a major barrier in the large scale acceptance and deployment of the technology. This dissertation focuses on the challenges of designing safe control strategies for self-driving vehicles due to the presence of uncertainty arising from the non-deterministic forecasts of the driving scene. The overall goal is to unify elements from the fields of vehicle dynamics modeling, machine learning, real-time optimization and control design under uncertainty to enable the safe operation of self-driving vehicles. We propose a systematic framework based on Model Predictive Control (MPC) for the controller design, the effectiveness of which is demonstrated via applications such as lateral stability control, autonomous cruise control and autonomous overtaking on highways. Data collected from our experimental vehicles is used to build predictive models of the vehicle and the environment, and characterize the uncertainty therein. Several approaches for the control design are presented based on a worst-case or probabilistic view of the uncertain forecasts, depending on the application. The proposed control methodologies are validated by experiments performed on prototype passenger vehicles and are executed in real-time on embedded hardware with limited computational power. The experiments show the ability of the proposed framework to handle a variety of driving scenarios including aggressive maneuvers on low-friction surfaces such as snow and navigation in the presence of multiple vehicles.

Control of autonomous vehicle teams has emerged as a key topic in the control and robotics communities, owing to a growing range of applications that can benefit from the increased functionality provided by multiple vehicles. However, the mathematical analysis of the vehicle control problems is complicated by their nonholonomic and kinodynamic constraints, and, due to environmental uncertainties and information flow constraints, the vehicles operate with heightened uncertainty about the team's future motion. In this dissertation, we are motivated by autonomous vehicle control problems that highlight these uncertainties, with in particular attention paid to the uncertainty in the future motion of a secondary agent. Focusing on the Dubins vehicle and unicycle model, we propose a stochastic modeling and optimal feedback control approach that anticipates the uncertainty inherent to the systems. We first consider the application of a Dubins vehicle that should maintain a nominal distance from a target with an unknown future trajectory, such as a tagged animal or vehicle. Stochasticity is introduced in the problem by assuming that the target's motion can be modeled as a Wiener process, and the possibility for the loss of target observations is modeled using stochastic transitions between discrete states. An optimal control policy that is consistent with the stochastic kinematics is computed and is shown to perform well both in the case of a Brownian target and for natural, smooth target motion. We also characterize the resulting optimal feedback control laws in comparison to their deterministic counterparts for the case of a Dubins vehicle in a stochastically varying wind. Turning to the case of multiple vehicles, we develop a method using a Kalman smoothing algorithm for multiple vehicles to enhance an underlying analytic feedback control. The vehicles achieve a formation optimally and in a manner that is robust to uncertainty. To deal with a key implementation issue of these controllers on autonomous vehicle systems, we propose a self-triggering scheme for stochastic control systems, whereby the time points at which the control loop should be closed are computed from predictions of the process in a way that ensures stability.

Kirchhoff’s laws give a mathematical description of electromechanics. Similarly, translational motion mechanics obey Newton’s laws, while rotational motion mechanics comply with Euler’s moment equations, a set of three nonlinear, coupled differential equations. Nonlinearities complicate the mathematical treatment of the seemingly simple action of rotating, and these complications lead to a robust lineage of research culminating here with a text on the ability to make rigid bodies in rotation become self-aware, and even learn. This book is meant for basic scientifically inclined readers commencing with a first chapter on the basics of stochastic artificial intelligence to bridge readers to very advanced topics of deterministic artificial intelligence, espoused in the book with applications to both electromechanics (e.g. the forced van der Pol equation) and also motion mechanics (i.e. Euler’s moment equations). The reader will learn how to bestow self-awareness and express optimal learning methods for the self-aware object (e.g. robot) that require no tuning and no interaction with humans for autonomous operation. The topics learned from reading this text will prepare students and faculty to investigate interesting problems of mechanics. It is the fondest hope of the editor and authors that readers enjoy the book.

This book presents techniques such as the robust control and nonlinearity approximation using linear-parameter-varying (LPV) techniques. Meanwhile, the control of independently driven electric vehicles and autonomous vehicles is introduced. It covers a comprehensive literature review, robust state estimation with uncertain measurements, sideslip angle estimation with finite-frequency optimization, fault detection of vehicle steering systems, output-feedback control of in-wheel motor-driven electric vehicles, robust path following control with network-induced issues, and lateral motion control with the consideration of actuator saturation. This book is a good reference for researchers and engineers working on control of electric vehicles.

This book is the first technical overview of autonomous vehicles written for a general computing and engineering audience. The authors share their practical experiences of creating autonomous vehicle systems. These systems are complex, consisting of three major subsystems: (1) algorithms for localization, perception, and planning and control; (2) client systems, such as the robotics operating system and hardware platform; and (3) the cloud platform, which includes data storage, simulation, high-definition (HD) mapping, and deep learning model training. The algorithm subsystem extracts meaningful information from sensor raw data to understand its environment and make decisions about its actions. The client subsystem integrates these algorithms to meet real-time and reliability requirements. The cloud platform provides offline computing and storage capabilities for autonomous vehicles. Using the cloud platform, we are able to test new algorithms and update the HD map—plus, train better recognition, tracking, and decision models. This book consists of nine chapters. Chapter 1 provides an overview of autonomous vehicle systems; Chapter 2 focuses on localization technologies; Chapter 3 discusses traditional techniques used for perception; Chapter 4 discusses deep learning based techniques for perception; Chapter 5 introduces the planning and control sub-system, especially prediction and routing technologies; Chapter 6 focuses on motion planning and feedback control of the planning and control subsystem; Chapter 7 introduces reinforcement learning-based planning and control; Chapter 8 delves into the details of client systems design; and Chapter 9 provides the details of cloud platforms for autonomous driving. This book should be useful to students, researchers, and practitioners alike. Whether you are an undergraduate or a graduate student interested in autonomous driving, you will find herein a comprehensive overview of the whole autonomous vehicle technology stack. If you are an autonomous driving practitioner, the many practical techniques introduced in this book will be of interest to you. Researchers will also find plenty of references for an effective, deeper exploration of the various technologies.

This book explores a new rapidly developing area of robotics. It describes the state of the art in intelligence control, applied machine intelligence, and research and initial stages of manufacturing autonomous mobile robots. A complete account of the theoretical and experimental results obtained during the last two decades together with some generalizations on Autonomous Mobile Systems are included in this book.