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This book introduces and provides a detailed understanding of on- and off-road vehicle dynamics. It discusses classical on-road tyre mechanics, including finite element tyre modelling and validation, using a combination of theoretical and experimental data sets. Chapters explore new computational techniques that describe terrain models and combined to develop better off-road vehicle models, and focus is placed on terrain characterization and modelling, using two popular modelling techniques, as well as performance characteristics of off-road vehicles - including rolling and driven combinations, traction, and steering. The effect of multi-pass and soil compaction on tyre performance is described as well. The book presents a unique neuro-tyre model for both on-road and off-road situations, capable of computing the steering, braking characteristics, and soil compaction. Road vehicle characteristics are described, including the stability and control, roll centre and roll axis, and rollover mechanics. The road vehicle braking performance is also described, including the brake components, choice of brake, and the transient load transfer. Finally, the dynamics and control of multi-wheel combat vehicles are presented and described extensively. The book is dedicated to undergraduate and graduate engineering students, in addition to researchers, and the automotive industry. As well as provide the readers with a better understanding of vehicle dynamics and soil mechanics. The book is also beneficial for automotive industries looking for a quick and reliable model to be implemented in their main software.
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.