The major problem addressed by this research is how to plan a safe motion for autonomous vehicles in a two dimensional, rectilinear world. With given start and goal configurations, the planner performs motion planning which will lead a vehicle to achieve its task safely. During the planning, in addition to the safety consideration, motion's smoothness is also taken into account. The approach taken was to divide whole motion planning task into two layers. The top layer finds a global path by decomposing the free space into convex regions, then searching for an optimal global path class. The bottom layer performs local motion planning which further subdivides the planning problem into mid-portion and end-portion motion planning. The local motion planning is carried out region by region along the global path class. As results, simple motion instructions are generated for each region. For execution of planned motion, a software system, Model-based Mobile robot Language (MML- 11), was developed. This easy- to-use robot language provides users a convenient tool to program their applications through its function library. The results of the research were implemented in MML-1l and tested on an experimental robot Yamabico-11 successfully. (AN).

The major problem addressed by this research is how to plan a safe motion for autonomous vehicles in a two dimensional, rectilinear world. With given start and goal configurations, the planner performs motion planning which will lead a vehicle to achieve its task safely. During the planning, in addition to the safety consideration, motion's smoothness is also taken into account. The approach taken was to divide whole motion planning task into two layers. The top layer finds a global path by decomposing the free space into convex regions, then searching for an optimal global path class. The bottom layer performs local motion planning which further subdivides the planning problem into mid-portion and end-portion motion planning. The local motion planning is carried out region by region along the global path class. As results, simple motion instructions are generated for each region. For execution of planned motion, a software system, Model-based Mobile robot Language (MML- 11), was developed. This easy- to-use robot language provides users a convenient tool to program their applications through its function library. The results of the research were implemented in MML-1l and tested on an experimental robot Yamabico-11 successfully. (AN).

Path Planning (PP) is one of the prerequisites in ensuring safe navigation and manoeuvrability control for driverless vehicles. Due to the dynamic nature of the real world, PP needs to address changing environments and how autonomous vehicles respond to them. This book explores PP in the context of road vehicles, robots, off-road scenarios, multi-robot motion, and unmanned aerial vehicles (UAVs ).

This work develops a motion planner that compensates the deficiencies from perception modules by exploiting the reaction capabilities of a vehicle. The work analyzes present uncertainties and defines driving objectives together with constraints that ensure safety. The resulting problem is solved in real-time, in two distinct ways: first, with nonlinear optimization, and secondly, by framing it as a partially observable Markov decision process and approximating the solution with sampling.

In motion planning for automated vehicles, a thorough uncertainty consideration is crucial to facilitate safe and convenient driving behavior. This work presents three motion planning approaches which are targeted towards the predominant uncertainties in different scenarios, along with an extended safety verification framework. The approaches consider uncertainties from imperfect perception, occlusions and limited sensor range, and also those in the behavior of other traffic participants.

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.