Self-recovery System for Lidar-based Autonomous Driving PDF Download
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Author: 王若芸
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: 王若芸
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Jesse Sol Levinson
Publisher: Stanford University
ISBN:
Category :
Languages : en
Pages : 153
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Book Description
This dissertation presents several related algorithms that enable important capabilities for self-driving vehicles. Using a rotating multi-beam laser rangefinder to sense the world, our vehicle scans millions of 3D points every second. Calibrating these sensors plays a crucial role in accurate perception, but manual calibration is unreasonably tedious, and generally inaccurate. As an alternative, we present an unsupervised algorithm for automatically calibrating both the intrinsics and extrinsics of the laser unit from only seconds of driving in an arbitrary and unknown environment. We show that the results are not only vastly easier to obtain than traditional calibration techniques, they are also more accurate. A second key challenge in autonomous navigation is reliable localization in the face of uncertainty. Using our calibrated sensors, we obtain high resolution infrared reflectivity readings of the world. From these, we build large-scale self-consistent probabilistic laser maps of urban scenes, and show that we can reliably localize a vehicle against these maps to within centimeters, even in dynamic environments, by fusing noisy GPS and IMU readings with the laser in realtime. We also present a localization algorithm that was used in the DARPA Urban Challenge, which operated without a prerecorded laser map, and allowed our vehicle to complete the entire six-hour course without a single localization failure. Finally, we present a collection of algorithms for the mapping and detection of traffic lights in realtime. These methods use a combination of computer-vision techniques and probabilistic approaches to incorporating uncertainty in order to allow our vehicle to reliably ascertain the state of traffic-light-controlled intersections.
Author: Shaoshan Liu
Publisher: Morgan & Claypool Publishers
ISBN: 1681731673
Category : Computers
Languages : en
Pages : 285
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Book Description
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.
Author: 汪聖倫
Publisher:
ISBN:
Category :
Languages : en
Pages : 71
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Author: Mohd Anas Ali
Publisher: Mha Publisher
ISBN:
Category :
Languages : en
Pages : 0
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The Self-driving car is an autonomous robot that navigates to its destination without human operator. The aim of this project is to make an efficient lidar sensing system for Self-driving cars that is capable of mapping its surroundings, navigating through the path, and reaches the destination automatically. This is done by implementing Simultaneous Localization and Mapping (SLAM) algorithm on a collected series of lidar scans using pose graph optimization to build the map of the environment. The robot recognizes a previously visited place through scan matching and establishes one or more loop closures along its moving path. The SLAM algorithm utilizes the loop closure information to update the map and adjust the estimated robot trajectory. To plan a path through an environment effectively a probabilistic roadmap (PRM) algorithm uses a network of connected nodes to find an obstacle-free path from a start to an end location. Nodes are connected based on the obstacle locations specified in the Map. The results are demonstrated on a low-cost Autonomous RC Robot that uses ROS (Robotic Operating System) software library, version of ROS Kinetic booted on Raspberry Pi interfaced with YDLidar X2 in the front top portion of it. This low-cost autonomous bot emerges with features like SLAM (Simultaneous Localization and Mapping), Path planning, and Path Following which can reach the destination automatically after its destination is fixed on the Map.
Author: Kai Zhou
Publisher:
ISBN: 9781653277919
Category :
Languages : en
Pages : 112
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Book Description
The largest high-tech companies and leading automobile manufacturers in the world have unleashed torrents of effort and capital to position themselves for the arrival of autonomous vehicles. What is the fuss about? What is at stake? What are the leading sensor technologies? What is meant by "flash lidar" or "time-of-flight" sensors? With no less than 40 - 50 lidar companies vying to create mainstream automotive sensors, the climate is unique for young scientists and engineers to enter the field. What are the alliances forming between the companies, and how are they shifting? Who are current incumbents in the field? This tutorial text aims to introduce a technical but nonspecialist reader to autonomous vehicle lidar, starting from the fundamental physics of lidar and motivation for its application to autonomous vehicle systems. We will then introduce time of flight design concepts, following the light pathway through the source and transmitter optics to the photodetector. Next two distinct timing methods will be introduced, followed up by a brief discussion of beam steering. After finishing this text, the reader should be prepared to enter into laboratory explorations on the topic.
Author: James M. Anderson
Publisher: Rand Corporation
ISBN: 0833084372
Category : Transportation
Languages : en
Pages : 215
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Book Description
The automotive industry appears close to substantial change engendered by “self-driving” technologies. This technology offers the possibility of significant benefits to social welfare—saving lives; reducing crashes, congestion, fuel consumption, and pollution; increasing mobility for the disabled; and ultimately improving land use. This report is intended as a guide for state and federal policymakers on the many issues that this technology raises.
Author: Maximilian Paulsen Apodaca
Publisher:
ISBN:
Category :
Languages : en
Pages : 30
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Book Description
Failures are unavoidable in engineered systems such as autonomous vehicles, but the latency of recovering a failed component degrades the performance of autonomous vehicles. We proposed a scheme to reduce the time of recovering autonomous vehicles from failures. Using Linux kernel features and container technology, we containerize functional components of autonomous vehicles and periodically take checkpoints. After detecting a failure, we recover the failed component to a previous state, and we notify the rest of the system to coordinate with the recovery. We test our method using the Robot Operating System (ROS), a widely-used middleware for robots and autonomous driving vehicles. Our initial experimental results show that we reduced the recovery time of a practical pointcloud processing component in autonomous driving by 94%.
Author: David Fernández-Llorca
Publisher: MDPI
ISBN: 3039434020
Category : Technology & Engineering
Languages : en
Pages : 752
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Book Description
This book presents the results of the successful Sensors Special Issue on Intelligent Vehicles that received submissions between March 2019 and May 2020. The Guest Editors of this Special Issue are Dr. David Fernández-Llorca, Dr. Ignacio Parra-Alonso, Dr. Iván García-Daza and Dr. Noelia Parra-Alonso, all from the Computer Engineering Department at the University of Alcalá (Madrid, Spain). A total of 32 manuscripts were finally accepted between 2019 and 2020, presented by top researchers from all over the world. The reader will find a well-representative set of current research and developments related to sensors and sensing for intelligent vehicles. The topics of the published manuscripts can be grouped into seven main categories: (1) assistance systems and automatic vehicle operation, (2) vehicle positioning and localization, (3) fault diagnosis and fail-x systems, (4) perception and scene understanding, (5) smart regenerative braking systems for electric vehicles, (6) driver behavior modeling and (7) intelligent sensing. We, the Guest Editors, hope that the readers will find this book to contain interesting papers for their research, papers that they will enjoy reading as much as we have enjoyed organizing this Special Issue
Author: Qingguo Zhou
Publisher: Elsevier
ISBN: 0323994490
Category : Technology & Engineering
Languages : en
Pages : 346
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Book Description
Self-driving vehicles are a rapidly growing area of research and expertise. Theories and Practice of Self-Driving Vehicles presents a comprehensive introduction to the technology of self driving vehicles across the three domains of perception, planning and control. The title systematically introduces vehicle systems from principles to practice, including basic knowledge of ROS programming, machine and deep learning, as well as basic modules such as environmental perception and sensor fusion. The book introduces advanced control algorithms as well as important areas of new research. This title offers engineers, technicians and students an accessible handbook to the entire stack of technology in a self-driving vehicle. Theories and Practice of Self-Driving Vehicles presents an introduction to self-driving vehicle technology from principles to practice. Ten chapters cover the full stack of driverless technology for a self-driving vehicle. Written by two authors experienced in both industry and research, this book offers an accessible and systematic introduction to self-driving vehicle technology. Provides a comprehensive introduction to the technology stack of a self-driving vehicle Covers the three domains of perception, planning and control Offers foundational theory and best practices Introduces advanced control algorithms and high-potential areas of new research Gives engineers, technicians and students an accessible handbook to self-driving vehicle technology and applications
