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Autonomous Vehicle Lidar Instruction
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Book Synopsis Autonomous Vehicle Lidar Instruction by : Burton Pettinger
Download or read book Autonomous Vehicle Lidar Instruction written by Burton Pettinger and published by Independently Published. This book was released on 2021-05 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: LiDAR, typically used as an acronym for " light detection and ranging", is essentially a sonar that uses pulsed laser waves to map the distance to surrounding objects. It is used by a large number of autonomous vehicles to navigate environments in real-time. 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 a time of flight design concept, following the light pathway through the source and transmitter optics to the photodetector. The 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.
Book Synopsis Autonomous Vehicle Lidar by : Kai Zhou
Download or read book Autonomous Vehicle Lidar written by Kai Zhou and published by . This book was released on 2019-12-31 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Automatic Laser Calibration, Mapping, and Localization for Autonomous Vehicles by : Jesse Sol Levinson
Download or read book Automatic Laser Calibration, Mapping, and Localization for Autonomous Vehicles written by Jesse Sol Levinson and published by Stanford University. This book was released on 2011 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Creating Autonomous Vehicle Systems, Second Edition by : Liu Shaoshan
Download or read book Creating Autonomous Vehicle Systems, Second Edition written by Liu Shaoshan and published by Springer Nature. This book was released on 2022-05-31 with total page 221 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is one of the first technical overviews of autonomous vehicles written for a general computing and engineering audience. The authors share their practical experiences designing 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 as to its future 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, new algorithms can be tested so as to update the HD map—in addition to training better recognition, tracking, and decision models. Since the first edition of this book was released, many universities have adopted it in their autonomous driving classes, and the authors received many helpful comments and feedback from readers. Based on this, the second edition was improved by extending and rewriting multiple chapters and adding two commercial test case studies. In addition, a new section entitled “Teaching and Learning from this Book” was added to help instructors better utilize this book in their classes. The second edition captures the latest advances in autonomous driving and that it also presents usable real-world case studies to help readers better understand how to utilize their lessons in commercial autonomous driving projects. 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 extensive references for an effective, deeper exploration of the various technologies.
Book Synopsis Autonomous Vehicle Lidar Tutorial by : Chantelle Tangredi
Download or read book Autonomous Vehicle Lidar Tutorial written by Chantelle Tangredi and published by . This book was released on 2021-05-03 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: LiDAR, typically used as an acronym for " light detection and ranging", is essentially a sonar that uses pulsed laser waves to map the distance to surrounding objects. It is used by a large number of autonomous vehicles to navigate environments in real-time. 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 a time of flight design concept, following the light pathway through the source and transmitter optics to the photodetector. The 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.
Book Synopsis Autonomous Vehicle Technology by : James M. Anderson
Download or read book Autonomous Vehicle Technology written by James M. Anderson and published by Rand Corporation. This book was released on 2014-01-10 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Autonomous Vehicle Lidar Guide by : Nathaniel Parga
Download or read book Autonomous Vehicle Lidar Guide written by Nathaniel Parga and published by Independently Published. This book was released on 2021-05 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: LiDAR, typically used as an acronym for " light detection and ranging", is essentially a sonar that uses pulsed laser waves to map the distance to surrounding objects. It is used by a large number of autonomous vehicles to navigate environments in real-time. 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 a time of flight design concept, following the light pathway through the source and transmitter optics to the photodetector. The 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.
Book Synopsis Autonomous Vehicle Lidar Tutorial by : Gemma Salk
Download or read book Autonomous Vehicle Lidar Tutorial written by Gemma Salk and published by Independently Published. This book was released on 2021-03-20 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: LiDAR, typically used as an acronym for " light detection and ranging", is essentially a sonar that uses pulsed laser waves to map the distance to surrounding objects. It is used by a large number of autonomous vehicles to navigate environments in real-time. 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 a time of flight design concept, following the light pathway through the source and transmitter optics to the photodetector. The 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.
Book Synopsis Creating Autonomous Vehicle Systems by : Shaoshan Liu
Download or read book Creating Autonomous Vehicle Systems written by Shaoshan Liu and published by Morgan & Claypool Publishers. This book was released on 2017-10-25 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Learning to Drive by : David Michael Stavens
Download or read book Learning to Drive written by David Michael Stavens and published by Stanford University. This book was released on 2011 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: Every year, 1.2 million people die in automobile accidents and up to 50 million are injured. Many of these deaths are due to driver error and other preventable causes. Autonomous or highly aware cars have the potential to positively impact tens of millions of people. Building an autonomous car is not easy. Although the absolute number of traffic fatalities is tragically large, the failure rate of human driving is actually very small. A human driver makes a fatal mistake once in about 88 million miles. As a co-founding member of the Stanford Racing Team, we have built several relevant prototypes of autonomous cars. These include Stanley, the winner of the 2005 DARPA Grand Challenge and Junior, the car that took second place in the 2007 Urban Challenge. These prototypes demonstrate that autonomous vehicles can be successful in challenging environments. Nevertheless, reliable, cost-effective perception under uncertainty is a major challenge to the deployment of robotic cars in practice. This dissertation presents selected perception technologies for autonomous driving in the context of Stanford's autonomous cars. We consider speed selection in response to terrain conditions, smooth road finding, improved visual feature optimization, and cost effective car detection. Our work does not rely on manual engineering or even supervised machine learning. Rather, the car learns on its own, training itself without human teaching or labeling. We show this "self-supervised" learning often meets or exceeds traditional methods. Furthermore, we feel self-supervised learning is the only approach with the potential to provide the very low failure rates necessary to improve on human driving performance.
Book Synopsis Automatic Laser Calibration, Mapping, and Localization for Autonomous Vehicles by : Jesse Sol Levinson
Download or read book Automatic Laser Calibration, Mapping, and Localization for Autonomous Vehicles written by Jesse Sol Levinson and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Multi-LiDAR Placement, Calibration, and Co-registration for Off-road Autonomous Vehicle Operation by : Will Meadows
Download or read book Multi-LiDAR Placement, Calibration, and Co-registration for Off-road Autonomous Vehicle Operation written by Will Meadows and published by . This book was released on 2019 with total page 47 pages. Available in PDF, EPUB and Kindle. Book excerpt: For autonomous vehicles, 3D, rotating LiDAR sensors are critically important towards the vehicle's ability to sense its environment. Generally, these sensors scan their environment, using multiple laser beams to gather information about the range and the intensity of the reflection from an object. For multi--LiDAR systems, the placement of the sensors determines the density of the combined point cloud. I perform preliminary research on the optimal LiDAR placement strategy for an off--road, autonomous vehicle known as the Halo project. I use simulation to generate large amounts of labeled LiDAR data that can be used to train and evaluate a neural network used to process LiDAR data in the vehicle. The performance metrics of the network are then used to generalize the performance of the sensor pose. I also, describe and evaluate intrinsic and extrinsic calibration methods that are applied in the multi--LiDAR system.
Book Synopsis Unsettled Topics Concerning Coating Detection by LiDAR in Autonomous Vehicles by : Cristina P. Magnusson
Download or read book Unsettled Topics Concerning Coating Detection by LiDAR in Autonomous Vehicles written by Cristina P. Magnusson and published by . This book was released on 2021-01-18 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous vehicles (AVs) utilize multiple devices, like high-resolution cameras and radar sensors, to interpret the driving environment and achieve full autonomy. One of these instruments-the light detection and ranging (LiDAR) sensor-functions like radar, but utilizes pulsed infrared (IR) light, typically at wavelengths of 905 nm or 1,550 nm. The LiDAR sensor receives the reflected light from objects and calculates each object's distance and position. In current vehicles, the exterior automotive paint system covers an area larger than any other exterior material. Therefore, understanding how LiDAR wavelengths interact with other vehicles' coatings is extremely important for the safety of future automated driving technologies. Some coatings are more easily detected by LiDAR than others. In general, dark colors can absorb as much as 95% of the incident LiDAR intensity, reducing the amount of signal reflected toward the sensor. White cars are more easily detected as they exhibit high IR reflectivity. Many other factors like gloss level, effect pigments, and refinishes can affect reflectivity and even blind LiDAR sensors. On the other hand, several variables define overall LiDAR and perception system performance, including IR reflectivity of paint but also the target object's geometry, the type of LiDAR technology employed, angle of the target surface, environmental conditions, and sensor fusion software architecture. Sensing Technologies and Materials are two different industries that have not directly interacted in the perception and system sense. With the new applications in the AV industry, approaches need to be taken in a multidisciplinary way to ensure a reliable and safe technology for the future. This report provides a transversal view of the different industry segments from pigment and coating manufacturers to LiDAR component and vehicle system development and integration, and a structured decomposition of the different variables and technologies involved. NOTE: SAE EDGE Research Reports are intended to identify and illuminate key issues in emerging, but still unsettled, technologies of interest to the mobility industry. The goal of SAE EDGE Research Reports is to stimulate discussion and work in the hope of promoting and speeding resolution of identified issues. These reports are not intended to resolve the challenges they identify or close any topic to further scrutiny.
Book Synopsis Autonomous Driving Perception by : Rui Fan
Download or read book Autonomous Driving Perception written by Rui Fan and published by Springer Nature. This book was released on 2023-10-06 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover the captivating world of computer vision and deep learning for autonomous driving with our comprehensive and in-depth guide. Immerse yourself in an in-depth exploration of cutting-edge topics, carefully crafted to engage tertiary students and ignite the curiosity of researchers and professionals in the field. From fundamental principles to practical applications, this comprehensive guide offers a gentle introduction, expert evaluations of state-of-the-art methods, and inspiring research directions. With a broad range of topics covered, it is also an invaluable resource for university programs offering computer vision and deep learning courses. This book provides clear and simplified algorithm descriptions, making it easy for beginners to understand the complex concepts. We also include carefully selected problems and examples to help reinforce your learning. Don't miss out on this essential guide to computer vision and deep learning for autonomous driving.
Book Synopsis LiDAR and Camera Fusion in Autonomous Vehicles by : Jie Zhang
Download or read book LiDAR and Camera Fusion in Autonomous Vehicles written by Jie Zhang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: LiDAR and camera can be an excellent complement to the advantages in an autonomous vehicle system. Various fusion methods have been developed for sensor fusion. Due to information lost, the autonomous driving system cannot navigate complex driving scenarios. When integrating the camera and LiDAR data, to account for loss of some detail of characters when using late fusion, we could choose a convolution neural network to fuse the features. However, the current sensor fusion method has low efficiency for the actual self-driving task due to the complex scenarios. To improve the efficiency and effectiveness of context fusion in high density traffic, we propose a new fusion method and architecture to combine the multi-model information after extracting the features from the LiDAR and camera. This new method is able to pay extra attention to features we want by allocating the weight during the feature extractor level.
Book Synopsis Multi-sensor Fusion for Autonomous Driving by : Xinyu Zhang
Download or read book Multi-sensor Fusion for Autonomous Driving written by Xinyu Zhang and published by Springer Nature. This book was released on with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Deep Learning for Autonomous Vehicle Control by : Sampo Kuutti
Download or read book Deep Learning for Autonomous Vehicle Control written by Sampo Kuutti and published by Springer Nature. This book was released on 2022-06-01 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt: The next generation of autonomous vehicles will provide major improvements in traffic flow, fuel efficiency, and vehicle safety. Several challenges currently prevent the deployment of autonomous vehicles, one aspect of which is robust and adaptable vehicle control. Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalizing previously learned rules to new scenarios. For these reasons, the use of deep neural networks for vehicle control has gained significant interest. In this book, we introduce relevant deep learning techniques, discuss recent algorithms applied to autonomous vehicle control, identify strengths and limitations of available methods, discuss research challenges in the field, and provide insights into the future trends in this rapidly evolving field.
