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Autonomous Vehicle Lidar Tutorial
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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 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 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 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 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 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 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 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 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 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 Automotive LiDAR A Complete Guide - 2019 Edition by : Gerardus Blokdyk
Download or read book Automotive LiDAR A Complete Guide - 2019 Edition written by Gerardus Blokdyk and published by 5starcooks. This book was released on 2019-05-30 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Which primary stakeholders are driving development, and where are the decisions influencing this development being made? Full Autonomous Vehicle: When and How? Where can you find an end-to-end solution for LiDAR data? Would a self-driving car put pedestrians at risk to avoid a collision? Has the existing hierarchy of the overall vehicle concept been taken into consideration for information, warnings and feedback in terms of the type of feedback? Defining, designing, creating, and implementing a process to solve a challenge or meet an objective is the most valuable role... In EVERY group, company, organization and department. Unless you are talking a one-time, single-use project, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, 'What are we really trying to accomplish here? And is there a different way to look at it?' This Self-Assessment empowers people to do just that - whether their title is entrepreneur, manager, consultant, (Vice-)President, CxO etc... - they are the people who rule the future. They are the person who asks the right questions to make Automotive LiDAR investments work better. This Automotive LiDAR All-Inclusive Self-Assessment enables You to be that person. All the tools you need to an in-depth Automotive LiDAR Self-Assessment. Featuring 900 new and updated case-based questions, organized into seven core areas of process design, this Self-Assessment will help you identify areas in which Automotive LiDAR improvements can be made. In using the questions you will be better able to: - diagnose Automotive LiDAR projects, initiatives, organizations, businesses and processes using accepted diagnostic standards and practices - implement evidence-based best practice strategies aligned with overall goals - integrate recent advances in Automotive LiDAR and process design strategies into practice according to best practice guidelines Using a Self-Assessment tool known as the Automotive LiDAR Scorecard, you will develop a clear picture of which Automotive LiDAR areas need attention. Your purchase includes access details to the Automotive LiDAR self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next. You will receive the following contents with New and Updated specific criteria: - The latest quick edition of the book in PDF - The latest complete edition of the book in PDF, which criteria correspond to the criteria in... - The Self-Assessment Excel Dashboard - Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation - In-depth and specific Automotive LiDAR Checklists - Project management checklists and templates to assist with implementation INCLUDES LIFETIME SELF ASSESSMENT UPDATES Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips.
Author :Paul F. McManamon Publisher :SPIE-International Society for Optical Engineering ISBN 13 :9781510625396 Total Pages : pages Book Rating :4.6/5 (253 download)
Book Synopsis LiDAR Technologies and Systems by : Paul F. McManamon
Download or read book LiDAR Technologies and Systems written by Paul F. McManamon and published by SPIE-International Society for Optical Engineering. This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "LiDAR technology and Systems is a tutorial book, covering LiDAR Technology. The introduction sets lidar in context, as one of many sensor technologies utilizing electro-magnetic radiation. LiDAR is in the optical and infrared wavelengths, and it is an active sensor, which collects reflected EM radiation. It is similar to more familiar passive EO/IR sensors in wavelength, and similar to radar in that it uses reflected radiation emitted by the sensor. The second chapter goes the > 50 years of lidar history. Chapter 3 covers the link budget - how much signal a LiDAR must emit in order to get a certain number of reflected photons back. Chapter 4 discusses the rich phenomenology of LiDAR. One of the strengths of LiDAR is its' diverse phenomenology's. As a result, there are many flavors of LiDAR. The most common is 3D imaging, but there are many other types of lidars, with different measurement objectives. The next 4 chapters discuss components of a LiDAR. Chapter 5 discusses laser sources, chapter 6 LiDAR receivers, chapter 7 beam steering approaches, and chapter 8 LiDAR processing. The last 3 chapters are testing, metrics, and applications. Chapter 11, the applications chapter, picks 4 popular applications and discusses these LiDARs, and how to build them, for these applications. Chapter 11 as a result will repeats some information in earlier chapters, but in the context of a particular application"--
Book Synopsis Explainable Artificial Intelligence for Autonomous Vehicles by : Kamal Malik
Download or read book Explainable Artificial Intelligence for Autonomous Vehicles written by Kamal Malik and published by CRC Press. This book was released on 2024-08-14 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explainable AI for Autonomous Vehicles: Concepts, Challenges, and Applications is a comprehensive guide to developing and applying explainable artificial intelligence (XAI) in the context of autonomous vehicles. It begins with an introduction to XAI and its importance in developing autonomous vehicles. It also provides an overview of the challenges and limitations of traditional black-box AI models and how XAI can help address these challenges by providing transparency and interpretability in the decision-making process of autonomous vehicles. The book then covers the state-of-the-art techniques and methods for XAI in autonomous vehicles, including model-agnostic approaches, post-hoc explanations, and local and global interpretability techniques. It also discusses the challenges and applications of XAI in autonomous vehicles, such as enhancing safety and reliability, improving user trust and acceptance, and enhancing overall system performance. Ethical and social considerations are also addressed in the book, such as the impact of XAI on user privacy and autonomy and the potential for bias and discrimination in XAI-based systems. Furthermore, the book provides insights into future directions and emerging trends in XAI for autonomous vehicles, such as integrating XAI with other advanced technologies like machine learning and blockchain and the potential for XAI to enable new applications and services in the autonomous vehicle industry. Overall, the book aims to provide a comprehensive understanding of XAI and its applications in autonomous vehicles to help readers develop effective XAI solutions that can enhance autonomous vehicle systems' safety, reliability, and performance while improving user trust and acceptance. This book: Discusses authentication mechanisms for camera access, encryption protocols for data protection, and access control measures for camera systems. Showcases challenges such as integration with existing systems, privacy, and security concerns while implementing explainable artificial intelligence in autonomous vehicles. Covers explainable artificial intelligence for resource management, optimization, adaptive control, and decision-making. Explains important topics such as vehicle-to-vehicle (V2V) communication, vehicle-to-infrastructure (V2I) communication, remote monitoring, and control. Emphasizes enhancing safety, reliability, overall system performance, and improving user trust in autonomous vehicles. The book is intended to provide researchers, engineers, and practitioners with a comprehensive understanding of XAI's key concepts, challenges, and applications in the context of autonomous vehicles. It is primarily written for senior undergraduate, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer science and engineering, information technology, and automotive engineering.
Book Synopsis Automotive LiDAR A Complete Guide - 2020 Edition by : Gerardus Blokdyk
Download or read book Automotive LiDAR A Complete Guide - 2020 Edition written by Gerardus Blokdyk and published by 5starcooks. This book was released on 2019-09-06 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: What are the security/safety/resilience goals of self-driving vehicles? Full Autonomous Vehicle: When and How? Emergency situations: in the event of an emergency, what will be required of the human occupant, the car itself, and the technology it utilizes? What are the incremental safety impact potential? As part of the quality assurance, can the LiDAR continuously, between tracks, provide automatic gain control and verify detection as the threshold is lowered? Defining, designing, creating, and implementing a process to solve a challenge or meet an objective is the most valuable role... In EVERY group, company, organization and department. Unless you are talking a one-time, single-use project, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, 'What are we really trying to accomplish here? And is there a different way to look at it?' This Self-Assessment empowers people to do just that - whether their title is entrepreneur, manager, consultant, (Vice-)President, CxO etc... - they are the people who rule the future. They are the person who asks the right questions to make Automotive LiDAR investments work better. This Automotive LiDAR All-Inclusive Self-Assessment enables You to be that person. All the tools you need to an in-depth Automotive LiDAR Self-Assessment. Featuring 900 new and updated case-based questions, organized into seven core areas of process design, this Self-Assessment will help you identify areas in which Automotive LiDAR improvements can be made. In using the questions you will be better able to: - diagnose Automotive LiDAR projects, initiatives, organizations, businesses and processes using accepted diagnostic standards and practices - implement evidence-based best practice strategies aligned with overall goals - integrate recent advances in Automotive LiDAR and process design strategies into practice according to best practice guidelines Using a Self-Assessment tool known as the Automotive LiDAR Scorecard, you will develop a clear picture of which Automotive LiDAR areas need attention. Your purchase includes access details to the Automotive LiDAR self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next. You will receive the following contents with New and Updated specific criteria: - The latest quick edition of the book in PDF - The latest complete edition of the book in PDF, which criteria correspond to the criteria in... - The Self-Assessment Excel Dashboard - Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation - In-depth and specific Automotive LiDAR Checklists - Project management checklists and templates to assist with implementation INCLUDES LIFETIME SELF ASSESSMENT UPDATES Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips.
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-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 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.
