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Author : Alaa Mohamed Leithy Ali
Publisher :
ISBN 13 :
Total Pages : 242 pages
Book Rating : 4.:/5 (12 download)
Download or read book Deep Learning Robust Perception Paradigm for Autonomous Vehicles written by Alaa Mohamed Leithy Ali and published by . This book was released on 2017 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Tim Fingscheidt
Publisher : Springer Nature
ISBN 13 : 303101233X
Total Pages : 435 pages
Book Rating : 4.0/5 (31 download)
Download or read book Deep Neural Networks and Data for Automated Driving written by Tim Fingscheidt and published by Springer Nature. This book was released on 2022-07-19 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book brings together the latest developments from industry and research on automated driving and artificial intelligence. Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing? How to use synthetic data to save labeling costs for training? How do we increase robustness and decrease memory usage? For inevitably poor conditions: How do we know that the network is uncertain about its decisions? Can we understand a bit more about what actually happens inside neural networks? This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety? This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and, last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above.
Author : Rui Fan
Publisher : Springer Nature
ISBN 13 : 981994287X
Total Pages : 391 pages
Book Rating : 4.8/5 (199 download)
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.
Author : David Michael Stavens
Publisher : Stanford University
ISBN 13 :
Total Pages : 104 pages
Book Rating : 4.F/5 ( download)
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.
Author : Sampo Kuutti
Publisher : Morgan & Claypool Publishers
ISBN 13 : 168173608X
Total Pages : 82 pages
Book Rating : 4.6/5 (817 download)
Download or read book Deep Learning for Autonomous Vehicle Control written by Sampo Kuutti and published by Morgan & Claypool Publishers. This book was released on 2019-08-08 with total page 82 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.
Author : Huihui Pan
Publisher : Springer Nature
ISBN 13 : 9819977908
Total Pages : 308 pages
Book Rating : 4.8/5 (199 download)
Download or read book Robust Environmental Perception and Reliability Control for Intelligent Vehicles written by Huihui Pan and published by Springer Nature. This book was released on 2023-11-25 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the most recent state-of-the-art algorithms on robust environmental perception and reliability control for intelligent vehicle systems. By integrating object detection, semantic segmentation, trajectory prediction, multi-object tracking, multi-sensor fusion, and reliability control in a systematic way, this book is aimed at guaranteeing that intelligent vehicles can run safely in complex road traffic scenes. Adopts the multi-sensor data fusion-based neural networks to environmental perception fault tolerance algorithms, solving the problem of perception reliability when some sensors fail by using data redundancy. Presents the camera-based monocular approach to implement the robust perception tasks, which introduces sequential feature association and depth hint augmentation, and introduces seven adaptive methods. Proposes efficient and robust semantic segmentation of traffic scenes through real-time deep dual-resolution networks and representation separation of vision transformers. Focuses on trajectory prediction and proposes phased and progressive trajectory prediction methods that is more consistent with human psychological characteristics, which is able to take both social interactions and personal intentions into account. Puts forward methods based on conditional random field and multi-task segmentation learning to solve the robust multi-object tracking problem for environment perception in autonomous vehicle scenarios. Presents the novel reliability control strategies of intelligent vehicles to optimize the dynamic tracking performance and investigates the completely unknown autonomous vehicle tracking issues with actuator faults.
Author : Alexandros Iosifidis
Publisher : Academic Press
ISBN 13 : 0323885721
Total Pages : 638 pages
Book Rating : 4.3/5 (238 download)
Download or read book Deep Learning for Robot Perception and Cognition written by Alexandros Iosifidis and published by Academic Press. This book was released on 2022-02-04 with total page 638 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks. Presents deep learning principles and methodologies Explains the principles of applying end-to-end learning in robotics applications Presents how to design and train deep learning models Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more Uses robotic simulation environments for training deep learning models Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis
Author : Alexander Andre Amini
Publisher :
ISBN 13 :
Total Pages : 64 pages
Book Rating : 4.:/5 (15 download)
Download or read book Robust End-to-end Learning for Autonomous Vehicles written by Alexander Andre Amini and published by . This book was released on 2018 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning has been successfully applied to "end-to-end" learning of the autonomous driving task, where a deep neural network learns to predict steering control commands from camera data input. While these works support reactionary control, the representation learned is not usable for higher-level decision making required for autonomous navigation. This thesis tackles the problem of learning a representation to predict a continuous control probability distribution, and thus steering control options and bounds for those options, which can be used for autonomous navigation. Each mode in the learned distribution encodes a possible macro-action that the system could execute at that instant, and the covariances of the modes place bounds on safe steering control values. Our approach has the added advantage of being trained solely on unlabeled data collected from inexpensive cameras. In addition to uncertainty estimates computed directly by our model, we add robustness by developing a novel stochastic dropout sampling technique for estimating the inherent confidence of the model's output. We install the relevant processing hardware pipeline on-board a full-scale autonomous vehicle and integrate our learning algorithms for real-time control inference. Finally, we evaluate our models on a challenging dataset containing a wide variety of driving conditions, and show that the algorithms developed as part of this thesis are capable of successfully controlling the vehicle on real roads and even under a parallel autonomy paradigm wherein control is shared between human and robot.
Author : Xinyu Zhang
Publisher : Springer Nature
ISBN 13 : 9819932807
Total Pages : 237 pages
Book Rating : 4.8/5 (199 download)
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:
Author : Tim Fingscheidt
Publisher :
ISBN 13 : 9783031034893
Total Pages : 0 pages
Book Rating : 4.0/5 (348 download)
Download or read book Deep Neural Networks and Data for Automated Driving written by Tim Fingscheidt and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book brings together the latest developments from industry and research on automated driving and artificial intelligence. Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing? How to use synthetic data to save labeling costs for training? How do we increase robustness and decrease memory usage? For inevitably poor conditions: How do we know that the network is uncertain about its decisions? Can we understand a bit more about what actually happens inside neural networks? This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety? This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and, last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above.
Author : Vipin Kumar Kukkala
Publisher : Springer Nature
ISBN 13 : 3031280164
Total Pages : 782 pages
Book Rating : 4.0/5 (312 download)
Download or read book Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems written by Vipin Kumar Kukkala and published by Springer Nature. This book was released on 2023-10-03 with total page 782 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides comprehensive coverage of various solutions that address issues related to real-time performance, security, and robustness in emerging automotive platforms. The authors discuss recent advances towards the goal of enabling reliable, secure, and robust, time-critical automotive cyber-physical systems, using advanced optimization and machine learning techniques. The focus is on presenting state-of-the-art solutions to various challenges including real-time data scheduling, secure communication within and outside the vehicle, tolerance to faults, optimizing the use of resource-constrained automotive ECUs, intrusion detection, and developing robust perception and control techniques for increasingly autonomous vehicles.
Author : Xiaobai Ma
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (126 download)
Download or read book Deep Reinforcement Learning Methods for Autonomous Driving Safety and Interactivity written by Xiaobai Ma and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: To drive a vehicle fully autonomously, an intelligent system needs to be capable of having accurate perception and comprehensive understanding of the surroundings, making reasonable predictions of the progressing of the scenario, and executing safe, comfortable, as well as efficient control actions. Currently, these requirements are mostly fulfilled by the intelligence of human drivers. During past decades, with the development of machine learning and computer science, artificial intelligence starts to show better-than-human performance on more and more practical applications, while autonomous driving is still one of the most attractive and difficult unconquered challenges. This thesis studies the challenges of autonomous driving on its safety and interaction with the surrounding vehicles, and how deep reinforcement learning methods could help address these challenges. Reinforcement learning (RL) is an important paradigm of machine learning which focuses on learning sequential decision-making policies which interact with the task environment. Combining with deep neural networks, the recent development of deep reinforcement learning has shown promising results on control and decision-making tasks with high dimensional observations and complex strategies. The capability and achievements of deep reinforcement learning indicate a wide range of potential applications in autonomous driving. Focusing on autonomous driving safety and interactivity, this thesis presents novel contributions on topics including safe and robust reinforcement learning, reinforcement learning-based safety test, human driver modeling, and multi-agent reinforcement learning. This thesis begins with the study of deep reinforcement learning methods on autonomous driving safety, which is the most critical concern for all autonomous driving systems. We study the autonomous driving safety problem from two points of view: the first is the risk caused by the reinforcement learning control policies due to the mismatch between simulations and the real world; the second is the deep reinforcement learning-based safety test. In both problems, we explore the usage of adversary reinforcement learning agents on finding failures of the system with different focuses: on the first problem, the RL adversary is trained and applied at the learning stage of the control policy to guide it to learn more robust behaviors; on the second problem, the RL adversary is used at the test stage to find the most likely failures in the system. Different learning approaches are proposed and studied for the two problems. Another fundamental challenge for autonomous driving is the interaction between the autonomous vehicle and its surrounding vehicles, which requires accurate modeling of the behavior of surrounding drivers. In the second and third parts of the thesis, we study the surrounding driver modeling problem on three different levels: the action distribution level, the latent state level, and the reasoning level. On the action distribution level, we explore advanced policy representations for modeling the complex distribution of driver's control actions. On the latent state level, we study how to efficiently infer the latent states of surrounding drivers like their driving characteristics and intentions, and how it could be combined with the learning of autonomous driving decision-making policies. On the reasoning level, we investigate the reasoning process between multiple interacting agents and use this to build their behavior models through multi-agent reinforcement learning.
Author : David Michael Stavens
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (745 download)
Download or read book Learning to Drive written by David Michael Stavens and published by . This book was released on 2011 with total page 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.
Author : George Dimitrakopoulos
Publisher : Elsevier
ISBN 13 : 0323901387
Total Pages : 202 pages
Book Rating : 4.3/5 (239 download)
Download or read book Autonomous Vehicles written by George Dimitrakopoulos and published by Elsevier. This book was released on 2021-04-15 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous Vehicles: Technologies, Regulations, and Societal Impacts explores both the autonomous driving concepts and the key hardware and software enablers, Artificial intelligence tools, needed infrastructure, communication protocols, and interaction with non-autonomous vehicles. It analyses the impacts of autonomous driving using a scenario-based approach to quantify the effects on the overall economy and affected sectors. The book assess from a qualitative and quantitative approach, the future of autonomous driving, and the main drivers, challenges, and barriers. The book investigates whether individuals are ready to use advanced automated driving vehicles technology, and to what extent we as a society are prepared to accept highly automated vehicles on the road. Building on the technologies, opportunities, strengths, threats, and weaknesses, Autonomous Vehicles: Technologies, Regulations, and Societal Impacts discusses the needed frameworks for automated vehicles to move inside and around cities. The book concludes with a discussion on what in applications comes next, outlining the future research needs. Broad, interdisciplinary and systematic coverage of the key issues in autonomous driving and vehicles Examines technological impact on society, governance, and the economy as a whole Includes foundational topical coverage, case studies, objectives, and glossary
Author : Huihui Pan (Of Haerbin gong ye da xue)
Publisher :
ISBN 13 : 9789819977925
Total Pages : 0 pages
Book Rating : 4.9/5 (779 download)
Download or read book Robust Environmental Perception and Reliability Control for Intelligent Vehicles written by Huihui Pan (Of Haerbin gong ye da xue) and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book presents the most recent state-of-the-art algorithms on robust environmental perception and reliability control for intelligent vehicle systems. By integrating object detection, semantic segmentation, trajectory prediction, multi-object tracking, multi-sensor fusion, and reliability control in a systematic way, this book is aimed at guaranteeing that intelligent vehicles can run safely in complex road traffic scenes. Adopts the multi-sensor data fusion-based neural networks to environmental perception fault tolerance algorithms, solving the problem of perception reliability when some sensors fail by using data redundancy. Presents the camera-based monocular approach to implement the robust perception tasks, which introduces sequential feature association and depth hint augmentation, and introduces seven adaptive methods. Proposes efficient and robust semantic segmentation of traffic scenes through real-time deep dual-resolution networks and representation separation of vision transformers. Focuses on trajectory prediction and proposes phased and progressive trajectory prediction methods that is more consistent with human psychological characteristics, which is able to take both social interactions and personal intentions into account. Puts forward methods based on conditional random field and multi-task segmentation learning to solve the robust multi-object tracking problem for environment perception in autonomous vehicle scenarios. Presents the novel reliability control strategies of intelligent vehicles to optimize the dynamic tracking performance and investigates the completely unknown autonomous vehicle tracking issues with actuator faults."--
Author : Shaoshan Liu
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1681731673
Total Pages : 285 pages
Book Rating : 4.6/5 (817 download)
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.
Author : Sampo Kuutti
Publisher :
ISBN 13 :
Total Pages : 82 pages
Book Rating : 4.:/5 (114 download)
Download or read book Deep Learning for Autonomous Vehicle Control written by Sampo Kuutti and published by . This book was released on 2019 with total page 82 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.
