Point Cloud Processing For Environmental Analysis In Autonomous Driving Using Deep Learning
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Point Cloud Processing For Environmental Analysis In Autonomous Driving Using Deep Learning
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Author : Martin Simon
language : en
Publisher: BoD – Books on Demand
Release Date : 2023-01-01
Point Cloud Processing For Environmental Analysis In Autonomous Driving Using Deep Learning written by Martin Simon and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-01 with Computers categories.
Autonomous self-driving cars need a very precise perception system of their environment, working for every conceivable scenario. Therefore, different kinds of sensor types, such as lidar scanners, are in use. This thesis contributes highly efficient algorithms for 3D object recognition to the scientific community. It provides a Deep Neural Network with specific layers and a novel loss to safely localize and estimate the orientation of objects from point clouds originating from lidar sensors. First, a single-shot 3D object detector is developed that outputs dense predictions in only one forward pass. Next, this detector is refined by fusing complementary semantic features from cameras and joint probabilistic tracking to stabilize predictions and filter outliers. The last part presents an evaluation of data from automotive-grade lidar scanners. A Generative Adversarial Network is also being developed as an alternative for target-specific artificial data generation.
Construction Methods For An Autonomous Driving Map In An Intelligent Network Environment
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Author : Zhijun Chen
language : en
Publisher: Elsevier
Release Date : 2024-04-19
Construction Methods For An Autonomous Driving Map In An Intelligent Network Environment written by Zhijun Chen and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-19 with Technology & Engineering categories.
This book provides an overview of constructing advanced Autonomous Driving Maps. It includes coverage of such methods as: fusion target perception (based on vehicle vision and millimeter wave radar), cross-field of view object perception, vehicle motion recognition (based on vehicle road fusion information), vehicle trajectory prediction (based on improved hybrid neural network) and the driving map construction method driven by road perception fusion. An Autonomous Driving Map is used for optimization of not only for a single vehicle, but also for the entire traffic system.
Neural Computing For Advanced Applications
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Author : Haijun Zhang
language : en
Publisher: Springer Nature
Release Date : 2022-10-20
Neural Computing For Advanced Applications written by Haijun Zhang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-20 with Computers categories.
The two-volume Proceedings set CCIS 1637 and 1638 constitutes the refereed proceedings of the Third International Conference on Neural Computing for Advanced Applications, NCAA 2022, held in Jinan, China, during July 8–10, 2022. The 77 papers included in these proceedings were carefully reviewed and selected from 205 submissions. These papers were categorized into 10 technical tracks, i.e., neural network theory, and cognitive sciences, machine learning, data mining, data security & privacy protection, and data-driven applications, computational intelligence, nature-inspired optimizers, and their engineering applications, cloud/edge/fog computing, the Internet of Things/Vehicles (IoT/IoV), and their system optimization, control systems, network synchronization, system integration, and industrial artificial intelligence, fuzzy logic, neuro-fuzzy systems, decision making, and their applications in management sciences, computer vision, image processing, and their industrial applications, natural language processing, machine translation, knowledge graphs, and their applications, Neural computing-based fault diagnosis, fault forecasting, prognostic management, and system modeling, and Spreading dynamics, forecasting, and other intelligent techniques against coronavirus disease (COVID-19).
Autonomous Driving Algorithms And Its Ic Design
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Author : Jianfeng Ren
language : en
Publisher: Springer Nature
Release Date : 2023-08-09
Autonomous Driving Algorithms And Its Ic Design written by Jianfeng Ren and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-09 with Technology & Engineering categories.
With the rapid development of artificial intelligence and the emergence of various new sensors, autonomous driving has grown in popularity in recent years. The implementation of autonomous driving requires new sources of sensory data, such as cameras, radars, and lidars, and the algorithm processing requires a high degree of parallel computing. In this regard, traditional CPUs have insufficient computing power, while DSPs are good at image processing but lack sufficient performance for deep learning. Although GPUs are good at training, they are too “power-hungry,” which can affect vehicle performance. Therefore, this book looks to the future, arguing that custom ASICs are bound to become mainstream. With the goal of ICs design for autonomous driving, this book discusses the theory and engineering practice of designing future-oriented autonomous driving SoC chips. The content is divided into thirteen chapters, the first chapter mainly introduces readers to the current challenges and research directions in autonomous driving. Chapters 2–6 focus on algorithm design for perception and planning control. Chapters 7–10 address the optimization of deep learning models and the design of deep learning chips, while Chapters 11-12 cover automatic driving software architecture design. Chapter 13 discusses the 5G application on autonomous drving. This book is suitable for all undergraduates, graduate students, and engineering technicians who are interested in autonomous driving.
Deep Learning For Autonomous And Driver Assistant Systems
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Author : Farzan Nowruzi
language : en
Publisher:
Release Date : 2020
Deep Learning For Autonomous And Driver Assistant Systems written by Farzan Nowruzi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
The main goal of autonomous driving is the complete removal of human supervision from the work-flow of autonomous vehicles. This objective represents an opportunity for enhancing quality of life by reducing traffic, removing parking spaces in cities, increasing collective fuel efficiency, and reducing accidents. As autonomous driving is progressively getting integrated into our daily lives, viable solutions are required for its challenges. Artificial intelligence is the main technology that provides intelligent agents with the capability to perceive visual information in a way similar or even superior to human agents. In recent years the deep learning methods showed their outstanding power in dealing with various data processing tasks. Most of the open problems in autonomous driving are focused on the surrounding environment, and some are within the cabin. This dissertation presents solutions to selected problems in both domains using deep learning methods with various sensor modalities. We introduce a model that is able to extract the geometric relationship between two camera images. These results then allow us to proceed with the development of a model to solve geometric transformation in a sequence of point-cloud observations to address the odometry problem. Our proposed method is directly consuming the point-clouds in real-time. Further, we develop the first publicly available comprehensive Radar dataset and propose an open space segmentation model for this task. Lastly, we present a method that uses thermal imaging within the vehicle to count the number of passengers. The thermal images are hiding most of the visual features of passengers and better respect their privacy.
Deep Learning For Autonomous Vehicle Control
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Author : Sampo Kuutti
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2019-08-08
Deep Learning For Autonomous Vehicle Control written by Sampo Kuutti and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-08 with Technology & Engineering categories.
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.
Robot2022 Fifth Iberian Robotics Conference
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Author : Danilo Tardioli
language : en
Publisher: Springer Nature
Release Date : 2022-11-18
Robot2022 Fifth Iberian Robotics Conference written by Danilo Tardioli and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-18 with Technology & Engineering categories.
This book contains a selection of papers accepted for presentation and discussion at ROBOT 2022—Fifth Iberian Robotics Conference, held in Zaragoza, Spain, on November 23-25, 2022. ROBOT 2022 is part of a series of conferences that are a joint organization of SEIDROB—Sociedad Española para la Investigación y Desarrollo en Robótica/Spanish Society for Research and Development in Robotics, and SPR—Sociedade Portuguesa de Robótica/Portuguese Society for Robotic. ROBOT 2022 builds upon several previous successful events, including three biennial workshops and the four previous editions of the Iberian Robotics Conference, and is focused on presenting the research and development of new applications, on the field of Robotics, in the Iberian Peninsula, although open to research and delegates from other countries. ROBOT 2022 featured four plenary talks on state-of-the-art subjects on robotics and 15 special sessions, plus a main/general robotics track. In total, after a careful review process, 98 high-quality papers were selected for publication, with a total of 219 unique authors, from 22 countries.
Learning To Drive
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Author : David Michael Stavens
language : en
Publisher: Stanford University
Release Date : 2011
Learning To Drive written by David Michael Stavens and has been published by Stanford University this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
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.
Point Cloud Compression
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Author : Ge Li
language : en
Publisher: Springer Nature
Release Date :
Point Cloud Compression written by Ge Li and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Autonomous Driving Perception
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Author : Rui Fan
language : en
Publisher: Springer Nature
Release Date : 2023-10-06
Autonomous Driving Perception written by Rui Fan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-06 with Technology & Engineering categories.
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.