Point Cloud Processing for Environmental Analysis in Autonomous Driving using Deep Learning PDF Download
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Author: Martin Simon
Publisher: BoD – Books on Demand
ISBN: 3863602722
Category : Computers
Languages : en
Pages : 194
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Book Description
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.
Author: Martin Simon
Publisher: BoD – Books on Demand
ISBN: 3863602722
Category : Computers
Languages : en
Pages : 194
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Book Description
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.
Author: Haijun Zhang
Publisher: Springer Nature
ISBN: 9811961352
Category : Computers
Languages : en
Pages : 532
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Book Description
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).
Author: Farzan Nowruzi
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
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.
Author: Zhijun Chen
Publisher: Elsevier
ISBN: 0443273170
Category : Technology & Engineering
Languages : en
Pages : 197
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Book Description
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.
Author: Jianfeng Ren
Publisher: Springer Nature
ISBN: 9819928974
Category : Technology & Engineering
Languages : en
Pages : 306
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Book Description
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.
Author: Sampo Kuutti
Publisher: Morgan & Claypool Publishers
ISBN: 168173608X
Category : Technology & Engineering
Languages : en
Pages : 82
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Book Description
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: Danilo Tardioli
Publisher: Springer Nature
ISBN: 3031210654
Category : Technology & Engineering
Languages : en
Pages : 616
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Book Description
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.
Author: Rui Fan
Publisher: Springer Nature
ISBN: 981994287X
Category : Technology & Engineering
Languages : en
Pages : 391
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Book Description
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: Ge Li
Publisher: Springer Nature
ISBN: 9819719577
Category :
Languages : en
Pages : 264
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Book Description
Author: Adam Glowacz
Publisher: MDPI
ISBN: 3039282948
Category : Technology & Engineering
Languages : en
Pages : 604
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Book Description
This Special Issue with 35 published articles shows the significance of the topic “Signal Processing and Analysis of Electrical Circuit”. This topic has been gaining increasing attention in recent times. The presented articles can be categorized into four different areas: signal processing and analysis methods of electrical circuits; electrical measurement technology; applications of signal processing of electrical equipment; fault diagnosis of electrical circuits. It is a fact that the development of electrical systems, signal processing methods, and circuits has been accelerating. Electronics applications related to electrical circuits and signal processing methods have gained noticeable attention in recent times. The methods of signal processing and electrical circuits are widely used by engineers and scientists all over the world. The constituent papers represent a significant contribution to electronics and present applications that can be used in industry. Further improvements to the presented approaches are required for realizing their full potential.
