The Path to Safe Machine Learning for Automotive Applications PDF Download
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Author: Simon Burton
Publisher: SAE International
ISBN: 1468606581
Category : Technology & Engineering
Languages : en
Pages : 24
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Book Description
Recent rapid advancement in machine learning (ML) technologies have unlocked the potential for realizing advanced vehicle functions that were previously not feasible using traditional approaches to software development. One prominent example is the area of automated driving. However, there is much discussion regarding whether ML-based vehicle functions can be engineered to be acceptably safe, with concerns related to the inherent difficulty and ambiguity of the tasks to which the technology is applied. This leads to challenges in defining adequately safe responses for all possible situations and an acceptable level of residual risk, which is then compounded by the reliance on training data. The Path to Safe Machine Learning for Automotive Applications discusses the challenges involved in the application of ML to safety-critical vehicle functions and provides a set of recommendations within the context of current and upcoming safety standards. In summary, the potential of ML will only be unlocked for safety-related functions if the inevitable uncertainties associated with both the specification and performance of the trained models can be sufficiently well understood and controlled within the application-specific context. Click here to access the full SAE EDGETM Research Report portfolio. https://doi.org/10.4271/EPR2023023
Author: Simon Burton
Publisher: SAE International
ISBN: 1468606581
Category : Technology & Engineering
Languages : en
Pages : 24
Get Book Here
Book Description
Recent rapid advancement in machine learning (ML) technologies have unlocked the potential for realizing advanced vehicle functions that were previously not feasible using traditional approaches to software development. One prominent example is the area of automated driving. However, there is much discussion regarding whether ML-based vehicle functions can be engineered to be acceptably safe, with concerns related to the inherent difficulty and ambiguity of the tasks to which the technology is applied. This leads to challenges in defining adequately safe responses for all possible situations and an acceptable level of residual risk, which is then compounded by the reliance on training data. The Path to Safe Machine Learning for Automotive Applications discusses the challenges involved in the application of ML to safety-critical vehicle functions and provides a set of recommendations within the context of current and upcoming safety standards. In summary, the potential of ML will only be unlocked for safety-related functions if the inevitable uncertainties associated with both the specification and performance of the trained models can be sufficiently well understood and controlled within the application-specific context. Click here to access the full SAE EDGETM Research Report portfolio. https://doi.org/10.4271/EPR2023023
Author: Simon Burton
Publisher: SAE International
ISBN: 1468608371
Category : Technology & Engineering
Languages : en
Pages : 32
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Book Description
ML approaches to solving some of the key perception and decision challenges in automated vehicle functions are maturing at an incredible rate. However, the setbacks experienced during initial attempts at widespread deployment have highlighted the need for a careful consideration of safety during the development and deployment of these functions. To better control the risk associated with this storm of complex functionality, open operating environments, and cutting-edge technology, there is a need for industry consensus on best practices for achieving an acceptable level of safety. Navigating the Evolving Landscape of Safety Standards for Machine Learning-based Road Vehicle Functions provides an overview of standards relevant to the safety of ML-based vehicle functions and serves as guidance for technology providers—including those new to the automotive sector—on how to interpret the evolving standardization landscape. The report also contains practical guidance, along with an example from the perspective of a developer of an ML-based perception function on how to interpret the requirements of these standards. Click here to access the full SAE EDGETM Research Report portfolio. https://doi.org/10.4271/EPR2024017
Author: Danil Prokhorov
Publisher: Springer Science & Business Media
ISBN: 3540792562
Category : Computers
Languages : en
Pages : 374
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Book Description
This edited volume is the first of its kind and provides a representative sample of contemporary computational intelligence (CI) activities in the area of automotive technology. All chapters contain overviews of the state-of-the-art.
Author: Xin-She Yang
Publisher: Springer Nature
ISBN: 9811558566
Category : Technology & Engineering
Languages : en
Pages : 666
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Book Description
This book gathers selected high-quality research papers presented at the Fifth International Congress on Information and Communication Technology, held at Brunel University, London, on February 20–21, 2020. It discusses emerging topics pertaining to information and communication technology (ICT) for managerial applications, e-governance, e-agriculture, e-education and computing technologies, the Internet of Things (IoT) and e-mining. Written by respected experts and researchers working on ICT, the book offers a valuable asset for young researchers involved in advanced studies.
Author: Adam Bohr
Publisher: Academic Press
ISBN: 0128184396
Category : Computers
Languages : en
Pages : 385
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Book Description
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data
Author: Giampiero Mastinu
Publisher: Springer Nature
ISBN: 3031703928
Category :
Languages : en
Pages : 1016
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Book Description
Author: Jörg Dubbert
Publisher: Springer
ISBN: 3319997629
Category : Technology & Engineering
Languages : en
Pages : 201
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Book Description
This volume of the Lecture Notes in Mobility series contains papers written by speakers at the 22nd International Forum on Advanced Microsystems for Automotive Applications (AMAA 2018) "Smart Systems for Clean, Safe and Shared Road Vehicles" that was held in Berlin, Germany in September 2018. The authors report about recent breakthroughs in electric and electronic components and systems, driver assistance, vehicle automation and electrification as well as data, clouds and machine learning. Furthermore, innovation aspects and impacts of connected and automated driving are covered. The target audience primarily comprises research experts and practitioners in industry and academia, but the book may also be beneficial for graduate students alike.
Author: Markus Maurer
Publisher: Springer
ISBN: 3662488477
Category : Technology & Engineering
Languages : en
Pages : 698
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Book Description
This book takes a look at fully automated, autonomous vehicles and discusses many open questions: How can autonomous vehicles be integrated into the current transportation system with diverse users and human drivers? Where do automated vehicles fall under current legal frameworks? What risks are associated with automation and how will society respond to these risks? How will the marketplace react to automated vehicles and what changes may be necessary for companies? Experts from Germany and the United States define key societal, engineering, and mobility issues related to the automation of vehicles. They discuss the decisions programmers of automated vehicles must make to enable vehicles to perceive their environment, interact with other road users, and choose actions that may have ethical consequences. The authors further identify expectations and concerns that will form the basis for individual and societal acceptance of autonomous driving. While the safety benefits of such vehicles are tremendous, the authors demonstrate that these benefits will only be achieved if vehicles have an appropriate safety concept at the heart of their design. Realizing the potential of automated vehicles to reorganize traffic and transform mobility of people and goods requires similar care in the design of vehicles and networks. By covering all of these topics, the book aims to provide a current, comprehensive, and scientifically sound treatment of the emerging field of “autonomous driving".
Author: Vipin Kumar Kukkala
Publisher: Springer Nature
ISBN: 3031280164
Category : Technology & Engineering
Languages : en
Pages : 782
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Book Description
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: Sumit Ranjan
Publisher: Packt Publishing Ltd
ISBN: 1838647023
Category : Computers
Languages : en
Pages : 320
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Book Description
Explore self-driving car technology using deep learning and artificial intelligence techniques and libraries such as TensorFlow, Keras, and OpenCV Key FeaturesBuild and train powerful neural network models to build an autonomous carImplement computer vision, deep learning, and AI techniques to create automotive algorithmsOvercome the challenges faced while automating different aspects of driving using modern Python libraries and architecturesBook Description Thanks to a number of recent breakthroughs, self-driving car technology is now an emerging subject in the field of artificial intelligence and has shifted data scientists' focus to building autonomous cars that will transform the automotive industry. This book is a comprehensive guide to use deep learning and computer vision techniques to develop autonomous cars. Starting with the basics of self-driving cars (SDCs), this book will take you through the deep neural network techniques required to get up and running with building your autonomous vehicle. Once you are comfortable with the basics, you'll delve into advanced computer vision techniques and learn how to use deep learning methods to perform a variety of computer vision tasks such as finding lane lines, improving image classification, and so on. You will explore the basic structure and working of a semantic segmentation model and get to grips with detecting cars using semantic segmentation. The book also covers advanced applications such as behavior-cloning and vehicle detection using OpenCV, transfer learning, and deep learning methodologies to train SDCs to mimic human driving. By the end of this book, you'll have learned how to implement a variety of neural networks to develop your own autonomous vehicle using modern Python libraries. What you will learnImplement deep neural network from scratch using the Keras libraryUnderstand the importance of deep learning in self-driving carsGet to grips with feature extraction techniques in image processing using the OpenCV libraryDesign a software pipeline that detects lane lines in videosImplement a convolutional neural network (CNN) image classifier for traffic signal signsTrain and test neural networks for behavioral-cloning by driving a car in a virtual simulatorDiscover various state-of-the-art semantic segmentation and object detection architecturesWho this book is for If you are a deep learning engineer, AI researcher, or anyone looking to implement deep learning and computer vision techniques to build self-driving blueprint solutions, this book is for you. Anyone who wants to learn how various automotive-related algorithms are built, will also find this book useful. Python programming experience, along with a basic understanding of deep learning, is necessary to get the most of this book.
