Explainable Artificial Intelligence For Autonomous Vehicles
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Explainable Artificial Intelligence For Autonomous Vehicles
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Author : Kamal Malik
language : en
Publisher: CRC Press
Release Date : 2024-08-14
Explainable Artificial Intelligence For Autonomous Vehicles written by Kamal Malik and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-14 with Computers categories.
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.
Explainable Artificial Intelligence For Autonomous Vehicles
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Author : Kamal Malik
language : en
Publisher: CRC Press
Release Date : 2024-08-14
Explainable Artificial Intelligence For Autonomous Vehicles written by Kamal Malik and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-14 with Computers categories.
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.
Explainable Artificial Intelligence For Intelligent Transportation Systems
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Author : Loveleen Gaur
language : en
Publisher: Springer Nature
Release Date : 2022-08-08
Explainable Artificial Intelligence For Intelligent Transportation Systems written by Loveleen Gaur and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-08 with Computers categories.
Transportation typically entails crucial “life-death” choices, delegating crucial decisions to an AI algorithm without any explanation poses a serious threat. Hence, explainability and responsible AI is crucial in the context of intelligent transportation. In Intelligence Transportation System (ITS) implementations such as traffic management systems and autonomous driving applications, AI-based control mechanisms are gaining prominence. Explainable artificial intelligence for intelligent transportation system tackling certain challenges in the field of autonomous vehicle, traffic management system, data integration and analytics and monitor the surrounding environment. The book discusses and inform researchers on explainable Intelligent Transportation system. It also discusses prospective methods and techniques for enabling the interpretability of transportation systems. The book further focuses on ethical considerations apart from technical considerations.
Explainable Artificial Intelligence For Intelligent Transportation Systems
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Author : Amina Adadi
language : en
Publisher: CRC Press
Release Date : 2023-10-20
Explainable Artificial Intelligence For Intelligent Transportation Systems written by Amina Adadi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-20 with Technology & Engineering categories.
Artificial Intelligence (AI) and Machine Learning (ML) are set to revolutionize all industries, and the Intelligent Transportation Systems (ITS) field is no exception. While ML, especially deep learning models, achieve great performance in terms of accuracy, the outcomes provided are not amenable to human scrutiny and can hardly be explained. This can be very problematic, especially for systems of a safety-critical nature such as transportation systems. Explainable AI (XAI) methods have been proposed to tackle this issue by producing human interpretable representations of machine learning models while maintaining performance. These methods hold the potential to increase public acceptance and trust in AI-based ITS. FEATURES: Provides the necessary background for newcomers to the field (both academics and interested practitioners) Presents a timely snapshot of explainable and interpretable models in ITS applications Discusses ethical, societal, and legal implications of adopting XAI in the context of ITS Identifies future research directions and open problems
Toward Explainable Robust And Fair Ai In Automated And Autonomous Vehicles
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Author :
language : en
Publisher:
Release Date : 2023
Toward Explainable Robust And Fair Ai In Automated And Autonomous Vehicles written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
In March 2022 the JRC (Units B.6, C.4, E.3) organized an Exploratory Workshop entitled "Toward explainable, robust, and fair AI in automated and autonomous vehicles", bringing together experts in fields such as Trustworthy AI, autonomous driving, and vehicle testing. This report summarizes the steps that followed the organization of the workshop, including the definition of the scientific objectives, the list of invited presenters and participants, and the conditions under which the workshop took place. The report also presents the main findings of each talk that occurred during the workshop and an analysis of the discussions that occurred during collaborative working sessions. Topics of interest included, among others, current regulations and standards regarding automated and autonomous road vehicles and analysis of their limitations; explainability of artificial intelligence ; accuracy, robustness, security, and fairness of AI systems. These insights are used to provide concluding remarks on the outlook of the Workshop, in particular how the findings of the Workshop can help to promote further research within and outside of the JRC on this topic, with the goal of making safer transport through innovative ecosystems and effective regulations. We identified gaps in the scientific literature on the relationship between AI and safety of Automated and Autonomous Vehicles (A&AVs) such as: establishment of reasoning vocabulary for acceptable factual and/or counterfactual interpretations, certification readiness matrix must be developed for each cyber scenario for different adversarial attacks and for naturally occurring perturbations, behavioural models are missing for motion prediction of different social agents and tests with standardized dummies lack the features of different social groups, currently there are not enough data to assess the fairness of A&AV vehicles and how fairness or bias influences safety. In our next report, we will focus on the above points by involving experts of the fields.
Explainable Artificial Intelligence A Practical Guide
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Author : Parikshit Narendra Mahalle
language : en
Publisher: CRC Press
Release Date : 2024-12-02
Explainable Artificial Intelligence A Practical Guide written by Parikshit Narendra Mahalle and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-02 with Computers categories.
This book explores the growing focus on artificial intelligence (AI) systems in both industry and academia. It evaluates and justifies AI applications while enhancing trust in AI outcomes and aiding comprehension of AI feature development. Key topics include an overview of explainable AI, black box model understanding, interpretability techniques, practical XAI applications, and future trends and challenges in XAI. Technical topics discussed in the book include: Explainable AI overview Understanding black box models Techniques for model interpretability Practical applications of XAI Future trends and challenges in XAI
Explainable Ai Foundations Methodologies And Applications
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Author : Mayuri Mehta
language : en
Publisher: Springer Nature
Release Date : 2022-10-19
Explainable Ai Foundations Methodologies And Applications written by Mayuri Mehta and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-19 with Technology & Engineering categories.
This book presents an overview and several applications of explainable artificial intelligence (XAI). It covers different aspects related to explainable artificial intelligence, such as the need to make the AI models interpretable, how black box machine/deep learning models can be understood using various XAI methods, different evaluation metrics for XAI, human-centered explainable AI, and applications of explainable AI in health care, security surveillance, transportation, among other areas. The book is suitable for students and academics aiming to build up their background on explainable AI and can guide them in making machine/deep learning models more transparent. The book can be used as a reference book for teaching a graduate course on artificial intelligence, applied machine learning, or neural networks. Researchers working in the area of AI can use this book to discover the recent developments in XAI. Besides its use in academia, this book could be used by practitioners in AI industries, healthcare industries, medicine, autonomous vehicles, and security surveillance, who would like to develop AI techniques and applications with explanations.
Explainable Ai Transparency And Accountability In Machine Learning
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Author : Mrs.J.Ramya
language : en
Publisher: SK Research Group of Companies
Release Date : 2025-09-18
Explainable Ai Transparency And Accountability In Machine Learning written by Mrs.J.Ramya and has been published by SK Research Group of Companies this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-18 with Computers categories.
Mrs.J.Ramya, Assistant Professor, Department of Computer Science and Applications, Agurchand Manmull Jain College, Chennai, Tamil Nadu, India. Dr.Kalpana.A, Assistant Professor, Department of Computer Applications, Agurchand Manmull Jain College, Chennai, Tamil Nadu, India.
Explainable Interpretable And Transparent Ai Systems
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Author : B. K. Tripathy
language : en
Publisher: CRC Press
Release Date : 2024-08-23
Explainable Interpretable And Transparent Ai Systems written by B. K. Tripathy and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-23 with Technology & Engineering categories.
Transparent Artificial Intelligence (AI) systems facilitate understanding of the decision-making process and provide opportunities in various aspects of explaining AI models. This book provides up-to-date information on the latest advancements in the field of explainable AI, which is a critical requirement of AI, Machine Learning (ML), and Deep Learning (DL) models. It provides examples, case studies, latest techniques, and applications from domains such as healthcare, finance, and network security. It also covers open-source interpretable tool kits so that practitioners can use them in their domains. Features: Presents a clear focus on the application of explainable AI systems while tackling important issues of “interpretability” and “transparency”. Reviews adept handling with respect to existing software and evaluation issues of interpretability. Provides insights into simple interpretable models such as decision trees, decision rules, and linear regression. Focuses on interpreting black box models like feature importance and accumulated local effects. Discusses capabilities of explainability and interpretability. This book is aimed at graduate students and professionals in computer engineering and networking communications.
Artificial Intelligence For Autonomous Vehicles And Driver Assistance Systems
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Author : Meenakshi Malik
language : en
Publisher: CRC Press
Release Date : 2025-11-18
Artificial Intelligence For Autonomous Vehicles And Driver Assistance Systems written by Meenakshi Malik and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-18 with Technology & Engineering categories.
This book aims to provide a comprehensive exploration of the integration of machine learning and deep learning algorithms into the field of autonomous vehicles and advanced driver assistance systems. It also highlights the use of various sensing technologies such as LiDAR, radar, cameras, and ultrasonic sensors. This book presents machine learning techniques relevant to autonomous systems, with a focus on deep learning, neural networks, and reinforcement learning, providing readers with a solid understanding of these foundational concepts. It further includes real-world applications, offering insights into how these cutting-edge techniques are being employed by industry leaders and startups to improve the perception capabilities of autonomous vehicles. It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer engineering, and automotive engineering.