Explainable Artificial Intelligence for Autonomous Vehicles: Concepts, Challenges, and Applications
Malik, Kamal, Sharma, Moolchand, Deswal, Suman
相關主題
商品描述
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.
商品描述(中文翻譯)
《可解釋的人工智慧在自駕車中的應用：概念、挑戰與應用》是一本全面指南，旨在開發和應用可解釋的人工智慧（XAI）於自駕車的背景下。書中首先介紹了XAI及其在自駕車開發中的重要性，並概述了傳統黑箱AI模型的挑戰和限制，以及XAI如何透過提供透明度和可解釋性來解決這些挑戰，從而改善自駕車的決策過程。接著，書中涵蓋了自駕車中XAI的最先進技術和方法，包括模型無關的方法、事後解釋以及局部和全局可解釋性技術。書中還討論了XAI在自駕車中的挑戰和應用，例如增強安全性和可靠性、提高用戶信任和接受度，以及提升整體系統性能。倫理和社會考量也在書中有所提及，例如XAI對用戶隱私和自主性的影響，以及XAI系統中潛在的偏見和歧視問題。此外，書中提供了對未來方向和自駕車XAI新興趨勢的見解，例如將XAI與機器學習和區塊鏈等其他先進技術整合，以及XAI在自駕車產業中啟用新應用和服務的潛力。總體而言，這本書旨在幫助讀者全面理解XAI及其在自駕車中的應用，以便開發有效的XAI解決方案，增強自駕車系統的安全性、可靠性和性能，同時提高用戶的信任和接受度。
本書內容包括：
- 討論相機訪問的身份驗證機制、數據保護的加密協議以及相機系統的訪問控制措施。
- 展示在自駕車中實施可解釋的人工智慧時面臨的挑戰，如與現有系統的整合、隱私和安全問題。
- 涵蓋資源管理、優化、自適應控制和決策的可解釋人工智慧。
- 解釋重要主題，如車對車（V2V）通信、車對基礎設施（V2I）通信、遠程監控和控制。
- 強調增強自駕車的安全性、可靠性、整體系統性能以及提高用戶信任。
本書旨在為研究人員、工程師和實務工作者提供對XAI在自駕車背景下的關鍵概念、挑戰和應用的全面理解。主要針對電機工程、電子與通信工程、計算機科學與工程、資訊科技及汽車工程等領域的高年級本科生、研究生及學術研究人員撰寫。
作者簡介
Kamal Malik is currently working as a Professor in CSE in the School of Engineering and Technology at CTU Ludhiana, Punjab, India. She has published Scientific Research Publications in reputed International Journals, including SCI and Scopus indexed Journals.
Moolchand Sharma is currently an Assistant Professor in the Department of Computer Science and Engineering at the Maharaja Agrasen Institute of Technology, GGSIPU Delhi. He has published scientific research publications in reputed international journals and conferences, including SCI-indexed and Scopus-indexed journals.
Suman Deswal holds a Ph.D. from DCR University of Science & Technology, Murthal, India. She completed her M. Tech (CSE) from Kurukshetra University, Kurukshetra, India, and B. Tech (Computer Science & Engg.) from CR State College of Engg., Murthal, India, in 2009 and 1998, respectively. She has 18 years of teaching experience and works as a Professor in the Department of Computer Science and Engineering at DCR University of Science and Technology, Murthal, India. Her research area includes wireless networks, heterogeneous networks, distributed systems, Machine Learning and Bioinformatics.
Umesh Gupta is currently an Associate Professor at the School of Computer Science Engineering and Technology at Bennett University, Times of India Group, Greater Noida, Uttar Pradesh, India. He received a Doctor of Philosophy (Ph.D.) (Machine Learning) from the National Institute of Technology, Arunachal Pradesh, India. He has awarded a gold medal for his Master of Engineering (M.E.) from the National Institute of Technical Teachers Training and Research (NITTTR), Chandigarh, India, and Bachelor of Technology (B.Tech.) from Dr. APJ, Abdul Kalam Technical University, Lucknow, India. His research interests include SVM, ELM, RVFL, machine learning, and deep learning approaches.
Deevyankar Agarwal is a lecturer at the University of Technology and Applied Sciences in Muscat, Oman. He works in the Engineering Department, EEE Section (Computer Engineering), . He has 22 years of teaching and research experience. He is currently a doctoral researcher at the University of Valladolid, Spain.
Yahya Obaid Al Shamsi is working as the Dean of Engineering at the University of Technology and Applied Sciences in Muscat, Oman. He has 25 years of teaching and research experience. He got his PhD from the University of Bath, Department of Architecture and Civil Engineering, UK.
作者簡介(中文翻譯)
Kamal Malik 目前在印度旁遮普邦的 CTU Ludhiana 工程與技術學院擔任計算機科學與工程系教授。她在多本知名國際期刊上發表了科學研究論文，包括 SCI 和 Scopus 索引的期刊。
Moolchand Sharma 目前是德里 Maharaja Agrasen Institute of Technology 計算機科學與工程系的助理教授。他在多本知名國際期刊和會議上發表了科學研究論文，包括 SCI 索引和 Scopus 索引的期刊。
Suman Deswal 擁有印度 Murthal 的 DCR 科學與技術大學的博士學位。她於 2009 年在印度 Kurukshetra 大學獲得計算機科學與工程碩士學位，並於 1998 年在印度 Murthal 的 CR 州立工程學院獲得計算機科學與工程學士學位。她擁有 18 年的教學經驗，並在印度 Murthal 的 DCR 科學與技術大學計算機科學與工程系擔任教授。她的研究領域包括無線網絡、異構網絡、分散式系統、機器學習和生物資訊學。
Umesh Gupta 目前是印度大諾伊達 Bennett University 計算機科學工程與技術學院的副教授。他在印度阿魯納恰爾邦的國立技術學院獲得機器學習的哲學博士（Ph.D.）。他在印度昌迪加爾的國立技術教師培訓與研究院（NITTTR）獲得工程碩士（M.E.）金獎，並在印度勒克瑙的 Dr. APJ Abdul Kalam 技術大學獲得技術學士（B.Tech.）學位。他的研究興趣包括支持向量機（SVM）、極限學習機（ELM）、隨機向量功能連接（RVFL）、機器學習和深度學習方法。
Deevyankar Agarwal 是阿曼馬斯喀特應用科技大學的講師。他在工程系的電子與電氣工程（計算機工程）部門工作，擁有 22 年的教學和研究經驗。目前他是西班牙巴利亞多利德大學的博士研究生。
Yahya Obaid Al Shamsi 擔任阿曼馬斯喀特應用科技大學的工程學院院長，擁有 25 年的教學和研究經驗。他在英國巴斯大學建築與土木工程系獲得博士學位。
