| 書目名稱 | Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems | | 編輯 | Vipin Kumar Kukkala,Sudeep Pasricha | | 視頻video | http://file.papertrans.cn/621/620568/620568.mp4 | | 概述 | The book describes state-of-the-art solutions to design, secure, robust, and time critical automotive systems.Various approaches are discussed that will impact the design of the emerging autonomous ve | | 圖書封面 |  | | 描述 | .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.. | | 出版日期 | Book 2023 | | 關(guān)鍵詞 | Automotive embedded systems; Machine learning; Intrusion detection systems; Reliable automotive network | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-28016-0 | | isbn_softcover | 978-3-031-28018-4 | | isbn_ebook | 978-3-031-28016-0 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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