Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems
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 cybe...
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| Erakunde egilea: | |
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| Beste egile batzuk: | , |
| Formatua: | Baliabide elektronikoa eBook |
| Hizkuntza: | ingelesa |
| Argitaratua: |
Cham :
Springer International Publishing : Imprint: Springer,
2023.
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| Edizioa: | 1st ed. 2023. |
| Gaiak: | |
| Sarrera elektronikoa: | https://doi.org/10.1007/978-3-031-28016-0 |
| Etiketak: |
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| Gaia: | 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. 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 emerging autonomous vehicle systems; The content is relevant to researchers and industry practitioners interested in future automotive platforms. . |
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| Deskribapen fisikoa: | 1 online resource (XV, 789 p.). |
| ISBN: | 9783031280160 |
| DOI: | 10.1007/978-3-031-28016-0 |