Machine Learning and Optimization Techniques for Automotive Cyber-Physical SystemsКНИГИ » ТЕХНИЧЕСКИЕ НАУКИ
Название: Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems Автор: Vipin Kumar Kukkala, Sudeep Pasricha Издательство: Springer Год: 2023 Страниц: 782 Язык: английский Формат: pdf (true) Размер: 32.1 MB
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.
Modern vehicles are examples of complex cyber-physical systems (CPS) with tens to hundreds of interconnected Electronic Control Units (ECUs) that manage various vehicular subsystems. The ECU functionalities range from simple window control to highly complex Advanced Driver Assistance System (ADAS) applications such as adaptive cruise control, lane-keeping assist, and collision avoidance. With the shift toward autonomous driving and ever-increasing connectivity with external systems, connected and autonomous vehicles (CAVs) have emerged as one of the most complex automotive CPS. The modern CAV ecosystem is characterized by increased ECU count, greater software complexity, and highly complex heterogeneous vehicular networks (within and outside the vehicle). Moreover, the aggressive attempts of automakers to make vehicles fully autonomous have led to the adoption of Artificial Intelligence (AI)-based techniques for advanced perception and control. These paradigm shifts have resulted in increased overall complexity and have severe performance and safety implications on the automotive CPS. This book identifies and explores the most challenging issues (listed below) in designing a safe, secure, and robust automotive CPS.
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Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems 
