智能汽车从集中式架构转向分布式区域架构，提升系统性能与可靠性。
Zonal Architecture Development with evolution of Artificial Intelligence
- 采用分布式区域架构替代传统集中式设计，优化系统可扩展性。
- 结合边缘计算与神经网络，实现传感器融合与智能决策能力提升。
- 适用于自动驾驶车辆与智能电网系统，适合汽车工程与AI融合研究者。
本文阐述了传统集中式架构如何向分布式区域架构演进，以应对可扩展性、可靠性、性能和成本效益方面的挑战。重点探讨了边缘计算与神经网络在自动驾驶车辆中实现复杂传感器融合与决策能力的作用。此外，还分析了区域架构对车辆诊断、电力分配及智能电源管理系统的影响。文中提出了有效实施区域架构的关键设计考量，并概述了当前挑战与未来发展方向。本文旨在全面理解区域架构如何塑造汽车技术的未来，特别是在自动驾驶车辆与人工智能集成背景下的应用。
原文摘要 · Abstract (English)
This paper explains how traditional centralized architectures are transitioning to distributed zonal approaches to address challenges in scalability, reliability, performance, and cost-effectiveness. The role of edge computing and neural networks in enabling sophisticated sensor fusion and decision-making capabilities for autonomous vehicles is examined. Additionally, this paper discusses the impact of zonal architectures on vehicle diagnostics, power distribution, and smart power management systems. Key design considerations for implementing effective zonal architectures are presented, along with an overview of current challenges and future directions. The objective of this paper is to provide a comprehensive understanding of how zonal architectures are shaping the future of automotive technology, particularly in the context of self-driving vehicles and artificial intelligence integration.
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