梳理自动驾驶车辆网络优化挑战与未来方向，澄清认知误区。
Network Optimization Aspects of Autonomous Vehicles: Challenges and Future Directions
- 从多学科视角分析车联网协同感知等关键技术。
- 结合实际案例与实验，揭示网络优化核心瓶颈。
- 适合关注智能交通系统与车联网的科研与工程人员。
全球城市化、人口增长及新兴网络解决方案正加速推动车联网与自动驾驶汽车（CAVs）产业的发展。公众对CAVs存在诸多事实、误解甚至过度期待。本文旨在全面回顾该领域，消除认知偏差，并通过协同感知等多学科方法，阐述自动驾驶车辆网络优化的未来发展方向。基于我们在CAVs领域的丰富经验，分享所获洞察、相关应用场景及实验结果。
原文摘要 · Abstract (English)
Global megatrends, such as urbanization, population growth, and emerging network solutions are accelerating the development of the Connected and Autonomous Vehicles (CAVs) industry. There are many truths, some misconceptions, and even some excitement about CAVs in the public's opinion. The main objective of the current article is to provide a comprehensive review, eliminate misconceptions, and outline the future of the network optimization aspects of autonomous vehicles by presenting various multidisciplinary methods, such as cooperative perception. Given our extensive experience with CAVs, we are aiming to share some of the insights and knowledge we have gained, along with relevant use-cases and experiment results.
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