This survey analyzes intermediate fusion methods in collaborative perception for autonomous driving, categorized by real-world challenges. We examine various methods, detailing their features and the evaluation metrics they employ. The focus is on addressing challenges like transmission efficiency, localization errors, communication disruptions, and heterogeneity. Moreover, we explore strategies to counter adversarial attacks and defenses, as well as approaches to adapt to domain shifts. The objective is to present an overview of how intermediate fusion methods effectively meet these diverse challenges, highlighting their role in advancing the field of collaborative perception in autonomous driving.

翻译：本综述分析了自动驾驶协同感知中的中间融合方法，并依据真实世界挑战对其进行分类。我们审视了多种方法，详细阐述了其特征及所采用的评估指标。重点在于解决传输效率、定位误差、通信中断和异质性等挑战。此外，我们探讨了应对对抗性攻击与防御的策略，以及适应领域偏移的方法。目标是概述中间融合方法如何有效应对这些多样化挑战，突出其在推动自动驾驶协同感知领域发展中的作用。