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作感知中的中间融合方法，并依据实际世界挑战对其进行了分类。我们考察了多种方法，详细阐述了它们的特性及所采用的评估指标。重点在于应对传输效率、定位误差、通信中断以及异构性等挑战。此外，我们还探讨了抵御对抗攻击与防御的策略，以及适应领域偏移的方法。目标在于概述中间融合方法如何有效应对这些多样化的挑战，突出其在推动自动驾驶协作感知领域发展中的作用。