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.

该研究综述了协作感知中的中间融合方法，针对自动驾驶领域中的实际挑战进行了分类分析。研究详细探讨了各种方法及其评估指标。重点解决了传输效率、定位误差、通信中断和异质性等挑战，并探索了对抗性攻击和防御的策略，以及应对领域转换的方法。综述旨在呈现中间融合方法如何有效应对这些多样挑战，突出它们在推动自动驾驶中协作感知领域的作用。

基于真实世界挑战分类的协同感知中间融合方法调研