Abstract
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.
Abstract (translated)
这份调查对自动驾驶中协作感知的中间融合方法进行了分析，按照现实世界的挑战进行分类。我们检查了各种方法，详细介绍了它们的特征以及它们采用的评估指标。重点在于解决诸如传输效率、定位误差、通信干扰和异质性等问题。此外，我们还探讨了应对对抗攻击和防御策略以及适应领域转移的方法。调查的目的是提供一个概述，表明中间融合方法如何有效应对这些多样挑战，突出它们在自动驾驶领域协作感知中的作用。
URL
https://arxiv.org/abs/2404.16139
