A Survey on Intermediate Fusion Methods for Collaborative Perception Categorized by Real World Challenges
It provides an overview for researchers and practitioners in autonomous driving, but is incremental as it surveys existing methods without introducing new techniques.
This survey analyzes intermediate fusion methods for collaborative perception in autonomous driving, categorizing them by real-world challenges such as transmission efficiency and localization errors, and highlights their role in advancing the field.
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.