  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.
