A Survey on Intermediate Fusion Methods for Collaborative Perception Categorized by Real World Challenges

TL;DR

This survey reviews intermediate fusion methods in collaborative perception for autonomous driving, focusing on real-world challenges like communication issues, localization errors, and adversarial attacks, and discusses strategies to address these problems.

Contribution

It categorizes and analyzes various intermediate fusion methods based on real-world challenges, providing a comprehensive overview of their features and evaluation metrics.

Findings

Intermediate fusion methods improve robustness against communication disruptions.

Strategies to counter adversarial attacks enhance safety in collaborative perception.

Methods adapt to domain shifts, increasing reliability in diverse environments.

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.