Vehicle-to-Everything Cooperative Perception for Autonomous Driving

TL;DR

This survey reviews recent advances in vehicle-to-everything cooperative perception, highlighting models, techniques, challenges, and future research directions to improve autonomous driving safety and efficiency.

Contribution

It provides a comprehensive overview of mathematical models, key techniques, and challenges in vehicle-to-everything cooperative perception, offering insights for future research.

Findings

Enhanced perception range and accuracy through cooperation

Identification of key challenges like communication constraints

Outlook on privacy-preserving and collaborative intelligence methods

Abstract

Achieving fully autonomous driving with enhanced safety and efficiency relies on vehicle-to-everything cooperative perception, which enables vehicles to share perception data, thereby enhancing situational awareness and overcoming the limitations of the sensing ability of individual vehicles. Vehicle-to-everything cooperative perception plays a crucial role in extending the perception range, increasing detection accuracy, and supporting more robust decision-making and control in complex environments. This paper provides a comprehensive survey of recent developments in vehicle-to-everything cooperative perception, introducing mathematical models that characterize the perception process under different collaboration strategies. Key techniques for enabling reliable perception sharing, such as agent selection, data alignment, and feature fusion, are examined in detail. In addition, major challenges are discussed, including differences in agents and models, uncertainty in perception outputs, and the impact of communication constraints such as transmission delay and data loss. The paper concludes by outlining promising research directions, including privacy-preserving artificial intelligence methods, collaborative intelligence, and integrated sensing frameworks to support future advancements in vehicle-to-everything cooperative perception.