Vehicle-to-Everything Cooperative Perception for Autonomous Driving
arXiv:2310.03525 · doi:10.1109/JPROC.2025.3600903
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.
This article has been accepted for publication in Proceedings of the IEEE on 11 August 2025
References in corpus (20)
- Integrated Sensing and Communication Signals Toward 5G-A and 6G: A Survey
- A Vision of C-V2X: Technologies, Field Testing and Challenges with Chinese Development
- Cooperative Perception for 3D Object Detection in Driving Scenarios using Infrastructure Sensors
- Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges
- Learning for Vehicle-to-Vehicle Cooperative Perception under Lossy Communication
- COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles
- Generation of Cooperative Perception Messages for Connected and Automated Vehicles
- PillarGrid: Deep Learning-based Cooperative Perception for 3D Object Detection from Onboard-Roadside LiDAR
- FedBEVT: Federated Learning Bird's Eye View Perception Transformer in Road Traffic Systems
- DOLPHINS: Dataset for Collaborative Perception enabled Harmonious and Interconnected Self-driving
- CMP: Cooperative Motion Prediction with Multi-Agent Communication
- Cooperative Perception with Learning-Based V2V communications
- DUSA: Decoupled Unsupervised Sim2Real Adaptation for Vehicle-to-Everything Collaborative Perception
- Online V2X Scheduling for Raw-Level Cooperative Perception
- Modeling Perception Errors towards Robust Decision Making in Autonomous Vehicles
- RoCo:Robust Collaborative Perception By Iterative Object Matching and Pose Adjustment
- Cyber Mobility Mirror for Enabling Cooperative Driving Automation in Mixed Traffic: A Co-Simulation Platform
- CoPEM: Cooperative Perception Error Models for Autonomous Driving
- Collective PV-RCNN: A Novel Fusion Technique using Collective Detections for Enhanced Local LiDAR-Based Perception
- Unlocking Past Information: Temporal Embeddings in Cooperative Bird's Eye View Prediction