When Autonomous Vehicle Meets V2X Cooperative Perception: How Far Are We?

2025-09-29

AI Summary

V2X cooperative perception promises to mitigate long-range detection and occlusion challenges inherent in standalone vehicle perception, yet its operational complexity—stemming from heterogeneous sources (sensors, agents, fusion strategies, communication conditions)—and the lack of systematic error analysis impede reliable deployment. This study conducts the first systematic identification and taxonomy of six classes of cooperative perception errors. Through large-scale end-to-end empirical evaluation—spanning LiDAR/V2I/V2V configurations, multi-agent architectures, diverse fusion strategies (e.g., early/late/fusion-aware), and realistic communication interference scenarios—we find: (1) LiDAR-based cooperation achieves superior performance; (2) V2I and V2V exhibit markedly divergent behavior across fusion paradigms; (3) system robustness degrades severely under communication impairments; and (4) perceptual errors strongly correlate with driving violations. These findings provide critical empirical foundations for designing robust, safety-aware cooperative perception systems and implementing effective risk mitigation strategies.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Vision for Robotics & Autonomous Driving
• Multiagent Systems: Coordination and Collaboration

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

Abstract

With the tremendous advancement of deep learning and communication technology, Vehicle-to-Everything (V2X) cooperative perception has the potential to address limitations in sensing distant objects and occlusion for a single-agent perception system. V2X cooperative perception systems are software systems characterized by diverse sensor types and cooperative agents, varying fusion schemes, and operation under different communication conditions. Therefore, their complex composition gives rise to numerous operational challenges. Furthermore, when cooperative perception systems produce erroneous predictions, the types of errors and their underlying causes remain insufficiently explored. To bridge this gap, we take an initial step by conducting an empirical study of V2X cooperative perception. To systematically evaluate the impact of cooperative perception on the ego vehicle's perception performance, we identify and analyze six prevalent error patterns in cooperative perception systems. We further conduct a systematic evaluation of the critical components of these systems through our large-scale study and identify the following key findings: (1) The LiDAR-based cooperation configuration exhibits the highest perception performance; (2) Vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication exhibit distinct cooperative perception performance under different fusion schemes; (3) Increased cooperative perception errors may result in a higher frequency of driving violations; (4) Cooperative perception systems are not robust against communication interference when running online. Our results reveal potential risks and vulnerabilities in critical components of cooperative perception systems. We hope that our findings can better promote the design and repair of cooperative perception systems.

Problem

• Investigating error patterns in V2X cooperative perception systems
• Evaluating impact of communication conditions on perception performance
• Identifying risks and vulnerabilities in autonomous vehicle perception

Innovation

• LiDAR-based cooperation for highest perception performance
• V2I and V2V communication with distinct fusion schemes
• Systematic evaluation of six prevalent error patterns