How the Fusion of Onboard Sensors and V2X Data can Improve (or not) the Cooperative Perception of Connected Automated Vehicles

2026-07-08

AI Summary

This study addresses the challenges of cooperative perception under adverse conditions, where onboard sensors suffer performance degradation and vehicle-to-everything (V2X) data fusion may introduce spurious targets—such as “ghost vehicles”—due to sensor inaccuracies, communication packet loss, and GNSS positioning errors. The authors propose a collaborative perception framework that integrates onboard sensor data with V2X information and systematically investigates the error propagation mechanisms in multi-source data fusion and their impact on perception performance. Experimental results demonstrate that judicious fusion significantly extends perceptual range and enhances accuracy, whereas improper fusion can lead to notable performance degradation. This work elucidates critical challenges in cooperative perception and provides both theoretical foundations and practical guidance for optimizing fusion strategies.

Abstract

Automated vehicles rely on onboard sensors to perceive their surroundings and navigate autonomously. However, sensor performance may degrade under adverse weather conditions or when line-of-sight is obstructed. Cooperative perception (or collective perception) is expected to mitigate these limitations by enabling Connected and Automated Vehicles (CAVs) to share sensor data and collaboratively enhance situational awareness. Several studies have analyzed the potential of cooperative perception, yet the fusion of V2X data with information from onboard sensors has received limited focus. V2X data may contain errors that affect the quality of the fused data, and hence the effectiveness of cooperative perception. This study analyzes the impact of sensing measurement errors, V2X packet losses, and GNSS inaccuracies on the effectiveness of cooperative perception. The results highlight the potential of cooperative perception to enhance perception levels and range compared to using onboard sensors alone. However, they also identify key challenges related to the generation of ghost vehicles during the fusion process, which must be addressed to prevent V2X data from introducing additional errors when fused with onboard sensor data.

Problem

• cooperative perception
• sensor fusion
• V2X
• connected automated vehicles
• ghost vehicles

Innovation

• cooperative perception
• sensor fusion
• V2X
• ghost vehicles
• connected automated vehicles