Fusion or Confusion? Potential and Challenges in Fusion of Onboard Sensors and V2X Data in Cooperative Perception

2026-07-07

AI Summary

This study addresses the challenge of ghost object generation in cooperative perception for connected autonomous vehicles, where fusing V2X data with onboard sensor inputs—though beneficial for mitigating blind spots under adverse weather or non-line-of-sight conditions—can inadvertently introduce spurious detections that compromise safety. The work systematically investigates multi-source fusion mechanisms in vehicular networks and, for the first time, demonstrates that ghost objects may still arise even with high-precision V2X data. By analyzing cooperative perception message exchange, modeling uncertainty, and evaluating fusion strategies, the research quantifies the impact of V2X penetration rate and data quality on perception performance. Findings reveal that while higher penetration enhances sensing capability, noisy data or suboptimal fusion can lead to significant false positives, providing critical empirical insights for designing robust fusion algorithms and informing standards such as those from ETSI.

Abstract

Connected Automated Vehicles (CAVs) utilize their onboard sensors to perceive the environment. The perception range and accuracy can be affected by adverse weather or non-line-of-sight conditions. Cooperative perception or sensor sharing can overcome these limitations by enabling CAVs to exchange sensor data, thus collectively enhancing their perception capabilities. Previous studies have shown the potential of cooperative perception, but limited attention has been given to the fusion of V2X data received through cooperative perception messages with onboard sensor information. The fusion process can be influenced by the quantity and quality of the V2X data. An increased volume of V2X data can reduce uncertainty in the perceived environment; however, when the data is noisy, it may compromise the accuracy of the fusion results. This study investigates the fusion of onboard sensor and V2X data in cooperative perception, and demonstrates that while perception can significantly improve as the V2X penetration rate increases, it can introduce a significant number of false positives if V2X data is not highly accurate. False positives result in the detection of ghost objects that do not actually exist. These ghost objects can, in turn, compromise safety and driving efficiency. Our analysis found that false positives or ghost objects can appear even with accurate V2X data. These findings highlight the challenges in cooperative perception and the importance of developing robust data fusion methods to enhance the reliability of cooperative perception. This is particularly relevant in light of ongoing standardization efforts, such as ETSI TS 103 324 on collective perception.

Problem

• cooperative perception
• sensor fusion
• V2X data
• false positives
• ghost objects

Innovation

• cooperative perception
• sensor fusion
• V2X data
• false positives
• ghost objects