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This study investigates the fusion of onboard sensor data and Vehicle-to-Everything (V2X) information to enhance the cooperative perception capabilities of Connected and Automated Vehicles (CAVs). The research reveals that while cooperative perception can significantly improve situational awareness, it is also susceptible to errors from V2X data, such as packet losses and GNSS inaccuracies, which can lead to the generation of ghost vehicles. Ultimately, the findings underscore the need for addressing these challenges to fully realize the benefits of cooperative perception in automated driving systems.
Cooperative perception can enhance situational awareness for automated vehicles, but V2X data may introduce critical errors that lead to ghost vehicle phenomena.
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.