Quantifying Ghost Object Detectability in V2X Networks via Data-Driven Traffic Generation

Vehicle-to-everything (V2X) communication enables connected vehicles to share sensor data, extending awareness beyond the individual field of view. This cooperation, however, exposes participants to the risk of receiving unreliable data, which is mitigated by misbehaviour detection mechanisms. A representative threat is the ghost object: a vehicle whose presence is announced in V2X messages but that does not physically exist on the road. Ghost objects may originate from deliberate attacks, e.g., a malicious participant claiming priority at an intersection, but also from perception faults on the sender's side, such as partial occlusions causing duplicate tracks. Since misbehaviour detection relies on cross-checking observations from multiple participants, a ghost attack can be exposed only if honest, sensor-equipped vehicles actually cover its alleged location. Evaluating how often real traffic satisfies this condition is challenging: hand-crafted scenes lack statistical significance, while large-scale simulations obscure the conditions driving to detection failure. We address this challenge by using a learned generative traffic model to synthesize large ensembles of realistic scenes (1,000 per experimental condition) over highway and intersection layouts. Through explicit sensor and occlusion modeling, we estimate the probability that a ghost object is observed by too few equipped vehicles to be reliably contradicted, thus remaining effectively undetected. We analyze how this probability varies with road topology, traffic density, environmental occlusion, and sensor penetration rate, under both awareness-only messaging (CAMs) and cooperative perception sharing (CPMs). We show that the dominant risk factor is the local visibility structure shaped by static obstructions preventing line-of-sight between equipped vehicles and the ghost location, and that the vulnerability transition shifts with penetration rate. Our analysis shows how generative traffic models may help to achieve a finer characterization of the performance of misbehaviour detection algorithms and ultimately a better understanding of the underlying phenomena.