Task-oriented Vehicle-to-Everything (V2X) networks have recently been proposed to scalably support the large-scale deployment of connected vehicles within the Internet of Vehicles (IoV) vision. In task-oriented V2X networks, vehicles select the content of the transmitted messages based on its relevance to the intended receivers. However, relevance estimation can be quite challenging, especially in highly dynamic and complex vehicular scenarios. Relevance estimation errors can cause a vehicle to omit relevant information from its transmitted message, leading to a content-selection error. Content-selection errors reduce the amount of relevant information available at the receivers and can potentially impair their situational awareness. This work analyses the impact of content-selection errors on task-oriented V2X networks. Our analysis reveals that task-oriented V2X networks feature an inherent resilience to content-selection errors that guarantees a consistent delivery of relevant information even under high relevance estimation error conditions. Moreover, we identify the fundamental conditions underpinning such inherent resilience. These conditions can be encountered in other task-oriented networks where multiple transmitters select the content of their messages based on the task-related requirements of a common set of intended receivers.

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