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.

翻译：面向任务的车联网（V2X）网络最近被提出，旨在可扩展地支持车联网愿景中大规模互联车辆的部署。在面向任务的V2X网络中，车辆根据传输内容与预期接收者的相关性来选择消息内容。然而，相关性估计可能极具挑战性，尤其是在高度动态和复杂的车辆场景中。相关性估计错误可能导致车辆在传输的消息中遗漏相关信息，从而引发内容选择错误。内容选择错误会减少接收者可用的相关信息量，并可能损害其态势感知能力。本文分析了内容选择错误对面向任务的V2X网络的影响。我们的分析表明，面向任务的V2X网络对内容选择错误具有内在鲁棒性，即使在相关性估计错误率较高的情况下，也能保证相关信息的持续传递。此外，我们揭示了支撑这种内在鲁棒性的基本条件。这些条件也可能出现在其他面向任务的网络中，其中多个发送者根据一组共同预期接收者的任务相关要求来选择其消息内容。