Scalable Vehicle-to-Everything (V2X) networks are key to support the large-scale deployment of connected and automated mobility. However, the scalability of V2X networks is currently challenged by the limitations of existing V2X communication paradigms, which prioritize the reliable and timely delivery of the transmitted information over a careful message content selection - an approach that can potentially lead to the transmission of unnecessary information and an inefficient usage of communication resources. Semantic and task-oriented V2X communications have recently been proposed to address these scalability challenges by focusing on the content of the transmitted messages, particularly on its relevance to the intended receivers. In this paper, we numerically demonstrate that semantic and task-oriented V2X communications can substantially improve the scalability of V2X networks, increasing by up to a 4.1x factor the number of supported vehicles under high-density conditions. In addition, we show that semantic and task-oriented V2X communications can also decrease the inter-reception time between consecutive messages by up to 67% and lead to a twofold increase in the probability of successfully delivering all required relevant information to the intended receivers.

翻译：可扩展的车联网是支持大规模部署互联及自动化交通的关键。然而，现有车联网通信范式优先保障传输信息的可靠性与时效性，而非精细化的消息内容选择，这可能导致无关信息的传输和通信资源的低效利用，从而对网络可扩展性构成挑战。近期提出的语义与任务导向车联网通信聚焦于传输消息的内容，特别是其对目标接收者的相关性，旨在应对上述可扩展性难题。本文通过数值仿真证明，语义与任务导向车联网通信能显著提升网络可扩展性：在高密度条件下，网络支持的车辆数量最高提升至4.1倍；此外，该方法还能将连续消息间的接收间隔降低67%，并将所有相关信息成功递送至目标接收者的概率提升至两倍。