论文标题
车辆网络中合作意识消息的现实产生的经验模型
Empirical Models for the Realistic Generation of Cooperative Awareness Messages in Vehicular Networks
论文作者
论文摘要
大多数V2X（车辆到所有内容）应用程序都依赖于ETSI或BSM中称为CAM（合作意识消息）的广播意识消息（基本的安全消息），以SAE标准为单位。大量研究已致力于确保其可靠的传播。但是，迄今为止，研究通常基于简化的数据流量模型，这些模型会定期或以恒定消息大小生成意识消息。这些模型并不能准确代表遵循特定基于移动性规则的CAM消息的真实产生。使用简化和不切实际的交通模型可以显着影响研究的结果和有效性，因此需要准确的模型来产生意识消息。本文提出了可以实际生成CAM消息的第一组模型。这些模型是由两家城市，郊区和高速公路测试驱动器收集的实际痕迹创建的。这些模型基于MTH Order Markov源，并建模CAM的大小和CAM之间的时间间隔。这些模型是公开提供给社区的，并且可以轻松地集成到任何模拟器中。
Most V2X (Vehicle-to-Everything) applications rely on broadcasting awareness messages known as CAM (Cooperative Awareness Messages) in ETSI or BSM (Basic Safety Message) in SAE standards. A large number of studies have been devoted to guarantee their reliable transmission. However, to date, the studies are generally based on simplified data traffic models that generate awareness messages at periodic intervals or with a constant message size. These models do not accurately represent the real generation of CAM messages that follow specific mobility-based rules. Using simplified and unrealistic traffic models can significantly impact the results and validity of the studies, and hence accurate models for the generation of awareness messages are necessary. This paper proposes the first set of models that can realistically generate CAM messages. The models have been created from real traces collected by two car manufacturers in urban, sub-urban and highway test drives. The models are based on mth order Markov sources, and model the size of CAMs and the time interval between CAMs. The models are openly provided to the community and can be easily integrated into any simulator.
