Cooperative Vehicular(合作车)研究综述
Cooperative Vehicular 合作车 - Therefore, in this paper, we analyze the sensing performance of distributed cooperative vehicular network over $\alpha-\eta-K-\mu$ fading model, which is more suitable for vehicular communication in comparison with existing fading models. [1] Moreover, by collaborating with the non-orthogonal multiple access (NOMA) technique, we investigate the practical application of GQSM to cooperative vehicular networks and propose the cooperative GQSM with OMA (C-OMA-GQSM) and cooperative GQSM with NOMA (C-NOMA-GQSM) schemes. [2] Initially, the study devises a novel cooperative vehicular fog cloud network (VFCN) based on container microservices which offers cost-efficient and mobility-aware services with rich resources for processing. [3] This paper proposes a new optimization framework for NOMA-enabled cooperative vehicular network. [4] In this paper, we study the physical layer security of multiple decode-and-forward cooperative vehicular to vehicular communications in which a known eavesdropper can receive the signals from source and relay nodes in the broadcast and cooperative phases, respectively. [5] Vehicular networks have tremendous potential to improve road safety, traffic efficiency, and driving comfort, where cooperative vehicular safety applications are a significant branch. [6] Vehicle-to-vehicle (V2V) communication-based cooperative vehicular networks are a key technology for future smarter transportation systems as they provide various applications for addressing road safety and traffic congestion. [7] Aerial-ground cooperative vehicular networks are envisioned as a novel paradigm in B5G/6G visions. [8] Cooperative vehicular technology in recent times has aided in realizing some state-of-art technologies like autonomous driving. [9] The connectivity between cars is modeled with a game theory framework considering a non cooperative vehicular mobility. [10] We thus propose a modular decentralized FL solution and we discuss its application to road user classification in a cooperative vehicular sensing use case. [11] Cooperative vehicular cyber physical systems (vCPSs) can shape a group of mobile robots that can accomplish a cooperative task using wireless communications and distributed control. [12] The paper investigates the improvement of using maximum ratio combining (MRC) in cooperative vehicular communications (VCs) transmission schemes considering non-orthogonal multiple access scheme (NOMA) at intersections. [13] This paper also finds that optimized combination of message length and transmit rate ensures the optimal channel utilization for cooperative vehicular communication, which in turn improves the situational awareness of the whole system. [14] The experimental results confirm that the cooperative vehicular VLC architecture is a promising approach concerning communications between road infrastructures and cars, fulfilling data privacy. [15] We study the performance of a cooperative vehicular communication system in a highway traffic scenario, where the locations of co-channel interfering vehicles are modeled by a one-dimensional Poisson point process (PPP). [16] Both clustering and cluster-head vehicles (CHVs) cooperative communication have been employed for reducing traffic congestion to improve road traffic efficiency in cooperative vehicular networks. [17] Scalable communication is of utmost importance for reliable dissemination of time-sensitive information in cooperative vehicular ad-hoc networks (VANETs), which is, in turn, an essential prerequisite for the proper operation of the critical cooperative safety applications. [18] The performance of cooperative vehicular applications is tightly dependent on the reliability of the underneath Vehicle-to-Everything (V2X) communication technology. [19] In this paper, we study cooperative vehicular communications (VC) transmission schemes using non-orthogonal multiple access scheme (NOMA) at intersections, in the presence of interference, when two destination nodes are involved. [20] Aiming at the cooperative vehicular networks, this paper adopts a method based on driver's emotional state, and proposes Anomaly Detection Based on Driver's Emotional State (EAD) algorithm to realize real-time detection of data related to safe driving. [21] This may have a detrimental impact on cooperative vehicular safety applications that build on the reliable regular broadcasting of status messages by vehicles in a local neighborhood. [22]因此，在本文中，我们分析了分布式协作车载网络在$\alpha-\eta-K-\mu$衰落模型上的感知性能，与现有的衰落模型相比，该模型更适合车载通信。 [1] 此外，通过与非正交多路访问（NOMA）技术的合作，我们研究了GQSM在协作车辆网络中的实际应用，并提出了具有OMA的协作GQSM（C-OMA-GQSM）和具有NOMA的协作GQSM（C-NOMA）。 -GQSM) 方案。 [2] nan [3] nan [4] nan [5] 车载网络在改善道路安全、交通效率和驾驶舒适性方面具有巨大潜力，其中协作式车辆安全应用是一个重要分支。 [6] 基于车对车 (V2V) 通信的协作车辆网络是未来更智能的交通系统的关键技术，因为它们为解决道路安全和交通拥堵提供了各种应用。 [7] nan [8] 近年来，协同车辆技术有助于实现一些最先进的技术，如自动驾驶。 [9] nan [10] 因此，我们提出了一种模块化的分散式 FL 解决方案，并讨论了其在协作车辆传感用例中在道路用户分类中的应用。 [11] 协作车辆信息物理系统（vCPS）可以塑造一组移动机器人，这些机器人可以使用无线通信和分布式控制完成协作任务。 [12] 本文研究了在交叉口考虑非正交多址接入方案（NOMA）的协同车辆通信（VCs）传输方案中使用最大比合并（MRC）的改进。 [13] 本文还发现，消息长度和传输速率的优化组合确保了协作车辆通信的最佳信道利用率，从而提高了整个系统的态势感知能力。 [14] 实验结果证实，协作式车辆 VLC 架构是一种有前途的道路基础设施与汽车之间通信的方法，可实现数据隐私。 [15] 我们研究了高速公路交通场景中协作车辆通信系统的性能，其中同信道干扰车辆的位置由一维泊松点过程（PPP）建模。 [16] 集群和集群头车辆（CHV）协作通信都已被用于减少交通拥堵，以提高协作车辆网络中的道路交通效率。 [17] 可扩展通信对于在协作车载自组织网络 (VANET) 中可靠地传播时间敏感信息至关重要，而这反过来又是关键协作安全应用正常运行的必要先决条件。 [18] 协作车辆应用程序的性能紧密依赖于底层车辆到一切 (V2X) 通信技术的可靠性。 [19] 在本文中，我们研究了在涉及两个目标节点时，在存在干扰的情况下，在交叉路口使用非正交多址接入方案 (NOMA) 的协作车辆通信 (VC) 传输方案。 [20] 针对协同车联网，采用基于驾驶员情绪状态的方法，提出基于驾驶员情绪状态的异常检测（EAD）算法，实现对安全驾驶相关数据的实时检测。 [21] 这可能对合作车辆安全应用产生不利影响，这些应用建立在当地社区车辆可靠定期广播状态消息的基础上。 [22]
non orthogonal multiple 非正交倍数
Moreover, by collaborating with the non-orthogonal multiple access (NOMA) technique, we investigate the practical application of GQSM to cooperative vehicular networks and propose the cooperative GQSM with OMA (C-OMA-GQSM) and cooperative GQSM with NOMA (C-NOMA-GQSM) schemes. [1]此外，通过与非正交多路访问（NOMA）技术的合作，我们研究了GQSM在协作车辆网络中的实际应用，并提出了具有OMA的协作GQSM（C-OMA-GQSM）和具有NOMA的协作GQSM（C-NOMA）。 -GQSM) 方案。 [1]
cooperative vehicular network 合作车联网
Therefore, in this paper, we analyze the sensing performance of distributed cooperative vehicular network over $\alpha-\eta-K-\mu$ fading model, which is more suitable for vehicular communication in comparison with existing fading models. [1] Moreover, by collaborating with the non-orthogonal multiple access (NOMA) technique, we investigate the practical application of GQSM to cooperative vehicular networks and propose the cooperative GQSM with OMA (C-OMA-GQSM) and cooperative GQSM with NOMA (C-NOMA-GQSM) schemes. [2] This paper proposes a new optimization framework for NOMA-enabled cooperative vehicular network. [3] Vehicle-to-vehicle (V2V) communication-based cooperative vehicular networks are a key technology for future smarter transportation systems as they provide various applications for addressing road safety and traffic congestion. [4] Aerial-ground cooperative vehicular networks are envisioned as a novel paradigm in B5G/6G visions. [5] Both clustering and cluster-head vehicles (CHVs) cooperative communication have been employed for reducing traffic congestion to improve road traffic efficiency in cooperative vehicular networks. [6] Aiming at the cooperative vehicular networks, this paper adopts a method based on driver's emotional state, and proposes Anomaly Detection Based on Driver's Emotional State (EAD) algorithm to realize real-time detection of data related to safe driving. [7]因此，在本文中，我们分析了分布式协作车载网络在$\alpha-\eta-K-\mu$衰落模型上的感知性能，与现有的衰落模型相比，该模型更适合车载通信。 [1] 此外，通过与非正交多路访问（NOMA）技术的合作，我们研究了GQSM在协作车辆网络中的实际应用，并提出了具有OMA的协作GQSM（C-OMA-GQSM）和具有NOMA的协作GQSM（C-NOMA）。 -GQSM) 方案。 [2] nan [3] 基于车对车 (V2V) 通信的协作车辆网络是未来更智能的交通系统的关键技术，因为它们为解决道路安全和交通拥堵提供了各种应用。 [4] nan [5] 集群和集群头车辆（CHV）协作通信都已被用于减少交通拥堵，以提高协作车辆网络中的道路交通效率。 [6] 针对协同车联网，采用基于驾驶员情绪状态的方法，提出基于驾驶员情绪状态的异常检测（EAD）算法，实现对安全驾驶相关数据的实时检测。 [7]
cooperative vehicular communication
The paper investigates the improvement of using maximum ratio combining (MRC) in cooperative vehicular communications (VCs) transmission schemes considering non-orthogonal multiple access scheme (NOMA) at intersections. [1] This paper also finds that optimized combination of message length and transmit rate ensures the optimal channel utilization for cooperative vehicular communication, which in turn improves the situational awareness of the whole system. [2] We study the performance of a cooperative vehicular communication system in a highway traffic scenario, where the locations of co-channel interfering vehicles are modeled by a one-dimensional Poisson point process (PPP). [3] In this paper, we study cooperative vehicular communications (VC) transmission schemes using non-orthogonal multiple access scheme (NOMA) at intersections, in the presence of interference, when two destination nodes are involved. [4]本文研究了在交叉口考虑非正交多址接入方案（NOMA）的协同车辆通信（VCs）传输方案中使用最大比合并（MRC）的改进。 [1] 本文还发现，消息长度和传输速率的优化组合确保了协作车辆通信的最佳信道利用率，从而提高了整个系统的态势感知能力。 [2] 我们研究了高速公路交通场景中协作车辆通信系统的性能，其中同信道干扰车辆的位置由一维泊松点过程（PPP）建模。 [3] 在本文中，我们研究了在涉及两个目标节点时，在存在干扰的情况下，在交叉路口使用非正交多址接入方案 (NOMA) 的协作车辆通信 (VC) 传输方案。 [4]
cooperative vehicular safety
Vehicular networks have tremendous potential to improve road safety, traffic efficiency, and driving comfort, where cooperative vehicular safety applications are a significant branch. [1] This may have a detrimental impact on cooperative vehicular safety applications that build on the reliable regular broadcasting of status messages by vehicles in a local neighborhood. [2]车载网络在改善道路安全、交通效率和驾驶舒适性方面具有巨大潜力，其中协作式车辆安全应用是一个重要分支。 [1] 这可能对合作车辆安全应用产生不利影响，这些应用建立在当地社区车辆可靠定期广播状态消息的基础上。 [2]