Department of Electrical Engineering, Universidad de Chile, Santiago 8370451, Chile.
Programa Institucional de Fomento a la I+D+i, Universidad Tecnológica Metropolitana, Santiago 8940577, Chile.
Sensors (Basel). 2021 Apr 23;21(9):2956. doi: 10.3390/s21092956.
The opportunistic exchange of information between vehicles can significantly contribute to reducing the occurrence of accidents and mitigating their damages. However, in urban environments, especially at intersection scenarios, obstacles such as buildings and walls block the line of sight between the transmitter and receiver, reducing the vehicular communication range and thus harming the performance of road safety applications. Furthermore, the sizes of the surrounding vehicles and weather conditions may affect the communication. This makes communications in urban V2V communication scenarios extremely difficult. Since the late notification of vehicles or incidents can lead to the loss of human lives, this paper focuses on improving urban vehicle-to-vehicle (V2V) communications at intersections by using a transmission scheme able of adapting to the surrounding environment. Therefore, we proposed a neuroevolution of augmenting topologies-based adaptive beamforming scheme to control the radiation pattern of an antenna array and thus mitigate the effects generated by shadowing in urban V2V communication at intersection scenarios. This work considered the IEEE 802.11p standard for the physical layer of the vehicular communication link. The results show that our proposal outperformed the isotropic antenna in terms of the communication range and response time, as well as other traditional machine learning approaches, such as genetic algorithms and mutation strategy-based particle swarm optimization.
车辆之间的机会性信息交换可以显著有助于减少事故的发生并减轻其损失。然而，在城市环境中，特别是在交叉路口场景中，建筑物和墙壁等障碍物会阻挡发射器和接收器之间的视线，从而降低车辆通信范围，并因此损害道路安全应用的性能。此外，周围车辆的大小和天气条件可能会影响通信。这使得城市 V2V 通信场景中的通信变得极其困难。由于车辆或事件的延迟通知可能导致人员伤亡，因此本文专注于通过使用能够适应周围环境的传输方案来改善交叉口的城市车对车 (V2V) 通信。因此，我们提出了一种基于神经进化增强拓扑结构的自适应波束形成方案，以控制天线阵列的辐射模式，从而减轻城市 V2V 通信中交叉口场景中的阴影效应。这项工作考虑了 IEEE 802.11p 标准作为车辆通信链路的物理层。结果表明，我们的方案在通信范围和响应时间方面优于各向同性天线，以及遗传算法和基于突变策略的粒子群优化等其他传统机器学习方法。
