Ye Shitong, Liu Shaojiang, Wang Feng
Department of Data Science, Guangzhou Huashang College, Guangzhou 511300, China.
Guangzhou Xinhua University, Dongguan 523133, China.
Comput Intell Neurosci. 2022 Jun 27;2022:3924013. doi: 10.1155/2022/3924013. eCollection 2022.
Vehicular ad hoc network (VANET) is a key part of intelligent transportation system. VANET technology is very important for realizing vehicle-to-vehicle communication, remote control of unmanned vehicles, and early warning reception of road condition information ahead of time when external networks such as the Internet are limited. Aiming at the problems of uncertainty in vehicle mobility, uneven distribution of traffic density in road sections, and uncertainty in the road scene where the vehicle is located in VANET, a multiscenario intelligent QoS routing algorithm (MISR) for vehicle network is proposed. The algorithm analyzes a variety of vehicle network scenarios and discusses the routing methods used in scenarios with/without roadside auxiliary units and vehicle uniform acceleration limited/unrestricted, so that the vehicle network can ensure that the communication link is not interrupted as much as possible. At the same time, QoS performance criteria such as data transmission rate, bit error rate, and delay time are considered. For complex scenes with variable vehicle speeds, this paper introduces a deep reinforcement learning method to intelligently select routing nodes for vehicle networks.
车载自组织网络（VANET）是智能交通系统的关键部分。VANET技术对于实现车对车通信、无人驾驶车辆的远程控制以及在互联网等外部网络受限的情况下提前接收路况信息的预警非常重要。针对VANET中车辆移动性的不确定性、路段交通密度分布不均以及车辆所处道路场景的不确定性等问题，提出了一种用于车辆网络的多场景智能QoS路由算法（MISR）。该算法分析了多种车辆网络场景，并讨论了有无路边辅助单元以及车辆均匀加速受限/不受限的场景中所使用的路由方法，以使车辆网络能够尽可能确保通信链路不中断。同时，考虑了数据传输速率、误码率和延迟时间等QoS性能标准。对于车辆速度可变的复杂场景，本文引入了一种深度强化学习方法来为车辆网络智能选择路由节点。
