基于北斗和边缘计算的车联网导航技术研究

随着车辆保有量的不断增长和车联网应用的普及，车辆终端会产生大量需要实时处理的数据消息。在车辆高速移动场景下，传统的车联网导航系统由于车辆差分定位数据存在传输时延，导致车辆定位结果存在一定的偏差，无法及时获得高精度定位结果。基于此，文中提出了一种基于北斗定位和边缘计算的车联网导航技术方案，采用改进的遗传算法进行终端定位请求的资源分配，有效降低整个边缘网络的服务时延，并利用基于边缘节点的优化无损卡尔曼滤波算法来提高车联网节点的定位精度。实验表明，文中所提出的方法能够为大规模车联网终端提供实时精准、低延迟和高精度的定位服务，具有较高的实际应用价值。

Research on Internet of Vehicles Navigation Technology Based on BDS and Edge Computing

With the continuous growth of vehicle ownership and the popularization of internet of vehicles applications, vehicle terminals generate large amounts of data messages that need to be processed in real time. In the scene of high-speed vehicle movement, the traditional internet of vehicles navigation system has a transmission delay due to vehicle differential positioning data, which results in a certain deviation in vehicle positioning results and cannot obtain high-precision positioning results in time. Based on this, this study proposes a technology solution for car networking navigation based on BDS positioning and edge computing. An improved genetic algorithm is used to allocate resources for terminal positioning requests, which effectively reduces the service delay of the entire edge network. The optimized unscented Kalman filter algorithm based on edge node is used to improve the positioning accuracy of the car network node. Experiments results show that the method proposed can provide real-time accurate, low-latency and high-precision positioning services for large-scale internet of vehicles terminals, and has high practical application value.