The invention discloses a 3D traffic scene, vehicle and unmanned aerial vehicle information real-time interaction method, which adopts a weighted road network algorithm to generate a traffic road network, carries out real-time semi-automatic extraction and identification, utilizes an optimized sparse region convolutional neural network algorithm to identify 3D scene signs and collect data, and then realizes the real-time interaction of 3D traffic scene, vehicle and unmanned aerial vehicle information through cooperation and mutual assistance of multiple unmanned aerial vehicles. Information in the scene is obtained in all directions and transmitted to the vehicle, and real-time information interaction among the 3D traffic scene, the vehicle and the unmanned aerial vehicle is achieved. The method has a relatively complete research entry point in the aspect of realizing real-time information interaction among the 3D traffic scene, the vehicle and the unmanned aerial vehicle, and provides systematic theoretical reference and guidance for finally realizing real-time information interaction among the 3D traffic scene, the vehicle and the unmanned aerial vehicle.
本发明公开了一种3D交通场景、车辆和无人机的信息实时交互方法,采用加权路网算法生成交通路网,进行实时半自动提取与识别,利用优化的稀疏区域卷积神经网络算法,识别3D场景标志并采集数据,再通过多无人机协同配合,彼此辅助,全方位获取场景内的信息并传递给车辆,实现3D交通场景、车辆和无人机之间信息实时交互。本发明在实现3D交通场景、车辆和无人机之间信息实时交互方面具有较为完整的研究切入点,为最终实现3D交通场景、车辆和无人机之间信息实时交互提供系统的理论借鉴和指导。
Information real-time interaction method for 3D traffic scene, vehicle and unmanned aerial vehicle
一种3D交通场景、车辆和无人机的信息实时交互方法
2024-07-05
Patent
Electronic Resource
Chinese
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