The invention relates to a multi-dimensional cooperative vehicle lane changing decision-making method based on a user portrait, and belongs to the technical field of mobile communication. In an automatic driving technology, a traditional method of a neural network in vehicle lane changing decision mainly depends on single vehicle data, so that the accuracy and the real-time performance are poor. In order to solve the problem, firstly, a vehicle generates a driving characteristic graph and a vehicle trajectory flow graph based on sensor data, and shares the driving characteristic graph and the vehicle trajectory flow graph to surrounding vehicles and a road side unit to realize data collaboration; secondly, the vehicle downloads the initial decision model from the road side unit, and carries out lane changing decision by combining data of the vehicle and surrounding vehicles; then, based on the data and traffic factors, the road side unit uses a deep neural network to update a model and re-issues the model to the vehicle, thereby ensuring real-time performance and safety of decision making. According to the method, the user portrait is generated by integrating the historical driving characteristics and the vehicle trajectory prediction, and then the deep neural network is applied, so that more accurate lane changing decision is realized, and the safety and the operation efficiency of a traffic system are improved.
本发明涉及一种基于用户画像的多维协同车辆变道决策方法,属于移动通信技术领域。在自动驾驶技术中,神经网络在车辆变道决策中的传统方法主要依赖单车数据,导致准确性和实时性差。为解决上述问题,首先,车辆基于传感器数据生成驾驶特征图和车辆轨迹流图,并共享给周围车辆和路侧单元,实现数据协同;其次,车辆从路侧单元下载初始决策模型,结合自身及周围车辆数据进行变道决策;然后,路侧单元基于这些数据及交通因素,使用深度神经网络更新模型并重新下发至车辆,确保决策的实时性和安全性。本发明通过整合历史驾驶特征和车辆轨迹预测生成用户画像,再应用深度神经网络,实现更精准的变道决策,提升交通系统的安全性和运行效率。
Multi-dimensional cooperative vehicle lane changing decision-making method based on user portraits
一种基于用户画像的多维协同车辆变道决策方法
2024-12-10
Patent
Electronic Resource
Chinese
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
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