The invention relates to the technical field of intelligent high-speed vehicle passing control, and provides a method for predicting a vehicle driving track under highway passing control based on digital twin tracking simulation, and the method comprises the steps: collecting highway digital twin system, target vehicle and environment data in real time, and carrying out the integration to form an input data set; a future driving track of a vehicle is predicted by using a pre-trained LSTM deep neural network model, and an actual track is collected through devices such as a GPS and the like for verification. And when the prediction deviation exceeds the preset range, the model is dynamically adjusted to improve the accuracy. Prediction results and deviation analysis are transmitted to a background control terminal and a driver mobile terminal in real time, and traffic control, accident early warning and driving aid decision making such as front road condition prediction and suggested driving speed providing are supported. The highway traffic efficiency and safety are effectively improved, the driver experience is enhanced, the development of an intelligent traffic system is promoted, the operation cost is reduced, and remarkable social and economic benefits are achieved.
本发明涉及智慧高速车辆通行管控技术领域,本发明提供的一种基于数字孪生跟踪模拟的高速公路通行管控下车辆行驶轨迹预测方法,通过实时收集高速公路数字孪生系统、目标车辆及环境数据,整合形成输入数据集;利用预先训练的LSTM深度神经网络模型预测车辆未来行驶轨迹,并通过GPS等设备采集实际轨迹进行验证。当预测偏差超出预设范围时,动态调整模型以提高准确性。预测结果及偏差分析实时传输至后台控制终端和驾驶员移动终端,支持交通管控、事故预警及驾驶辅助决策,如前方路况预测和建议行驶速度提供。本发明有效提升了高速公路通行效率与安全性,增强了驾驶员体验,促进了智能交通系统发展,并降低了运营成本,具有显著的社会和经济效益。
Vehicle driving track prediction method under highway traffic control based on digital twin tracking simulation
一种基于数字孪生跟踪模拟的高速公路通行管控下车辆行驶轨迹预测方法
2025-03-14
Patent
Electronic Resource
Chinese
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen |
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