The invention provides a traffic situation prediction method based on an LSTM (Long Short Term Memory) model, which is applied to an automatic driving vehicle and comprises the following steps: step 1, regularly acquiring open map data and processing the open map data; 2, calculating the travel speed of each road section according to the map data; 3, training a traffic situation algorithm model through an LSTM model according to the map data and the travel speed; and 4, predicting the traffic situation in a future time period through the traffic situation algorithm model and the historical data set, so that the vehicle can perform global path planning. The method can help the autonomous vehicle to effectively avoid the congested road section, and improves the customer satisfaction.
本发明提供一种基于LSTM模型的交通态势预测方法,应用于自动驾驶车辆上,所述方法包括:步骤1:定时获取开放地图数据并进行处理;步骤2:根据地图数据计算各个路段的行程速度;步骤3:根据地图数据和行程速度,通过LSTM模型训练交通态势算法模型;步骤4:通过交通态势算法模型和历史数据集,预测未来时间段的交通态势,以供车辆进行全局路径规划。该方法能够帮助自动驾驶车辆有效避开拥堵路段,提升客户满意度。
Traffic situation prediction method based on LSTM model
一种基于LSTM模型的交通态势预测方法
2024-03-29
Patent
Electronic Resource
Chinese
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