The invention relates to an overtaking prejudgment method based on a vehicle-mounted unit and a road side unit and a storage medium. The method comprises the steps that S1, the overtaking willingnessof a vehicle owner is obtained; S2, a vehicle-mounted unit is started to acquire overtaking prejudgment information in a set range by taking the vehicle as a center, wherein the overtaking prejudgmentinformation comprises speeds and positions of vehicles and other obstacles in the set range; S3, the vehicle-mounted unit is started to communicate with an adjacent road side unit to obtain road condition information and speed limiting information of the current road; S4, the overtaking prejudgment information, the road condition information and the speed limiting information serve as input quantities and are sent to a pre-trained neural network model, and an overtaking prejudgment result output by the neural network model is obtained; and S5, on the basis of the overtaking prejudgment resultand the overtaking willingness, the vehicle-mounted unit is controlled to implement overtaking behaviors. According to the method, the vehicle safety guarantee coefficient and the vehicle owner experience degree during overtaking behavior execution in the automatic driving process can be improved.
本发明涉及一种基于车载单元和路侧单元的预判超车方法、存储介质,其中方法包括:步骤S1.获取车主的超车意愿;步骤S2.启动车载单元以本车为中心采集设定范围内的超车预判信息,所述超车预判信息包括所述设定范围内的各车辆及其他障碍物的速度与位置;步骤S3.启动车载单元向邻近的路侧单元通讯获取当前道路的路况信息及限速信息;步骤S4.将超车预判信息、路况信息及限速信息作为输入量送入事先训练好的神经网络模型,并获取所述神经网络模型输出超车预判结果;步骤S5.基于所述超车预判结果及所述超车意愿控制车载单元实施超车行为。本发明的方法能提升自动驾驶过程中执行超车行为时的车辆安全保障系数及车主体验度。
Overtaking prejudgment method based on vehicle-mounted unit and road side unit and storage medium
基于车载单元和路侧单元的预判超车方法、存储介质
2020-08-04
Patent
Electronic Resource
Chinese
IPC: | B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen |
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