公开(公告)号：US20180364054A1

公开(公告)日：2018-12-20

申请号：US16008639

申请日：2018-06-14

Applicant: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD.

Inventor： Mengting CHEN ,  Huasheng LIANG ,  Fan WANG ,  Bo ZHOU

IPC: G01C21/34 ,  G01C21/36 ,  G06N3/02

CPC classification number: G01C21/343 ,  G01C21/3476 ,  G01C21/3492 ,  G01C21/3605 ,  G06N3/006 ,  G06N3/02 ,  G06N3/0454 ,  G06N3/08 ,  G06N5/022 ,  G06Q10/06

Abstract: The present disclosure provides a method and apparatus for building an itinerary-planning model and planning a traveling itinerary, wherein the method for building the itinerary-planning model comprises: obtaining a travel route with a known travel demand; training a deep learning model by regarding the travel demand, a set of candidate scenic spots determined by using the travel demand and the travel route corresponding to the travel demand as training samples, to obtain the itinerary-planning model; the itinerary-planning model is configured to use the travel demand to obtain a corresponding travel route. The method of planning a travelling itinerary comprises: obtaining the user's travel demand; according to the user's travel demand, obtaining a set of candidate scenic spots corresponding to the travel demand; inputting the user's travel demand and the set of candidate scenic spots into an itinerary-planning model, to obtain a travel route obtained by the itinerary-planning model.

公开(公告)号：US20190004518A1

公开(公告)日：2019-01-03

申请号：US16020340

申请日：2018-06-27

Applicant: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD.

Inventor： Bo ZHOU ,  Fan WANG

IPC: G05D1/00 ,  G06N3/08 ,  G05D1/10 ,  B64C39/02

Abstract: The present disclosure provides a method and system for training an unmanned aerial vehicle control model based on artificial intelligence. The method comprises: obtaining training data by using sensor data and target state information of the unmanned aerial vehicle and state information of the unmanned aerial vehicle under action of control information output by a deep neural network; training the deep neural network with the training data to obtain an unmanned aerial vehicle control model, the unmanned aerial vehicle control model being used to obtain the control information of the unmanned aerial vehicle according to the senor data and target state information of the unmanned aerial vehicle.