Beijing Baidu Netcom Science and Technology Co., Ltd.

Volkswagen Ag

Chinese Academy of Sciences

Apollo Intelligent Driving Technology (beijing) Co., Ltd.

Beijing Ten Dimensions Technology Co., Ltd.

Hang Gao

Shiyu Song

Guowei Wan

Shenhua Hou

Wendong Ding

Wei Sun

Xin Zhang

Ning Duan

Yuqing Li

Chao Guo

Zhen Shen

Yi Xie

Li Wan

Feiyue Wang

Gang Xiong

Weidong Liu

Xiuqin Shang

Zhengpeng Wu

Meihua Zhao

Xisong Dong

The present invention relates to method and apparatus for recommending a charging station, the method includes: determining, based on personal preference information of a user of an electric vehicle, a specific area reachable by the electric vehicle and suitable for charging the electric vehicle, when receiving a request for searching for a charging station for the electric vehicle; recommending a suitable charging station located in the specific area to the electric vehicle for charging. A suitable charging station may be recommended to the electric vehicle for charging with the method and apparatus.

Method and apparatus for recommending a charging station

The present disclosure provides a method, an apparatus, a computer device and a computer-readable storage medium for positioning, and relates to the field of autonomous driving. The method obtains point cloud data collected by a LiDAR on a device at a current time; determines, based on the point cloud data and a global map built in a global coordinate system, global positioning information of the device in the global coordinate system at the current time; and determine, based on the point cloud data and a local map built in a local coordinate system, local positioning information of the device in the local coordinate system at the current time. A positioning result of the device at the current time is determined based on at least the global positioning information and the local positioning information. Techniques of the present disclosure can provide an effective and stable positioning service.

Method, apparatus, computing device and computer-readable storage medium for positioning

The present disclosure provides a method, an apparatus, a computer device and a computer-readable storage medium for positioning, and relates to the field of autonomous driving. The method obtains inertial measurement data of a device to be positioned at a current time and point cloud data collected by a LiDAR on the device at the current time; determines, by integrating the inertial measurement data, inertial positioning information of the device in an inertial coordinate system at the current time; and determines, based on the inertial positioning information, the point cloud data and at least one local map built in a local coordinate system, a positioning result of the device in the local coordinate system at the current time. Techniques of the present disclosure can provide an effective and stable local positioning result.

The present disclosure provides a method, an apparatus, a computing device and a computer readable storage medium for detecting an environmental change, and relates to the field of autonomous driving. The method obtains a global map for an area and a first local map built in real time for a sub-area in the area; and determines an environmental change in the first sub-area by comparing the first local map and the global map and determining a first probability of the environmental change. Techniques of the present disclosure can automatically detect environmental changes that affect the positioning of autonomous driving, thereby facilitating the updating of positioning maps.

Method, apparatus, device and medium for detecting environmental change

A neural network-based error compensation method for 3D printing includes: compensating an input model by a deformation network/inverse deformation network constructed and trained according to a 3D printing deformation function/inverse deformation function, and performing the 3D printing based on the compensated model. Training samples of the deformation network/inverse deformation network include to-be-printed model samples and printed model samples. The deformation network constructed according to the 3D printing deformation function is marked as a first network. During training of the first network, the to-be-printed model samples are used as real input models, and the printed model samples are used as real output models. The inverse deformation network constructed according to the 3D printing inverse deformation function is marked as a second network. During training of the second network, the printed model samples are used as real input models, and the to-be-printed model samples are used as real output models.

Neural network-based error compensation method, system and device for 3D printing

Growing community of inventors

Beijing, China

Hang Gao