Beijing Didi Infinity Technology and Development Co., Ltd.

Microsoft Technology Licensing, LLC

Boe Technology Group Co., Ltd.

Hefei Xinsheng Optoelectronics Technology Co., Ltd.

Beijing University of Technology

Guizhou Expressway Group Co., Ltd (cn)

Pengfei Xu

Jun Zhang

Zhiwei Ruan

Xiaoqiang Teng

Hua Chai

Kaiqiang An

Mengxi Li

Runbo Hu

Zongyue Liu

Maozong Zheng

Wei-Ying Ma

Bin Xu

Yong Rui

Feng Liu

Chao Liu

Di Li

Xiaoyu Li

Min Xu

Tingting Song

Kai Wu

Yuanfeng Zhang

Jianmin Wang

Xiaojuan Gao

Wei Shao

Kuiyuan Yang

Jiajia Shan

Jiangbi Hu

Xiaoqin Zhang

Linxi Gao

Xiang Wen

Chunyan Liu

Guangzhi Wang

Baoxun Wang

Guiping Guan

Ronghua Wang

Sicheng Zhao

Shanghang Zhang

Yang Gu

Yaxian Li

Zhichao Song

Ling Bao

Xiance Du

Huanqing Zhou

Ronghao Li

Rongzhi Wang

Jiankuan Li

Shenggang Bao

Jiamei Liu

Jianlong Cheng

Jiechao Cheng

The embodiments of the present disclosure provide a visual positioning method, the method may include obtaining a positioning image collected by an imaging device; obtaining a three-dimensional (3D) point cloud map associated with an area where the imaging device is located; determining a target area associated with the positioning image from the 3D point cloud map based on the positioning image; and determining positioning information of the imaging device based on the positioning image and the target area.

Systems and methods for visual positioning

The present disclosure is related to systems and methods for indoor positioning. The method includes obtaining a first location of a subject at a first time point. The method also includes determining one or more motion parameters associated with the subject at least based on one or more sensors carried by the subject. The method further includes determining, based on the first location of the subject and the one or more motion parameters associated with the subject, a second location of the subject at a second time point after the first time point. The method still further includes adjusting, at least based on surroundings information associated with the subject at the second time point, the second location of the subject to determine a target location of the subject.

Systems and methods for indoor positioning

Systems and methods for updating a model of a building are provided. The method includes obtaining a three-dimensional (3D) model of a building including reference environmental data associated with a plurality of reference locations of the building and obtaining current environmental data corresponding to a current location of the building. The method further includes identifying, from the 3D model, a target reference location corresponding to the current location from the plurality of reference locations of the building and comparing the current environmental data with the target reference environmental data. The method further includes sending to the user device the target reference location when the current environmental data match the target reference environmental data, and updating the 3D model with the current environmental data when the current environmental data statistically un-match the target reference environmental data.

Systems and methods for updating a 3D model of building

The present disclosure relates to systems and methods for identifying one or more target roads. The systems may perform the methods to obtain a heat map associated with a plurality of driving track points along a plurality of roads in a target region, wherein the plurality of roads includes one or more target roads and one or more reference roads; obtain a road network map associated with the one or more reference roads in the target region; produce an intermediate heat map by: eliminating pixels in the heat map corresponding to the one or more reference roads in the road network map, eliminating background pixels corresponding to background of the heat map, and thinning the heat map; and determine start coordinate information and end coordinate information associated with the one or more target roads based on the intermediate heat map according to a linear transformation.

Systems and methods for new road determination

Embodiments of the disclosure provide systems and methods for domain adaptation between a plurality of source domains and a target domain. The artificial intelligence method includes receiving labeled data from the plurality of source domains and unlabeled data from the target domain. The method further includes separately training, by a processor, a plurality of source classifiers each corresponding to a source domain using the labeled data received from the respective source domains. The method also includes selecting a subset of the labeled data received from each source domain based on a similarity between the selected labeled data and the unlabeled data of the target domain. The method additionally includes refining, by the processor, each source classifier using the selected subset of the labeled data, and predicting labels of the unlabeled data using the refined source classifiers.

Machine learning systems and methods with source-target adaptation

A cleaner and a cleaning device are disclosed. The cleaner includes: a guide rail; a lifting lever, one end of which is disposed on the guide rail and capable of moving along the guide rail; a feed pipe, disposed inside the lifting lever; and a nozzle, disposed on an end portion of one end of the lifting lever away from the guide rail and communicated with the feed pipe. The lifting lever is configured to allow the nozzle to be away from or close to the guide rail.

Cleaner and cleaning device

A method configured to implemented on at least one map processing device for determining new roads on a map includes obtaining a first road network image of a region, the first road network image including a first plurality of roads. The method also includes determining a second road network image of the region based on a map of the region, the second road network image including a second plurality of roads that are not present in the first road network image. The method further includes determining a third road network image by concatenating the first road network image and the second road network image. The method still further includes determining a fourth road network image of the region by processing the third road network with at least one convolution layer, the fourth road network including the second plurality of roads.

Systems and methods for determining new roads on a map

The present invention relates to a visual efficacy measuring method for objects in different light environments, which comprises: (a) setting a light environment; (b) measuring a relationship between color temperature and time; (c) a driver visually recognizing a target object; (d) resetting the light environment, and repeating the step (c); (e) processing the visual recognition information data collected by the experiments so as to establish a relationship of the visual recognition time in relation with the light environmental parameters of color temperature, color rendering index and brightness; (f) performing visual efficacy analysis according to the processing results of the visual recognition information data. The present invention also provides a color temperature standard determining method for a tunnel middle section in day time, which comprises: (a) setting a light environment; (b) measuring a relationship between color temperature and time; (c) selecting a stably distributed color temperature segment; (d) determining the color temperature standard. The method of the present invention performs visual efficacy analysis of the influence that light environment settings of illumination on a night road or in a tunnel middle section have on drivers' visual recognition activity, thereby providing a good guiding standard for reasonable and efficient setting of color temperature, color rendering index and average brightness of an illumination device.

Visual efficacy measuring method for objects in different light environments

An image chat application generates comments to images based on features of the images. In one example, the image chat application searches through a repository of stored image-comment pairs to identify a stored image that is similar to the image, and generates a comment to the image based on an identified stored image-comment pair. In another example, the image chat application may identify and tag particular objects that dominate an image, and may generate a comment to the image based on characteristics of those particular objects. In this second example, the image chat application further generates a comment to the image based on comments previously associated with the identified tag.

Identifying image comments from similar images

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Beijing, China

Pengfei Xu