Abstract: A display panel, including an active area and a peripheral area, which is located outside of the active area, wherein the active area comprises a base substrate, and a display structure layer and a touch structure layer sequentially arranged on the base substrate; the peripheral area includes an isolation dam, a first ground trace and a second ground trace arranged on the base substrate; and the first ground trace is located at a side of the isolation dam close to the active area, and the second ground trace is located at a side of the isolation dam away from the active area.

Abstract: Systems and methods for detecting blockages in images are described. An example method may include receiving a plurality of images captured by a camera installed on an apparatus. The method may include identifying one or more candidate blocked regions in the plurality of images. Each of the candidate blocked regions may contain image data caused by blockages in the camera's field-of-view. The method may further include assigning scores to the one or more candidate blocked regions based on relationships among the one or more candidate blocked regions in the plurality of images. In response to a determination that one of the scores is above a predetermined blockage threshold, the method may include generating an alarm signal for the apparatus.

Abstract: Methods and systems for jointly estimating a pose and a shape of an object perceived by an autonomous vehicle are described. The system includes data and program code collectively defining a neural network which has been trained to jointly estimate a pose and a shape of a plurality of objects from incomplete point cloud data. The neural network includes a trained shared encoder neural network, a trained pose decoder neural network, and a trained shape decoder neural network. The method includes receiving an incomplete point cloud representation of an object, inputting the point cloud data into the trained shared encoder, outputting a code representative of the point cloud data. The method also includes generating an estimated pose and shape of the object based on the code. The pose includes at least a heading or a translation and the shape includes a denser point cloud representation of the object.

Abstract: Systems and methods for object detection. The methods include, by a computing device: obtaining a plurality of intensity values denoting at least a difference in a first location of at least one object in a first image and a second location of the at least one object in a second image; converting the intensity values to 3D position values; inputting the 3D position values into a classifier algorithm to obtain classifications for data points of a 3D point cloud (each of the classifications comprising a foreground classification or a background classification); and using the classifications to detect at least one object which is located in a foreground or a background.

Abstract: Methods and systems for jointly estimating a pose and a shape of an object perceived by an autonomous vehicle are described. The system includes data and program code collectively defining a neural network which has been trained to jointly estimate a pose and a shape of a plurality of objects from incomplete point cloud data. The neural network includes a trained shared encoder neural network, a trained pose decoder neural network, and a trained shape decoder neural network. The method includes receiving an incomplete point cloud representation of an object, inputting the point cloud data into the trained shared encoder, outputting a code representative of the point cloud data. The method also includes generating an estimated pose and shape of the object based on the code. The pose includes at least a heading or a translation and the shape includes a denser point cloud representation of the object.

Abstract: A system detects multiple instances of an object in a digital image by receiving a two-dimensional (2D) image that includes a plurality of instances of an object in an environment. For example, the system may receive the 2D image from a camera or other sensing modality of an autonomous vehicle (AV). The system uses a first object detection network to generate a plurality of predicted object instances in the image. The system then receives a data set that comprises depth information corresponding to the plurality of instances of the object in the environment. The data set may be received, for example, from a stereo camera of an AV, and the depth information may be in the form of a disparity map. The system may use the depth information to identify an individual instance from the plurality of predicted object instances in the image.

Abstract: A method and a system for generating a mesh representation of a surface. The method includes receiving a three-dimensional (3D) point cloud representing the surface, generating a reconstruction dataset having a higher resolution than the 3D point cloud in one or more regions corresponding to the surface from the 3D point cloud, and generate a polygon mesh representation of the surface by using a fine-to-coarse hash map for building polygons at a highest resolution first followed by progressively coarser resolution polygons, using the reconstruction dataset.

Abstract: Systems and methods for object detection. The methods comprise, by a computing device: obtaining a plurality of intensity values denoting at least a difference in a first location of at least one object in a first image and a second location of the at least one object in a second image; converting the intensity values to 3D position values; inputting the 3D position values into a classifier algorithm to obtain classifications for data points of a 3D point cloud (each of the classifications comprising a foreground classification or a background classification); and using the classifications to detect at least one object which is located in a foreground or a background.

Abstract: Aspects of the present disclosure involve a vehicle computer system comprising a computer-readable storage medium storing a set of instructions, and a method for online light detection and ranging (Lidar) intensity normalization. Consistent with some embodiments, the method may include accumulating point data output by a channel of a Lidar unit during operation of an autonomous or semi-autonomous vehicle. The accumulated point data includes raw intensity values that correspond to a particular surface type. The method further includes calculating a median intensity value based on the raw intensity values and generating an intensity normalization multiplier for the channel based on the median intensity value. The intensity normalization multiplier, when applied to the median intensity value, results in a reflectivity value that corresponds to the particular surface type.

Abstract: Systems and methods for object detection. The methods comprise, by a computing device: obtaining a plurality of intensity values denoting at least a difference in a first location of at least one object in a first image and a second location of the at least one object in a second image; converting the intensity values to 3D position values; inputting the 3D position values into a classifier algorithm to obtain classifications for data points of a 3D point cloud (each of the classifications comprising a foreground classification or a background classification); and using the classifications to detect at least one object which is located in a foreground or a background.

Abstract: A flexible stress sensing device is provided, which includes a flexible cloth substrate, a flexible stress sensor and textile knots configured to fix the flexible stress sensor on the flexible cloth substrate. The flexible stress sensor includes two conductive fiber bundles, wherein each of the conductive fiber bundles is provided with a loose structure, and the loose structures of two conductive fiber bundles contact with each other and form a stress sensing unit. The flexible stress sensing device can be washable, is not easy to fall off, and can resist against motion interference, and has other advantages of high resolution, high sensitivity and a compatibility with the prior textile techniques.

Abstract: Systems and methods for operating a vehicle. The methods comprise, by a processor: obtaining a pair of stereo images captured by a stereo camera; processing the pair of stereo images to generate a disparity map comprising a plurality of pixels defined by intensity values; converting each intensity value to a 3D position in a map (each 3D position defining a location of a data point in a point cloud); performing a hierarchical decision tree classification to determine a classification for each data point in the point cloud (the classification being a foreground classification or a background classification); and using the classifications to facilitate autonomous control of the vehicle.

Abstract: Aspects of the present disclosure involve a vehicle computer system comprising a computer-readable storage medium storing a set of instructions, and a method for online light detection and ranging (Lidar) intensity normalization. Consistent with some embodiments, the method may include accumulating point data output by a channel of a Lidar unit during operation of an autonomous or semi-autonomous vehicle. The accumulated point data includes raw intensity values that correspond to a particular surface type. The method further includes calculating a median intensity value based on the raw intensity values and generating an intensity normalization multiplier for the channel based on the median intensity value. The intensity normalization multiplier, when applied to the median intensity value, results in a reflectivity value that corresponds to the particular surface type.

Abstract: Systems and methods for operating a vehicle. The methods comprise, by a processor: obtaining a pair of stereo images captured by a stereo camera; processing the pair of stereo images to generate a disparity map comprising a plurality of pixels defined by intensity values; converting each intensity value to a 3D position in a map (each 3D position defining a location of a data point in a point cloud); performing a hierarchical decision tree classification to determine a classification for each data point in the point cloud (the classification being a foreground classification or a background classification); and using the classifications to facilitate autonomous control of the vehicle.

Abstract: Provided is a flexible temperature sensor including a flexible temperature-sensing fabric, wherein the flexible temperature-sensing fabric includes a fabric base and at least one flexible temperature-sensing conductive fiber, the fabric base is a flat fabric woven from a plurality of insulating fibers, and the temperature-sensing conductive fiber is fixed in the fabric base by weaving. The flexible temperature sensor has the advantages of being easy to fabricate, low in cost, washable, wide in temperature monitoring range, high in sensitivity, good in stability and repeatability, and compatible with existing textile technologies.

Abstract: A new tactile sensor having a sensing layer, and the sensing layer includes a flexible substrate, at least one stress sensing unit, and at least one temperature sensing unit. The stress sensing unit includes a flexible conductive fiber, a top electrode, and a bottom electrode, the flexible conductive fiber is laid flat on the flexible substrate, the top electrode is bendable, and when there is a potential difference between the top electrode and the bottom electrode, a current flows along a radial direction of the flexible conductive fiber; and the temperature sensing unit includes a thermal conductive fiber and two end electrodes, the thermal conductive fiber is laid flat on the flexible substrate, and when there is a potential difference between the two end electrodes, a current flows along an axial direction of the thermal conductive fiber.

Abstract: Methods, apparatuses, and client terminals for displaying user specified information of a data item. The method includes determining a user operation associated with a target data item on a data item display page; obtaining item information of the target data item from at least one data source when an instruction satisfying a preset trigger condition; determining user specified information of the target data item based on the item information; generating an information interface based on the user specified information; and displaying the information interface on the data item display page. The methods, apparatus, and client terminals, provided in example embodiments of the present application can lower the cost of decision making based on information noise reduction, optimize user data item information interactive experience, and improve user data item purchasing experience.

Abstract: A method and a system for generating a mesh representation of a surface. The method includes receiving a three-dimensional (3D) point cloud representing the surface, generating a reconstruction dataset having a higher resolution than the 3D point cloud in one or more regions corresponding to the surface from the 3D point cloud, and generate a polygon mesh representation of the surface by using a fine-to-coarse hash map for building polygons at a highest resolution first followed by progressively coarser resolution polygons, using the reconstruction dataset.

Abstract: A system detects multiple instances of an object in a digital image by receiving a two-dimensional (2D) image that includes a plurality of instances of an object in an environment. For example, the system may receive the 2D image from a camera or other sensing modality of an autonomous vehicle (AV). The system uses a first object detection network to generate a plurality of predicted object instances in the image. The system then receives a data set that comprises depth information corresponding to the plurality of instances of the object in the environment. The data set may be received, for example, from a stereo camera of an AV, and the depth information may be in the form of a disparity map. The system may use the depth information to identify an individual instance from the plurality of predicted object instances in the image.

Abstract: A method and a system for generating a mesh representation of a surface. The method includes receiving a three-dimensional (3D) point cloud representing the surface, identifying and discarding one or more outliers in the 3D point cloud to generate a filtered point cloud using a Gaussian process, adding one or more additional points to the filtered point cloud to generate a reconstruction dataset, and using Poisson surface reconstruction to generate an implicit surface corresponding to the surface from the reconstruction dataset.