Abstract: The present invention discloses a flame-retardant, thermal-insulating, and fireproof material for batteries, and relates to a silicone rubber flame-retardant and thermal-insulating material, which comprises a flame-retardant and thermal-insulating layer and an impact-resistant layer, wherein the flame-retardant and thermal-insulating layer comprises organic silicone rubber, a ceramic-forming filler, a flame retardant, an auxiliary agent, and glass powders with different initial melting temperatures, and molten-state temperature ranges of the glass powders cover 300° C. to 1500° C. Various glass powders with different molten-state temperature ranges are added to the material, so that when being burned to form a ceramic layer in the temperature range of 300° C. to 1500° C., the fireproof material always has glass powders in a molten state.

Abstract: The present disclosure provides devices and methods for repairing dice during micro-LED display fabrication. The devices include a backplane. The backplane has a plurality of backplane electrodes. Each backplane electrode includes a first material. A plurality of micro-LEDs having a plurality of micro-LED electrodes is included in the device. Each micro-LED electrode includes a second material. Each micro-LED electrode is bonded to each backplane electrode with an alloy of the first material and the second material therebetween. At least one backplane electrode is bonded to the micro-LED electrode via a repair material. The device includes a plurality of subpixel isolation (SI) structures formed over the backplane. The SI structures define wells of sub-pixels. Each well includes a respective micro-LED between adjacent SI structures. The sub-pixels have a color conversion material disposed in the wells.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing knowledge distillation for autonomous vehicles. One of the methods includes obtaining sensor data characterizing an environment, wherein the sensor data has been captured by one or more sensors on-board a vehicle in the environment; processing, for each of one or more surrounding agents in the environment, a network input generated from the sensor data using a neural network to generate an agent discomfort prediction that characterizes a level of discomfort of the agent; combining the one or more agent discomfort predictions to generate an aggregated discomfort score; and providing the aggregated discomfort score to a path planning system of the vehicle in order to generate a future path of the vehicle.

Abstract: Aspects of the disclosure provide for controlling a vehicle in an autonomous driving mode. For instance, sensor data for an object as well as a plurality of predicted trajectories may be received. Each predicted trajectory may represent a plurality of possible future locations for the object. A grid including a plurality of cells, each being associated with a geographic area, may be generated. Probabilities that the object will enter the geographic area associated with each of the plurality of cells over a period of time into the future may be determined based on the sensor data in order to generate a heat map. One or more of the plurality of predicted trajectories may be compared to the heat map. The vehicle may be controlled in the autonomous driving mode based on the comparison.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining a task schedule for generating prediction data for different agents. In one aspect, a method comprises receiving data that characterizes an environment in a vicinity of a vehicle at a current time step, the environment comprising a plurality of agents; receiving data that identifies high-priority agents for which respective data characterizing the agents must be generated at the current time step; identifying available computing resources at the current time step; processing the data that characterizes the environment using a complexity scoring model to determine a respective complexity score for each of the high-priority agents; and determining a schedule for the current time step that allocates the generation of the data characterizing the high-priority agents across the available computing resources based on the complexity scores.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that obtain scene features in an environment that includes an autonomous vehicle, a first target agent, and a second target agent, and determines whether the first target agent is an emergency vehicle that is active at a current time point. In response to determining that the first target agent is an emergency vehicle that is active at the current time point, an input is generated from the scene features. The input can characterize the scene and indicate that the first target agent is an emergency vehicle that is active at the current time point. Also in response, the input can be processed using a machine learning model that is configured to generate a trajectory prediction output for the second target agent that characterizes predicted future behavior of the second target agent after the current time point.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting near-curb driving behavior. One of the methods includes obtaining agent trajectory data for an agent in an environment, the agent trajectory data comprising a current location and current values for a predetermined set of motion parameters of the agent; processing a model input generated from the agent trajectory data using a trained machine learning model to generate a model output comprising a prediction of whether the agent will exhibit near-curb driving behavior within a predetermined timeframe, wherein an agent exhibits near-curb driving behavior when the agent operates within a particular distance of an edge of a road in the environment; and using the prediction to generate a planned path for a vehicle in the environment.

Abstract: A method, apparatus, and a non-transitory computer-readable medium storing program code may be provided. The method may include obtaining a first scene space unit in which a dynamic scene element in a scene space is located at a first moment and obtaining a first photographing space unit in which a virtual camera is located in a photographing space at the first moment. The method may also include determining a visibility relationship between the first scene space unit and the first photographing space unit based on prestored visibility data. When the visibility relationship between the first scene space unit and the first photographing space unit is invisible, the method includes removing the dynamic scene element and obtaining remaining dynamic scene elements in the scene space. The method then renders the scene picture in the first moment.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining a task schedule for generating prediction data for different agents. In one aspect, a method comprises receiving data that characterizes an environment in a vicinity of a vehicle at a current time step, the environment comprising a plurality of agents; receiving data that identifies high-priority agents for which respective data characterizing the agents must be generated at the current time step; identifying available computing resources at the current time step; processing the data that characterizes the environment using a complexity scoring model to determine a respective complexity score for each of the high-priority agents; and determining a schedule for the current time step that allocates the generation of the data characterizing the high-priority agents across the available computing resources based on the complexity scores.

Abstract: Compounds and compositions are provided that can inhibit microsomal prostaglandin E synthase-1 (mPGES-1). The compounds and compositions can reduce inflammation in a subject, such as inflammation caused by an inflammation disorder or symptoms thereof. Pharmaceutical compositions comprising the compound are also provided. Furthermore, methods are provided for reducing inflammation and/or inhibiting mPGES-1. The methods can comprise administering an effective amount of the composition to a subject.

Abstract: Micro-LED structures include an LED epilayer that may be formed before the micro-LED structure is coupled to a backplane substrate. In order to prevent light leakage and maximize light output, the sidewalls and other surfaces of the LED epilayer may be coated with a reflective coating. For example, the reflective coating may include a metal layer that is electrically insulated between dielectric layers from the micro-LED electrodes. The reflective coating may also be formed using multiple layers in a distributed Bragg reflector configuration. This reflective coating may be formed during the LED fabrication process before the micro-LED structure is coupled to the backplane. The pixel isolation structures on the backplane may also include a reflective coating that is applied above the LED epilayers.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying high-priority agents in the vicinity of a vehicle. The high-priority agents can be identified based on a set of mutual importance scores in which each mutual importance score indicates an estimated mutual relevance between the vehicle and a different agent from a set of agents on planning decisions of the other. The mutual importance scores can be calculated based on importance scores assessed from the perspectives of both the vehicle and the agents.

Abstract: To operate an autonomous vehicle, a rail agent is detected in a vicinity of the autonomous vehicle using a detection system. One or more tracks are determined on which the detected rail agent is possibly traveling, and possible paths for the rail agent are predicted based on the determined one or more tracks. One or more motion paths are determined for one or more probable paths from the possible paths, and a likelihood for each of the one or more probable paths is determined based on each motion plan. A path for the autonomous vehicle is then determined based on a most probable path associated with a highest likelihood for the rail agent, and the autonomous vehicle is operated using the determined path.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that obtain scene features in an environment that includes an autonomous vehicle and a target agent at a current time point; determine whether the target agent is an emergency vehicle that is active at the current time point; generate from the scene features, an input (i) that includes the scene features and (ii) that indicates whether the target vehicle is an emergency vehicle that is active at the current time point; and process the input using a machine learning model that is configured to generate a behavior prediction output for the target agent that characterizes predicted future behavior of the target agent after the current time point.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining occupancies of surrounding agents. One of the methods includes obtaining scene data characterizing an environment at a current time point; processing a first network input generated from the scene data using a first neural network to generate an intermediate output; obtaining an identification of a future time point that is after the current time point; and generating, from the intermediate output and the future time point, an occupancy output, wherein the occupancy output comprises respective occupancy probabilities for each of a plurality of locations in the environment, wherein the respective occupancy probability for each location characterizes a likelihood that one or more agents will occupy the location at the future time point.

Abstract: Processing methods are described that include forming a group of LED structures on a substrate layer to form a patterned LED substrate. The methods also include depositing a light absorption material on the pattered LED substrate, where the light absorption material includes at least one photocurable compound and at least one ultraviolet light absorbing material. The methods further include exposing a portion of the light absorption material to patterned light, wherein the patterned light cures the exposed portion of the light absorption material into pixel isolation structures. The methods additionally include depositing an isotropic layer on a top portion and a side portion of the pixel isolation structures, where the LED structures are substantially free of the as-deposited isotropic light reflecting layer.

Abstract: The technology relates to controlling a vehicle in an autonomous driving mode in accordance with behavior predictions for other road users in the vehicle's vicinity. In particular, the vehicle's onboard computing system may predict whether another road user will perform a “continuing” lane driving operation, such as going straight in a turn-only lane. Sensor data from detected/observed objects in the vehicle's nearby environment may be evaluated in view of one or more possible behaviors for different types of objects. In addition, roadway features, in particular whether lane segments are connected in a roadgraph, are also evaluated to determine probabilities of whether other road users may make an improper continuing lane driving operation. This is used to generate more accurate behavior predictions, which the vehicle can use to take alternative (e.g., corrective) driving actions.

Abstract: The present invention discloses a two-step anti-fraud vehicle insurance image collecting and quality testing method, system and device, the method comprises: step 1, collecting vehicle insurance scene images and marking vehicle orientation; step 2, performing object detection on the collected vehicle insurance scene images and screening to obtain object coordinates; step 3, according to the vehicle orientation and the object coordinates, obtaining the specific position of the object coordinates located in the whole vehicle; step 4, according to the object coordinates screened in step 2, performing vehicle component detection on the vehicle insurance scene images, obtaining the component coordinates of the vehicle components, and screening to obtain the vehicle component closest to the object coordinates; step 5, according to the specific position of the object coordinates located in the whole vehicle and the vehicle components closest to the object coordinates, obtaining the position of the vehicle components

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating candidate future trajectories for agents. One of the methods includes obtaining scene data characterizing a scene in an environment at a current time point; for each of a plurality of lane segments, processing a model input comprising (i) features of the lane segment and (ii) features of the target agent using a machine learning model that is configured to process the model input to generate a respective score for the lane segment that represents a likelihood that the lane segment will be a first lane segment traversed by the target agent after the current time point; selecting, as a set of seed lane segments, a proper subset of the plurality of lane segments based on the respective scores; and generating a plurality of candidate future trajectories for the target agent.

Abstract: Provided is a two-stage self-organizing optimized aggregation method and system for distributed resources of a virtual power plant.