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: Jaywalking behaviors of vulnerable road users (VRUs) such as cyclists or pedestrians can be predicted. Location data is obtained that identifies a location of a VRU within a vicinity of a vehicle. Environmental data is obtained that describes an environment of the VRU, where the environmental data identifies a set of environmental features in the environment of the VRU. The system can determine a nominal heading of the VRU, and generate a set of predictive inputs that indicate, for each of at least a subset of the set of environmental features, a physical relationship between the VRU and the environmental feature. The physical relationship can be determined with respect to the nominal heading of the VRU and the location of the VRU. The set of predictive inputs can be processed with a heading estimation model to generate a predicted heading offset (e.g., a target heading offset) for the VRU.

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: The present invention discloses a preprocessing and imputing method for structural data, comprising: step 1, querying the missing information of an original data, counting missing values, and obtaining a missing rate for the original data; step 2, based on the missing rate, performing listwise deletion on the original data, and then traversing the rows to generate corresponding dichotomous arrays, converting the arrays to the form of histogram, calculating the maximum rectangular area formed by the corresponding histogram, and then sorting all rectangular areas to obtain the maximum complete information matrix; step 3, using multiple imputation by chained equations, auto-encoders, or generative adversarial imputation networks to impute missing values on the original data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining respective importance scores for a plurality of agents in a vicinity of an autonomous vehicle navigating through an environment. The respective importance scores characterize a relative impact of each agent on planned trajectories generated by a planning subsystem of the autonomous vehicle. In one aspect, a method comprises providing different states of an environment as input to the planning subsystem and obtaining as output from the planning subsystem corresponding planned trajectories. Importance scores for the one or more agents that are in one state but not in the other are determined based on a measure of difference between the planned trajectories.

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 and, for only those agents which are high priority agents, generating data characterizing the agents using a first prediction model. In a first aspect, a system identifies multiple agents in an environment in a vicinity of a vehicle. The system generates a respective importance score for each of the agents by processing a feature representation of each agent using an importance scoring model. The importance score for an agent characterizes an estimated impact of the agent on planning decisions generated by a planning system of the vehicle which plans a future trajectory of the vehicle. The system identifies, as high-priority agents, a proper subset of the plurality of agents with the highest importance scores.

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: The present invention discloses a method and system of image annotation and element extraction for automobile insurance anti-fraud. The method of the present invention extracts anti-fraud elements from images such as automobile insurance scene collection and post supplementary images. The system of the present invention comprises an automobile insurance element table construction module, an image acquisition module, an annotation module and an element extraction module, wherein the annotation module comprises a multi-label classification annotation module, an automobile damage location annotation module and a personnel identity annotation module; and the element extraction module is used for performing element extraction on automobile insurance data.

Abstract: Aspects of the disclosure provide methods of modeling wrong-way driving of road users. For instance, log data including an observed trajectory of a first road user may be accessed. A first set of candidate lane segments for wrong-way driving may be identified from map information. A second set of candidate lane segments for not wrong-way driving may be identified from the map information. For each candidate lane segment in the first set and in the second set, a distance cost between the candidate lane segment and the observed trajectory may be determined. A candidate lane segment may be selected from at least one of the first set or the second set based on the determined distance costs. The selected candidate lane segment may be used to train a model to provide a likelihood of a second road user being engaged in wrong-way driving in a lane.

Abstract: The technology relates to controlling a vehicle in an autonomous driving mode. For instance, sensor data identifying an object in an environment of the vehicle may be received. A grid including a plurality of cells may be projected around the object. For each given one of the plurality of cells, a likelihood that the object will enter the given one within a period of time into the future is predicted. A contour is generated based on the predicted likelihoods. The vehicle is then controlled in the autonomous driving mode in order to avoid an area within the contour.

Abstract: Exemplary pixel structures are described that include a first light emitting diode structure, operable to generate blue light characterized by a peak emission wavelength of greater than or about 450 nm, and a second light emitting diode structure positioned on the first light emitting diode structure. The second light emitting diode structure is operable to generate ultraviolet light characterized by a peak emission wavelength of less than or about 380 nm. The pixel structures may also include a photoluminescent region, containing a photoluminescent material, that is positioned on the second light emitting diode structure.

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: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium that generates path prediction data for agents in the vicinity of an autonomous vehicle using machine learning models. One method includes identifying an agent in a vicinity of an autonomous vehicle navigating an environment and determining that the agent is within a vicinity of a crossing zone, which can be marked or unmarked. In response to determining that the agent is within a vicinity of a crossing zone: (i) features of the agent and of the crossing zone can be obtained; (ii) a first input that includes the features can be processed using a first machine learning model that is configured to generate a first crossing prediction that characterizes future crossing behavior of the agent, and (iii) a predicted path for the agent for crossing the roadway can be determined from at least the first crossing prediction.

Abstract: Jaywalking behaviors of vulnerable road users (VRUs) such as cyclists or pedestrians can be predicted. Location data is obtained that identifies a location of a VRU within a vicinity of a vehicle. Environmental data is obtained that describes an environment of the VRU, where the environmental data identifies a set of environmental features in the environment of the VRU. The system can determine a nominal heading of the VRU, and generate a set of predictive inputs that indicate, for each of at least a subset of the set of environmental features, a physical relationship between the VRU and the environmental feature. The physical relationship can be determined with respect to the nominal heading of the VRU and the location of the VRU. The set of predictive inputs can be processed with a heading estimation model to generate a predicted heading offset (e.g., a target heading offset) for the VRU.

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: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying high-priority agents in the vicinity of a vehicle. In one aspect, a method comprises processing an input that characterizes a trajectory of the vehicle in an environment using an importance scoring model to generate an output that defines a respective importance score for each of a plurality of agents in the environment in the vicinity of the vehicle. The importance score for an agent characterizes an estimated impact of the agent on planning decisions generated by a planning system of the vehicle which plans a future trajectory of the vehicle. The high-priority agents are identified as a proper subset of the plurality of agents with the highest importance scores.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a neural network configured to receive a network input and to assign a respective score to each of a plurality of locations in the network input. In one aspect, a method includes obtaining a training input and a corresponding ground truth output; processing the training input to generate a training output; computing a loss for the training input, comprising: selecting a plurality of candidate locations; setting to zero the training scores for any location in the selected candidate locations that has a ground truth score below a threshold value; for each of a plurality of pairs of locations in the selected candidate locations: computing a pair-wise loss for the pair; and combining the pair-wise losses to compute the loss for the training input; and determining an update to the current values of the parameters.

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 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: One or more embodiments of the disclosure include a digital merchant content system that creates one or more custom merchant content interfaces. In particular, in one or more embodiments, the digital merchant content system generates custom merchant content interfaces based on a custom merchant content template. The digital merchant content system can generate custom merchant content templates comprising unpopulated product display layouts. Moreover, in one or more embodiments, the digital merchant content system receives a product feed and generates a custom merchant content interface by populating the unpopulated product display layouts based on the custom merchant content template and the product feed.