Abstract: The present technology pertains to measuring the quality of an uncertainty prediction provided by a traj ectory prediction algorithm. An AI/ML platform can receive data including predicted trajectories of objects received from the trajectory prediction algorithm and observed paths for the objects received from the plurality of sensors. The predicted trajectories indicate predicted locations of the objects at a plurality of time intervals, and uncertainty predictions reflecting an uncertainty about the location of the objects at each of the predicted time intervals. The AI/ML platform can further determine respective standard deviations of the observed paths of the objects relative to the respective uncertainty predictions and plots the respective standard deviations against an ideal distribution of path distributions relative to the predicted locations of the objects at the plurality of time intervals.

Abstract: The present technology pertains to predicting trajectories of objects near an autonomous vehicle. The predictions may be obtained as output from a trajectory prediction machine learning model. The inputs to the trajectory prediction machine learning model may be based on a first map of an area surrounding an autonomous vehicle, and a second map of an area around an object within the first area. The second map may have a smaller area and a higher resolution relative to the first map.

Abstract: The subject disclosure relates to techniques for increasing a quality of predicted trajectories output from a trained prediction algorithm. A process of the disclosed technology can include receiving by a trained prediction algorithm, information about objects in an environment as perceived by sensors of an autonomous vehicle, receiving by the trained prediction algorithm, information about a location of the autonomous vehicle in the environment, generating by the trained prediction algorithm, a predicted trajectory for an object among the objects in the environment, wherein the predicted trajectory being anchored by a path through a tree of paths including at least two modes, wherein each mode creates a node in the tree, wherein a mode is a semantic classification of a portion of the predicted trajectory, and outputting by the trained prediction algorithm, the predicted trajectory for the object.

Abstract: System, methods, and computer-readable media for training an object path prediction model to reduce an uncertainty of a predicted path when the predicted path of an object adjacent to another object. The training penalizes an uncertainty area prediction associated with a predicted future location of a nearby object to an autonomous vehicle (AV) when the uncertainty area prediction overlaps with another object to which the first detected object would be adjacent at the predicted future location. The training also penalizes a set of predicted future locations that implies improbable vehicle kinematics, whereby the object path prediction model becomes trained to avoid predicting similar sets of predicted future locations with improbable vehicle kinematics.

Abstract: A network system can receive, from each of a plurality of devices operated by a respective provider, at least one set of availability data associated with that provider. Each set of availability data includes a start location, an end location, a date, and a start time range. For each set of availability data, the network system can identify a paired data set from a plurality of paired data sets based on the respective start location and the respective end location of that set of availability data, and associate an identifier of the respective provider of that set of availability data with a group associated with the identified paired data set. The network system can receive request data from a computing device of a user, determine one of the plurality of paired data sets, and select a first provider from the group associated with the determined paired data set.

Abstract: When an online system receives a request to present content items to a user, a content selection system included in the online system selects content items for presentation to the user. A feedback control mechanism communicates with each computing device of the content selection system to determine the latency period and the CPU utilization of each computing device. The feedback control mechanism also determines a target latency period and a target CPU utilization in which content items are selected. By comparing the latency period of each computing device to the target latency period and the CPU utilization to the target CPU utilization, an amount of information to be evaluated by each computing device is determined based on the comparisons.

Abstract: Some embodiments include a method of generating a compatibility score for a grouping of objects based on correlations between attributes of the objects. An example grouping is a pair of user and ad. The method may be implemented using a multi-threaded pipeline architecture that utilizes a learning model to compute the compatibility score. The learning model determines correlations between a first object's attributes (e.g., user's liked pages, user demographics, user's apps installed, pixels visited, etc.) and a second object's attributes (e.g., expressed or implied). Example expressed attributes can be targeting keywords; example implied attributes can be object IDs associated with the ad.

Abstract: A network system can receive, from each of a plurality of devices operated by a respective provider, at least one set of availability data associated with that provider. Each set of availability data includes a start location, an end location, a date, and a start time range. For each set of availability data, the network system can identify a paired data set from a plurality of paired data sets based on the respective start location and the respective end location of that set of availability data, and associate an identifier of the respective provider of that set of availability data with a group associated with the identified paired data set. The network system can receive request data from a computing device of a user, determine one of the plurality of paired data sets, and select a first provider from the group associated with the determined paired data set.