Abstract: The present technology pertains to measuring the quality of an uncertainty prediction provided by a trajectory 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 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: 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: Disclosed herein are systems and method including a method for managing an autonomous vehicle. The method includes providing input associated with an autonomous vehicle to a machine learning model, wherein the machine learning model is trained to predict what a planning stack of the autonomous vehicle will choose with respect to selecting a low cost branch of a tree structure in which a plurality of branches of the tree structure are evaluated to determine the low cost branch associated with a future route for the autonomous vehicle. The method further includes generating an output of the machine learning model to predict an output of the planning stack and inputting the output of the machine learning model into the planning stack. The planning stack can traverse a tree structure of possible routes more efficiently with a predicted outcome based on the output of the machine learning model.

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: Methods to prepare intermediates for the conjugation of active ingredients to hydrophilic carriers are described. The polyhydroxylated carriers are first derivatized to convert the hydroxyls to leaving groups and then reacted with an alkylene diamine in the presence of a reducing agent. The resulting derivatized carrier can then be coupled to substituents containing carboxyl groups in the presence of dehydrating agents.