Abstract: Provided are methods and systems for lane change and intent determination. A method for operating an autonomous vehicle is provided. The method includes obtaining, first scene data associated with a scene of an autonomous vehicle. The method includes generating a plurality of trajectories for the autonomous vehicle. The method includes selecting a trajectory from the plurality of trajectories. The method includes determining a vehicle action intent of the selected trajectory. The method includes combining the vehicle action intent of the selected trajectory with a set of vehicle action intents to form a plurality of vehicle action intents. The set of vehicle action intents correspond to a set of trajectories generated prior to the selected trajectory from second scene data. The method includes selecting a vehicle action for the autonomous vehicle and causing the autonomous vehicle to initiate performance of the vehicle action based on the selecting the action.

Abstract: Provided are methods for light-based object localization, which can include comparing unexpected light sources to expected light sources for determination of an agent, such as a partially and/or fully occluded agent. Some methods described also include generating a trajectory for an autonomous vehicle based on the comparison. Systems and computer program products are also provided.

Abstract: Provided are methods for motion planner constraint generation based on road surface hazards, which can include receiving information about an object, identifying the object as a particular road hazard, generating one or more motion constraints based on the road hazard, and controlling a vehicle based on the motion constraints. Systems and computer program products are also provided.

Abstract: Among other things, techniques are described for determining precedence order at a multiway stop. In embodiments, identifications are assigned to tracks, and young tracks are compared to stale tracks. A young track matches a stale track based on one or more factors. An identification of the young track is reassigned to an identification of the stale track, wherein the young track is determined to match the stale track based on the one or more factors. An earliest time of appearance of agents is determined based on identifications and in view of perception obscured areas. A precedence order for navigating through the intersection is determined based on local rules, the identifications, and the earliest time of appearance of agents, and the vehicle proceeds through the multiway stop intersection in accordance with the precedence order.

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media directed to operations to facilitate generation of time-lapse videos. In accordance with embodiments described herein, frames of a photographic input are analyzed to detect activity occurring across frame pairs. The photographic input, such as video input, is input for which a time-lapse video is to be generated. Activity detected across frame pairs is used to automatically select a plurality of the frames for use in generating the time-lapse video. At least a portion of the frames selected in accordance with the activity detected across frame pairs is used to generate the time-lapse video.

Abstract: A computer implemented method and apparatus for editing video scenes based on learned user preferences. The method comprises identifying a plurality of scenes in a video; for each scene of the plurality of scenes: applying an effect to a representative frame of a scene, wherein the effect is based on learned user preferences; receiving an input that adjusts at least one effect on the representative frame; updating user preferences based on received input; and applying the at least one effect to all frames of the scene; interpolating the adjusted at least one effect at a boundary of two consecutive scenes; and applying the interpolated at least one effect to transitional frames between the two consecutive scenes.

Abstract: A computer implemented method and apparatus for editing video scenes based on learned user preferences. The method comprises identifying a plurality of scenes in a video; for each scene of the plurality of scenes: applying an effect to a representative frame of a scene, wherein the effect is based on learned user preferences; receiving an input that adjusts at least one effect on the representative frame; updating user preferences based on received input; and applying the at least one effect to all frames of the scene; interpolating the adjusted at least one effect at a boundary of two consecutive scenes; and applying the interpolated at least one effect to transitional frames between the two consecutive scenes.

Abstract: A computer implemented method and apparatus for editing video scenes based on learned user preferences. The method comprises identifying a plurality of scenes in a video; for each scene of the plurality of scenes: applying an effect to a representative frame of a scene, wherein the effect is based on learned user preferences; receiving an input that adjusts at least one effect on the representative frame; updating user preferences based on received input; and applying the at least one effect to all frames of the scene; interpolating the adjusted at least one effect at a boundary of two consecutive scenes; and applying the interpolated at least one effect to transitional frames between the two consecutive scenes.

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media directed to operations to facilitate generation of time-lapse videos. In accordance with embodiments described herein, frames of a photographic input are analyzed to detect activity occurring across frame pairs. The photographic input, such as video input, is input for which a time-lapse video is to be generated. Activity detected across frame pairs is used to automatically select a plurality of the frames for use in generating the time-lapse video. At least a portion of the frames selected in accordance with the activity detected across frame pairs is used to generate the time-lapse video.

Abstract: A computer implemented method and apparatus for editing video scenes based on learned user preferences. The method comprises identifying a plurality of scenes in a video; for each scene of the plurality of scenes: applying an effect to a representative frame of a scene, wherein the effect is based on learned user preferences; receiving an input that adjusts at least one effect on the representative frame; updating user preferences based on received input; and applying the at least one effect to all frames of the scene; interpolating the adjusted at least one effect at a boundary of two consecutive scenes; and applying the interpolated at least one effect to transitional frames between the two consecutive scenes.

Abstract: A system is provided that implements a point-in-time query. The system selects a point-in-time query type from one or more point-in-time query types. The system further retrieves metadata that includes structured query language information based on the selected point-in-time query type. The system further creates a point-in-time query for a data source based on the retrieved metadata, where the point-in-time query is a query that is based on a date and/or time. The system further compiles the point-in-time query. The system further executes the point-in-time query on the data source; where the executing of the point-in-time query creates a case series.

Abstract: A system is provided that implements a point-in-time query. The system selects a point-in-time query type from one or more point-in-time query types. The system further retrieves metadata that includes structured query language information based on the selected point-in-time query type. The system further creates a point-in-time query for a data source based on the retrieved metadata, where the point-in-time query is a query that is based on a date and/or time. The system further compiles the point-in-time query. The system further executes the point-in-time query on the data source; where the executing of the point-in-time query creates a case series.