Abstract: Indexing a data corpus to a set of multidimensional points, including: generating a set of points in a multidimensional space; identifying, for each sample in a plurality of samples in a data corpus, a nearest point in the set of points; and generating an index mapping each sample with the nearest point in the set of points.

Abstract: Distributed data sampling, including: receiving a sampling target; generating, based on one or more sensors, sampled data; determining, based on the sampling target, a value for the sampled data; and determining, based on the value for the sampled data, whether to provide the sampled data to a remotely disposed computing device.

Abstract: Throughput reduction in autonomous vehicle camera sensors, including: generating, by a camera sensor, a frame; selecting an area of focus for the frame; and generating, by the camera sensor from the frame, a downsampled frame and a cropped frame, wherein the cropped frame is based on the area of focus.

Abstract: Indexing a data corpus to a set of multidimensional points, including: generating a set of points in a multidimensional space; identifying, for each sample in a plurality of samples in a data corpus, a nearest point in the set of points; and generating an index mapping each sample with the nearest point in the set of points.

Abstract: Determining control operations for an autonomous vehicle may include receiving, by an operational model, an operational model input based on camera data from one or more cameras of an automated vehicle; determining, by the operational model, based on the input, a current environmental state and a predicted environmental state; providing, to a rules module, a differential between the current environmental state and a previously predicted environmental state; and determining, based on the rules module, one or more control operations for the automated vehicle.

Abstract: Throughput reduction in autonomous vehicle camera sensors, including: generating, by a camera sensor, a frame; selecting an area of focus for the frame; and generating, by the camera sensor from the frame, a downsampled frame and a cropped frame, wherein the cropped frame is based on the area of focus.

Abstract: Camera data normalization for an autonomous vehicle are described herein, including: receiving, from one or more cameras of the autonomous vehicle, camera data; applying a color normalization to the camera data; applying a spherical reprojection to the camera data; and applying, based on a registration point, a stabilization to the camera data.

Abstract: Detecting out-of-model scenarios for an autonomous vehicle including: determining, based on first sensor data from one or more sensors, an environmental state relative to the autonomous vehicle, wherein operational commands for the autonomous vehicle are based on a selected machine learning model, wherein the selected machine learning model comprises a first machine learning model; comparing the environmental state to a predicted environmental state relative to the autonomous vehicle; and determining, based on a differential between the environmental state and the predicted environmental state, whether to select a second machine learning model as the selected machine learning model.

Abstract: Scene filtering using motion estimation, including identifying, in camera data from an autonomous vehicle, based on motion relative to the autonomous vehicle, one or more pixels; filtering, from the camera data, the one or more pixels; and training, based on the filtered camera data, a neural network.

Abstract: Using predictive visual anchors to control an autonomous vehicle, including: determining, based on a plurality of frames of video data from a camera of an autonomous vehicle, one or more predicted visual anchors, wherein the one or more predicted visual anchors comprise a predicted location of one or more visual anchors at a future time relative to when the plurality of frames were captured; identifying, in another frame of video data corresponding to the future time, the one or more visual anchors; determining one or more differentials between the one or more visual anchors and the one or more predicted visual anchors; determining, based on the one or more differentials, one or more control operations for the autonomous vehicle; and applying the one or more control operations.

Abstract: Controlling an automated vehicle using visual anchors, including receiving, from one or more cameras of an autonomous vehicle, first video data; identifying one or more visual anchors in the first video data; determining one or more differentials between the one or more visual anchors and one or more predicted visual anchors; and determining, based on the one or more differentials, one or more control operations for the autonomous vehicle to reduce the one or more differentials.

Abstract: Distributed data sampling, including: receiving a sampling target; generating, based on one or more sensors, sampled data; determining, based on the sampling target, a value for the sampled data; and determining, based on the value for the sampled data, whether to provide the sampled data to a remotely disposed computing device.

Abstract: Determining control operations for an autonomous vehicle may include receiving, by an operational model, an operational model input based on camera data from one or more cameras of an automated vehicle; determining, by the operational model, based on the input, a current environmental state and a predicted environmental state; providing, to a rules module, a differential between the current environmental state and a previously predicted environmental state; and determining, based on the rules module, one or more control operations for the automated vehicle.

Abstract: Scene filtering using motion estimation, including identifying, in camera data from an autonomous vehicle, based on motion relative to the autonomous vehicle, one or more pixels; filtering, from the camera data, the one or more pixels; and training, based on the filtered camera data, a neural network.

Abstract: Camera data normalization for an autonomous vehicle are described herein, including: receiving, from one or more cameras of the autonomous vehicle, camera data; applying a color normalization to the camera data; applying a spherical reprojection to the camera data; and applying, based on a registration point, a stabilization to the camera data.

Abstract: Motion-based scene selection for an autonomous vehicle may include identifying, in camera data from an autonomous vehicle, based on a plurality of motion vectors associated with the camera data, one or more image objects; determining, for each image object of the one or more image objects, based on the one or more motion vectors, a corresponding label of one or more labels; and encoding the one or more labels in association with the camera data.

Abstract: Detecting out-of-model scenarios for an autonomous vehicle including: determining, based on first sensor data from one or more sensors, an environmental state relative to the autonomous vehicle, wherein operational commands for the autonomous vehicle are based on a selected machine learning model, wherein the selected machine learning model comprises a first machine learning model; comparing the environmental state to a predicted environmental state relative to the autonomous vehicle; and determining, based on a differential between the environmental state and the predicted environmental state, whether to select a second machine learning model as the selected machine learning model.

Abstract: Controlling an automated vehicle using visual anchors, including receiving, from one or more cameras of an autonomous vehicle, first video data; identifying one or more visual anchors in the first video data; determining one or more differentials between the one or more visual anchors and one or more predicted visual anchors; and determining, based on the one or more differentials, one or more control operations for the autonomous vehicle to reduce the one or more differentials.

Abstract: Optimistic concurrency is effectuated to manage constraints in a synchronization environment at multiple computing device endpoints in a consistent fashion without utilizing concentrated centralized constraint logic. Implemented data synchronization constraints that identify false violation scenarios may be automatically resolved without user intervention by using an etag system directed by a master component to assist computing device endpoints to maintain data synchronization among them. Data entries defining each file hierarchy component to be synched are generated and shared with the master component and each computing device endpoint in a synchronization environment. Individual computing device endpoints can use the data entries generated locally with those generated by other computing device endpoints to locally resolve identified false violation scenarios.

Abstract: Optimistic concurrency is effectuated to manage constraints in a synchronization environment at multiple computing device endpoints in a consistent fashion without utilizing concentrated centralized constraint logic. Implemented data synchronization constraints that identify false violation scenarios may be automatically resolved without user intervention by using an etag system directed by a master component to assist computing device endpoints to maintain data synchronization among them. Data entries defining each file hierarchy component to be synched are generated and shared with the master component and each computing device endpoint in a synchronization environment. Individual computing device endpoints can use the data entries generated locally with those generated by other computing device endpoints to locally resolve identified false violation scenarios.