Abstract: This disclosure describes automatically identifying videos of a video dataset having high-content similarity and grouping the videos together with one or more possible tags for the videos for consideration by annotators. A labeling service of a service provider network receives (i) a dataset of unlabeled videos and (ii) category tags for labeling the videos from a client device associated with a user. A video data modeling engine analyzes the dataset to provide a set of ranked unlabeled videos from the dataset, wherein the set of ranked unlabeled videos are ranked according to confidence scores with respect to at least one tag. Based at least in part on the confidence scores, at least some of the unlabeled videos are labeled with the at least one tag to provide a dataset of labeled videos. The dataset of labeled videos is provided to a database and/or the user.

Abstract: Systems, methods, and non-transitory computer-readable media can determine a first utility metric associated with a region and first autonomous vehicle eligibility criteria, wherein the first utility metric is determined based on a first plurality of rides and a subset of the first plurality of rides that can be successfully executed within the region based on the first autonomous vehicle eligibility criteria. A second utility metric associated with the region and second autonomous vehicle eligibility criteria can be determined, wherein the second utility metric is determined based on a second plurality of rides and a subset of the second plurality of rides that can be successfully executed within the region based on the second autonomous vehicle eligibility criteria. An autonomous vehicle associated with the first autonomous vehicle eligibility criteria can be selected to drive in the region based on a comparison of the first utility metric and the second utility metric.

Abstract: Systems, methods, and non-transitory computer-readable media can receive transportation information associated with a transportation request, the transportation information comprising a pick up location and a drop off location. A first route associated with the transportation request and a non-autonomous vehicle can be determined. A second route associated with the transportation request and an autonomous vehicle can be determined based on an operating design domain (ODD) associated with one or more autonomous vehicles in a fleet of vehicles. At least one performance metric associated with the second route can be determined. The second route can be selected based at least in part on the at least one performance metric and a comparison of the first route and the second route. An autonomous vehicle from the fleet of vehicles can be assigned to the transportation request based on selection of the second route.

Abstract: Systems, methods, and non-transitory computer-readable media can determine a first utility metric associated with a region and first autonomous vehicle eligibility criteria, wherein the first utility metric is determined based on a first plurality of rides and a subset of the first plurality of rides that can be successfully executed within the region based on the first autonomous vehicle eligibility criteria. A second utility metric associated with the region and second autonomous vehicle eligibility criteria can be determined, wherein the second utility metric is determined based on a second plurality of rides and a subset of the second plurality of rides that can be successfully executed within the region based on the second autonomous vehicle eligibility criteria. An autonomous vehicle associated with the first autonomous vehicle eligibility criteria can be selected to drive in the region based on a comparison of the first utility metric and the second utility metric.

Abstract: Adjustments to bounding shapes for detected objects in image data may be independently determined. A bounding shape for an object detected in image data may be obtained. Independently determined adjustments for one or more edges of the bounding shape may be determined according to a multi-scale feature map generated from different resolutions of the image data that is provided as input to different dimension decoders to determine the adjustments to the bounding shape and respective confidence scores for the adjustments. The confidence scores are evaluated with respect to a confidence threshold to determine whether to provide the adjustments to the one or more edges of the bounding shape.

Abstract: Systems, methods, and non-transitory computer-readable media can receive transportation information associated with a transportation request, the transportation information comprising a pick up location and a drop off location. A first route associated with the transportation request and a non-autonomous vehicle can be determined. A second route associated with the transportation request and an autonomous vehicle can be determined based on an operating design domain (ODD) associated with one or more autonomous vehicles in a fleet of vehicles. At least one performance metric associated with the second route can be determined. The second route can be selected based at least in part on the at least one performance metric and a comparison of the first route and the second route. An autonomous vehicle from the fleet of vehicles can be assigned to the transportation request based on selection of the second route.

Abstract: Examples disclosed herein may involve (i) based on an analysis of 2D data captured by a vehicle while operating in a real-world environment during a window of time, generating a 2D track for at least one object detected in the environment comprising one or more 2D labels representative of the object, (ii) for the object detected in the environment: (a) using the 2D track to identify, within a 3D point cloud representative of the environment, 3D data points associated with the object, and (b) based on the 3D data points, generating a 3D track for the object that comprises one or more 3D labels representative of the object, and (iii) based on the 3D point cloud and the 3D track, generating a time-aggregated, 3D visualization of the environment in which the vehicle was operating during the window of time that includes at least one 3D label for the object.

Abstract: Systems, methods, and non-transitory computer-readable media can receive disengagement information associated with one or more autonomous vehicles, the disengagement information identifying a plurality of disengagements of an autonomy system during operation of the one or more autonomous vehicles. Each disengagement of the plurality of disengagements can be categorized based on a plurality of categories, wherein a first category of the plurality of categories is associated with disengagement that would not have led to a negative outcome. A performance metric associated with the one or more autonomous vehicles can be determined based on the categorizing each disengagement of the plurality of disengagements. Autonomous vehicle performance of the one or more autonomous vehicles can be evaluated based on the performance metric.

Abstract: Examples disclosed herein may involve (i) identifying, in a 3D point cloud representative of a real-world environment in which a vehicle was operating during a window of time, a set of 3D data points associated with an object detected in the environment that comprises different subsets of 3D data points corresponding to different capture times within the window of time, (ii) based at least on the 3D data points, evaluating a trajectory of the object and thereby determining that the object was in motion during some portion of the window of time, (iii) in response to determining that the object was in motion, reconstructing the different subsets of 3D data points into a single, assembled 3D representation of the object, and (iv) generating a time-aggregated, 3D visualization of the environment that presents the single, assembled 3D representation of the object at one or more points along the trajectory of the object.

Abstract: Examples disclosed herein may involve (i) identifying, in a 3D point cloud representative of a real-world environment in which a vehicle was operating during a window of time, a set of 3D data points associated with an object detected in the environment that comprises different subsets of 3D data points corresponding to different capture times within the window of time, (ii) based at least on the 3D data points, evaluating a trajectory of the object and thereby determining that the object was in motion during some portion of the window of time, (iii) in response to determining that the object was in motion, reconstructing the different subsets of 3D data points into a single, assembled 3D representation of the object, and (iv) generating a time-aggregated, 3D visualization of the environment that presents the single, assembled 3D representation of the object at one or more points along the trajectory of the object.

Abstract: Examples disclosed herein may involve (i) based on an analysis of 2D data captured by a vehicle while operating in a real-world environment during a window of time, generating a 2D track for at least one object detected in the environment comprising one or more 2D labels representative of the object, (ii) for the object detected in the environment: (a) using the 2D track to identify, within a 3D point cloud representative of the environment, 3D data points associated with the object, and (b) based on the 3D data points, generating a 3D track for the object that comprises one or more 3D labels representative of the object, and (iii) based on the 3D point cloud and the 3D track, generating a time-aggregated, 3D visualization of the environment in which the vehicle was operating during the window of time that includes at least one 3D label for the object.

Abstract: Examples disclosed herein may involve (i) based on an analysis of 2D data captured by a vehicle while operating in a real-world environment during a window of time, generating a 2D track for at least one object detected in the environment comprising one or more 2D labels representative of the object, (ii) for the object detected in the environment: (a) using the 2D track to identify, within a 3D point cloud representative of the environment, 3D data points associated with the object, and (b) based on the 3D data points, generating a 3D track for the object that comprises one or more 3D labels representative of the object, and (iii) based on the 3D point cloud and the 3D track, generating a time-aggregated, 3D visualization of the environment in which the vehicle was operating during the window of time that includes at least one 3D label for the object.

Abstract: Systems, methods, and non-transitory computer-readable media can receive transportation information associated with a transportation request, the transportation information comprising a pick up location and a drop off location. A first route associated with the transportation request and a non-autonomous vehicle can be determined. A second route associated with the transportation request and an autonomous vehicle can be determined based on an operating design domain (ODD) associated with one or more autonomous vehicles in a fleet of vehicles. At least one performance metric associated with the second route can be determined. The second route can be selected based at least in part on the at least one performance metric and a comparison of the first route and the second route. An autonomous vehicle from the fleet of vehicles can be assigned to the transportation request based on selection of the second route.

Abstract: Systems, methods, and non-transitory computer-readable media can determine a first utility metric associated with a region and first autonomous vehicle eligibility criteria, wherein the first utility metric is determined based on a first plurality of rides and a subset of the first plurality of rides that can be successfully executed within the region based on the first autonomous vehicle eligibility criteria. A second utility metric associated with the region and second autonomous vehicle eligibility criteria can be determined, wherein the second utility metric is determined based on a second plurality of rides and a subset of the second plurality of rides that can be successfully executed within the region based on the second autonomous vehicle eligibility criteria. An autonomous vehicle associated with the first autonomous vehicle eligibility criteria can be selected to drive in the region based on a comparison of the first utility metric and the second utility metric.

Abstract: Systems, methods, and non-transitory computer-readable media can receive disengagement information associated with one or more autonomous vehicles, the disengagement information identifying a plurality of disengagements of an autonomy system during operation of the one or more autonomous vehicles. Each disengagement of the plurality of disengagements can be categorized based on a plurality of categories, wherein a first category of the plurality of categories is associated with disengagement that would not have led to a negative outcome. A performance metric associated with the one or more autonomous vehicles can be determined based on the categorizing each disengagement of the plurality of disengagements. Autonomous vehicle performance of the one or more autonomous vehicles can be evaluated based on the performance metric.

Abstract: Disclosed in some examples are methods, systems, and machine-readable mediums which automatically determine network-based data sources for information ingestion and profile data completion. This method can be applied to automatically increase the library of network-based data sources utilized by the system to ingest profile information. This allows for more a complete tracking of member accomplishments and attributes and ultimately, allows for more complete member profiles. Before specific methods and systems for automatically determining network-based data sources are discussed, an overview of the process of ingesting information from network-based data sources and matching that information to members of the social networking service will be described.

Abstract: Disclosed in some examples are methods, systems, and machine-readable mediums which automatically determine network-based data sources for information ingestion and profile data completion. This method can be applied to automatically increase the library of network-based data sources utilized by the system to ingest profile information. This allows for more a complete tracking of member accomplishments and attributes and ultimately, allows for more complete member profiles. Before specific methods and systems for automatically determining network-based data sources are discussed, an overview of the process of ingesting information from network-based data sources and matching that information to members of the social networking service will be described.

Abstract: Disclosed in some examples are methods, systems, and machine readable mediums that utilize information ingested from publicly available network-based data sources to automatically suggest adding additional attributes to member profiles of a social networking service. Among other uses, this system allows for assisted member profile completion. The system ingests information from one or more publicly available network-based data sources (data sources that are different from the social networking service), creates information records that describe potential member profile attributes using that ingested data, identifies members of the social networking service that are associated with the information records using information in the information records and pre-existing member profile attributes, and then prompts one or more members to add the potential attributes to their profiles. The potential member profile attributes may be related to one or more member accomplishments.

Abstract: Technologies are described herein for constructing a minimal perfect hash function. According to embodiments, a hash table is constructed by double hashing each of the strings in a set of strings. A computed double hash value is utilized to identify an empty location in the hash table for each string. A signature for each string is stored in the empty location of the hash table identified for the string. In order to obtain a minimal perfect hash value for an input string, the input string is iteratively double hashed until a location is identified in the hash table that contains a signature corresponding to the input string. The minimal perfect hash value is an integer value identifying the location in the hash table that contains the signature corresponding to the input string.

Abstract: A processing device and method may be provided for determining whether a zero search result may be produced with respect to a search for a document including all words of a word group. An index, with respect to words included in a group of documents, may be searched for documents including all words of the word group when a zero search result is determined not likely to occur with respect to the search for the document including all of the words of the word group. A method for creating multiple types of data structures corresponding to word grouping collections may further be provided to store occurrence information indicating a likelihood of a presence of a document including all words of a word group.