Abstract: Systems and methods are directed to generating behavioral predictions in reaction to autonomous vehicle movement. In one example, a computer-implemented method includes obtaining, by a computing system, local scene data associated with an environment external to an autonomous vehicle, the local scene data including actor data for an actor in the environment external to the autonomous vehicle. The method includes extracting, by the computing system and from the local scene data, one or more actor prediction parameters for the actor using a machine-learned parameter extraction model. The method includes determining, by the computing system, a candidate motion plan for the autonomous vehicle. The method includes generating, by the computing system and using a machine-learned prediction model, a reactive prediction for the actor based at least in part on the one or more actor prediction parameters and the candidate motion plan.

Abstract: Example aspects of the present disclosure relate to an example computer-implemented method for predicting the intent of actors within an environment. The example method includes obtaining state data associated with a plurality of actors within the environment and map data indicating a plurality of lanes of the environment. The method include determining a plurality of potential goals each actor based on the state data and the map data. The method includes processing the state data, the map data, and the plurality of potential goals with a machine-learned forecasting model to determine (i) a forecasted goal for a respective actor of the plurality of actors, (ii) a forecasted interaction between the respective actor and a different actor of the plurality of actors based on the forecasted goal, and (iii) a continuous trajectory for the respective actor based on the forecasted goal.

Abstract: An autonomous vehicle can obtain state data associated with an object in an environment, obtain map data including information associated with spatial relationships between at least a subset of lanes of a road network, and determine a set of candidate paths that the object may follow in the environment based at least in part on the spatial relationships between at least two lanes of the road network. Each candidate path can include a respective set of spatial cells. The autonomous vehicle can determine, for each candidate path, a predicted occupancy for each spatial cell of the respective set of spatial cells of such candidate path during at least a portion of a prediction time horizon. The autonomous vehicle can generate prediction data associated with the object based at least in part on the predicted occupancy for each spatial cell of the respective set of spatial cells for at least one candidate path.

Abstract: Systems, methods, tangible non-transitory computer-readable media, and devices associated with vehicle control based on risk-based interactions are provided. For example, vehicle data and perception data can be accessed. The vehicle data can include the speed of an autonomous vehicle in an environment. The perception data can include location information and classification information associated with an object in the environment. A scenario exposure can be determined based on the vehicle data and perception data. Prediction data including predicted trajectories of the object can be accessed. Expected speed data can be determined based on hypothetical speeds and hypothetical distances between the vehicle and the object. A speed profile that satisfies a threshold criteria can be determining based on the scenario exposure, the prediction data, and the expected speed data, over a distance. A motion plan to control the autonomous vehicle can be generated based on the speed profile.

Abstract: Systems and methods are directed to generating behavioral predictions in reaction to autonomous vehicle movement. In one example, a computer-implemented method includes obtaining, by a computing system, local scene data associated with an environment external to an autonomous vehicle, the local scene data including actor data for an actor in the environment external to the autonomous vehicle. The method includes extracting, by the computing system and from the local scene data, one or more actor prediction parameters for the actor using a machine-learned parameter extraction model. The method includes determining, by the computing system, a candidate motion plan for the autonomous vehicle. The method includes generating, by the computing system and using a machine-learned prediction model, a reactive prediction for the actor based at least in part on the one or more actor prediction parameters and the candidate motion plan.

Abstract: An autonomous vehicle can obtain state data associated with an object in an environment, obtain map data including information associated with spatial relationships between at least a subset of lanes of a road network, and determine a set of candidate paths that the object may follow in the environment based at least in part on the spatial relationships between at least two lanes of the road network. Each candidate path can include a respective set of spatial cells. The autonomous vehicle can determine, for each candidate path, a predicted occupancy for each spatial cell of the respective set of spatial cells of such candidate path during at least a portion of a prediction time horizon. The autonomous vehicle can generate prediction data associated with the object based at least in part on the predicted occupancy for each spatial cell of the respective set of spatial cells for at least one candidate path.

Abstract: The present disclosure provides systems and methods for predicting the future locations of objects that are perceived by autonomous vehicles. An autonomous vehicle can include a prediction system that, for each object perceived by the autonomous vehicle, generates one or more potential goals, selects one or more of the potential goals, and develops one or more trajectories by which the object can achieve the one or more selected goals. The prediction systems and methods described herein can include or leverage one or more machine-learned models that assist in predicting the future locations of the objects. As an example, in some implementations, the prediction system can include a machine-learned static object classifier, a machine-learned goal scoring model, a machine-learned trajectory development model, a machine-learned ballistic quality classifier, and/or other machine-learned models. The use of machine-learned models can improve the speed, quality, and/or accuracy of the generated predictions.

Abstract: The present disclosure provides systems and methods for predicting the future locations of objects that are perceived by autonomous vehicles. An autonomous vehicle can include a prediction system that, for each object perceived by the autonomous vehicle, generates one or more potential goals, selects one or more of the potential goals, and develops one or more trajectories by which the object can achieve the one or more selected goals. The prediction systems and methods described herein can include or leverage one or more machine-learned models that assist in predicting the future locations of the objects. As an example, in some implementations, the prediction system can include a machine-learned static object classifier, a machine-learned goal scoring model, a machine-learned trajectory development model, a machine-learned ballistic quality classifier, and/or other machine-learned models. The use of machine-learned models can improve the speed, quality, and/or accuracy of the generated predictions.

Abstract: An ecosystem that enables content providers to decorate search results with interactive content. The searching user can then interact with the content and view the content without leaving the search results page. The content provider sends content and metadata to a content enrichment enabler that transforms the content into an enriched content, and receives back from the enrichment enabler a location identifier which includes information that identifies the provider and the location of the enriched content. The content provider then embeds the identifier in each of the content provider webpages for which such content has been produced. The identifier is indexed by a search engine to identify the interactive content and source thereof for surfacing on a search results page. The search result is decorated with an indicator that the user recognizes as the availability of the enriched content, and uses to access the content via the web page.

Abstract: An ecosystem that enables content providers to decorate search results with interactive content. The searching user can then interact with the content and view the content without leaving the search results page. The content provider sends content and metadata to a content enrichment enabler that transforms the content into an enriched content, and receives back from the enrichment enabler a location identifier which includes information that identifies the provider and the location of the enriched content. The content provider then embeds the identifier in each of the content provider webpages for which such content has been produced. The identifier is indexed by a search engine to identify the interactive content and source thereof for surfacing on a search results page. The search result is decorated with an indicator that the user recognizes as the availability of the enriched content, and uses to access the content via the web page.

Abstract: Architecture that facilitates the mapping of multimedia topic summaries from one data source, to web search results from another data source. An algorithmic technique is provided that discriminates (selects) between topic summaries that are wanted and not wanted for presentation to the search engine user based on a predetermine set of characteristics or features. Topic summaries are each pre-associated with a topic identifier. A page identifier is extracted from or created for the webpage that is used to match the topic identifiers to the correct webpage. The page identifier is aligned with the topic identifier of the topic summaries to find matches between topic summaries and webpages. Once alignment is completed, the correct topic identifier is inserted into the internal extended representation (e.g., associated with the content header) of every webpage, which enables the subsequent fetch of the topic summaries for display to users.