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公开(公告)号：US11801871B1
公开(公告)日：2023-10-31
申请号：US18147316
申请日：2022-12-28
CPC分类号： B60W60/00272 , B60W2552/53 , B60W2554/4041 , B60W2554/4044 , B60W2554/4045 , B60W2556/00 , G06Q50/30
摘要： 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.
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公开(公告)号：US12110042B1
公开(公告)日：2024-10-08
申请号：US17501480
申请日：2021-10-14
CPC分类号： B60W60/00274 , B60W40/04 , B60W50/0097 , B60W50/06 , B60W60/0011 , G06N3/044 , G06N3/045 , B60W2554/4041 , B60W2554/4044
摘要： Example aspects of the present disclosure describe the generation of more realistic trajectories for a moving actor with a hybrid technique using an algorithmic trajectory shaper in a machine-learned trajectory prediction pipeline. In this manner, for example, systems and methods of the present disclosure leverage the predictive power of machine-learning approaches combined with a priori knowledge about physically realistic trajectories for a given actor as encoded in an algorithmic approach.
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公开(公告)号：US20240217558A1
公开(公告)日：2024-07-04
申请号：US18471690
申请日：2023-09-21
IPC分类号： B60W60/00
CPC分类号： B60W60/00272 , B60W2554/4045 , B60W2556/00 , G06Q50/30
摘要： 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 includes 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.
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公开(公告)号：US20240270260A1
公开(公告)日：2024-08-15
申请号：US18626023
申请日：2024-04-03
CPC分类号： B60W50/0097 , G06N3/04 , B60W2050/0028 , B60W2554/4041 , B60W2554/4042 , B60W2554/4043 , B60W2554/801 , B60W2554/802
摘要： Systems and methods for predicting interactions between objects and predicting a trajectory of an object are presented herein. A system can obtain object data associated with a first object and a second object. The object data can have position data and velocity data for the first object and the second object. Additionally, the system can process the obtained object data to generate a hybrid graph using a graph generator. The hybrid graph can have a first node indicative of the first object and a second node indicative of the second object. Moreover, the system can process, using an interaction prediction model, the generated hybrid graph to predict an interaction type between the first node and the second node. Furthermore, the system can process, using a graph neural network model, the predicted interaction type between the first node and the second node to predict a trajectory of the first object.
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