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公开(公告)号：US20230051766A1
公开(公告)日：2023-02-16
申请号：US17445036
申请日：2021-08-13
Applicant: HERE GLOBAL B.V.
Inventor： Jerome BEAUREPAIRE , Dmitry KOVAL , Steven SCHULTING , Nicolas NEUBAUER , Remco TIMMER
Abstract: Embodiments described herein relate to predicting the utilization of electric vehicle (EV) charge points. Methods may include: receiving an indication of a plurality of candidate locations for EV charge points; determining static map features of the plurality of candidate locations; inputting the plurality of candidate locations and static map features into a machine learning model, where the machine learning model is trained on existing EV charge point locations, existing EV charge point static map features, and existing EV charge point utilization; determining, based on the machine learning model, a predicted utilization of an EV charge point at the plurality of candidate locations; and generating a representation of a map including the plurality of candidate locations, where candidate locations of the plurality of candidate locations are visually distinguished based on a respective predicted utilization of an EV charge point at the candidate locations.
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公开(公告)号：US20230358551A1
公开(公告)日：2023-11-09
申请号：US17739890
申请日：2022-05-09
Applicant: HERE Global B.V.
Inventor： Jerome BEAUREPAIRE , Remco TIMMER
CPC classification number: G01C21/343 , G01C21/3694 , G01C21/3605
Abstract: An approach is provided for providing a navigation route for a requesting entity to meet other entities at flexible meeting locations. The approach, for example, involves receiving a request to generate a navigation route to visit a plurality of entities, wherein one or more mobile entities of the plurality of entities are capable of moving from a current location during the navigation route. The approach also involves computing respective isolines around each mobile entity based on a travel distance, a travel time, or a combination thereof that each mobile entity is ready to travel from the current location of each mobile entity during the navigation route. The approach further involves connecting the respective isolines around each mobile entity to determine the navigation route. The approach further involves providing the navigation route as an output.
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3.发明公开公开(公告)号：US20230324553A1
公开(公告)日：2023-10-12
申请号：US17716760
申请日：2022-04-08
Applicant: HERE Global B.V.
Inventor： Remco TIMMER , Jérôme BEAUREPAIRE
CPC classification number: G01S17/89 , G06T7/73 , G06F16/29 , G06T2207/20104 , G06T2207/30181
Abstract: An approach is provided for extracting point-of-interest features based on depth sensor data captured by mobile devices. The approach involves, for instance, receiving a Light Detection and Ranging (LiDAR) scan of a location captured using a LiDAR sensor of a portable device. The approach also involves determining a context of the location, a point of interest (POI) associated with the location, or a combination thereof. The approach further involves selecting a feature recognition parameter based on the context. The feature recognition parameter performs a feature detection analysis of a scenery depicted in the scan. The approach further involves initiating the feature detection analysis of the scan based on the feature recognition parameter to identify a feature, an attribute of the feature, or a combination thereof in the scenery.
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公开(公告)号：US20230052733A1
公开(公告)日：2023-02-16
申请号：US17656500
申请日：2022-03-25
Applicant: HERE GLOBAL B.V.
Inventor： Jerome BEAUREPAIRE , Dmitry KOVAL , Steven SCHULTING , Nicolas NEUBAUER , Remco TIMMER
Abstract: Embodiments described herein relate to predicting the utilization of electric vehicle (EV) charge points. Methods may include: receiving an indication of a plurality of candidate locations for EV charge points; determining static map features of the plurality of candidate locations; inputting the plurality of candidate locations and static map features into a machine learning model, where the machine learning model is trained on existing EV charge point locations, existing EV charge point static map features, and existing EV charge point utilization; determining, based on the machine learning model, a predicted utilization of an EV charge point at the plurality of candidate locations; and generating a representation of a map including the plurality of candidate locations, where candidate locations of the plurality of candidate locations are visually distinguished based on a respective predicted utilization of an EV charge point at the candidate locations.
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