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公开(公告)号：US20230153567A1
公开(公告)日：2023-05-18
申请号：US17530023
申请日：2021-11-18
Applicant: HERE Global B.V.
Inventor： Tero Juhani KESKI-VALKAMA
Abstract: An approach is provided for deep learning of sparse spatial data functions. The approach involves, for instance, creating a sort convolutional neural network (SortCNN) layer comprising a multi-head cross-attention layer and one or more convolutional neural network (CNN) layers. At least one attention head of the multi-head cross-attention layer is associated with at least one linear projection matrix that is trained to arrange and quantize an unsorted set of input entities (e.g., sparse data) along an axis of a query/key space into a soft sorted set of the input entities based on inducing points in the query/key space. The approach also involves projecting the soft sorted entities from the multi-head cross-attention layer through the CNN layers. The CNN layers learn one or more functions based on integrating information from the soft sorted entities as arranged and quantized by the at least one linear projection matrix.
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2.发明公开公开(公告)号：US20230280178A1
公开(公告)日：2023-09-07
申请号：US17652950
申请日：2022-03-01
Applicant: HERE GLOBAL B.V.
Inventor： Tero Juhani KESKI-VALKAMA
CPC classification number: G01C21/3807 , G01C21/3848 , B60W60/001 , B60W2556/40
Abstract: A method, apparatus and computer program product are provided for learning to generate maps from raw geospatial observations from sensors traveling within an environment. Methods may include: receiving a plurality of sequences of geospatial observations from discrete trajectories; aligning and concatenating the trajectories to generate concatenated, aligned geospatial observations; performing attentional clustering on the concatenated, aligned geospatial observations to obtain a set of entities; processing the set of entities using one or more Set Transformers; generating a preliminary feature set of map object geometries; determining a number of output entities from each of the one or more output heads based on statistics from the plurality of sequences of geospatial observations from the discrete trajectories and a count of the discrete trajectories; and outputting a feature set, the feature set including a number of map object geometries corresponding to a respective number of output entities for the respective output head.
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公开(公告)号：US20220414267A1
公开(公告)日：2022-12-29
申请号：US17304913
申请日：2021-06-28
Applicant: HERE GLOBAL B.V.
Inventor： Stefano BENNATI , Tero Juhani KESKI-VALKAMA
Abstract: A method, apparatus, and computer program product are provided for using confidential computing to execute code on sensitive data in an encrypted area of an apparatus limiting access to data and code to only their respective owners. Methods may include: generating an outer enclave and at least one inner enclave within the outer enclave; providing an outer enclave key and an inner enclave key to a service provider; providing an inner enclave key to a data provider; receiving, from the data provider, a data retrieval location; processing data from the respective retrieval location at the data provider inner enclave using data provider code to generate data provider processed data; providing the data provider processed data to the service provider inner enclave; and processing the data provider processed data with service provider code to generate resultant data; decrypting the resultant data in the outer enclave.
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公开(公告)号：US20220413502A1
公开(公告)日：2022-12-29
申请号：US17358764
申请日：2021-06-25
Applicant: HERE Global B.V.
Inventor： Tero Juhani KESKI-VALKAMA
Abstract: An approach is provided for biasing machine learning models towards potential risks for controlling vehicles/robots. The approach involves, for example, determining an occluded space that is occluded in sensor data collected from one or more sensors of a vehicle or a robot. The approach also involves generating a sensor space completion that represents the occluded space based on biasing a generation of one or more potential risks to the vehicle or the robot originating from the occluded space. The approach further involves providing the sensor space completion to a system of the vehicle or the robot for generating a control decision, a warning, or a combination thereof.
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5.发明公开公开(公告)号：US20240013554A1
公开(公告)日：2024-01-11
申请号：US17859745
申请日：2022-07-07
Applicant: HERE Global B.V.
Inventor： Tero Juhani KESKI-VALKAMA , Reinhard Walter KÖHN
CPC classification number: G06V20/588 , G06V10/761 , G06V10/82 , G06T7/70 , G06N3/08
Abstract: An approach is provided for machine learning-based registration of imagery with different perspectives. The approach, for example, involves retrieving a first training image and a second training image. The first training image depicts a geographic area from a first perspective and the second training image depicts the geographic area from a second perspective. The approach also involves initiating a labeling of one or more ground truth correspondence masks between the first training image and the second training image. The one or more ground truth correspondence masks denote an image region of the first training image that matches a corresponding image region of the second training image or vice versa. The approach further involves using the one or more ground truth correspondence masks to train a machine learning model to determine one or more predicted correspondence masks between a first input image and a second input image.
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Patent Agency Ranking
公开(公告)号：US20230280184A1
公开(公告)日：2023-09-07
申请号：US17652951
申请日：2022-03-01
Applicant: HERE GLOBAL B.V.
Inventor： Tero Juhani KESKI-VALKAMA
CPC classification number: G01C21/3848 , G01C21/3819 , G01C21/3822 , B60W60/001 , G01C21/3859 , B60W2552/53 , B60W2554/20
Abstract: A method, apparatus and computer program product are provided for learning to generate maps from raw geospatial observations from sensors traveling within an environment. Methods may include: receiving a plurality of sequences of geospatial observations from discrete trajectories; refining a summary entity set representation through iteration over discrete trajectories; determining a drive offset for each of the discrete trajectories based on a comparison of the summary entity set representation to a drive entity set for each of the discrete trajectories; aligning the discrete trajectories to generate aligned geospatial observations based, at least in part, on the drive offset for a respective discrete trajectory; concatenating the aligned geospatial observations; processing the concatenated, aligned geospatial observations using at least one Set Transformer; and generating, from the at least one Set Transformer, map geometries including objects from the geospatial observations.
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公开(公告)号：US20230050402A1
公开(公告)日：2023-02-16
申请号：US17444861
申请日：2021-08-11
Applicant: HERE GLOBAL B.V.
Inventor： Tero Juhani KESKI-VALKAMA
Abstract: A method, apparatus and computer program product are provided for learning to generate maps from raw geospatial observations from sensors traveling within an environment. Methods may include: receiving a plurality of sequences of geospatial observations from discrete trajectories; aligning the trajectories to generate aligned geospatial observations; concatenating the aligned geospatial observations; processing the concatenated, aligned geospatial observations using one or more Set Transformers; generating, from the at least one Set Transformer, map geometries including objects from the geospatial observations; and providing at least one of navigational assistance or at least semi-autonomous vehicle control based on the map geometries. According to some embodiments, aligning the trajectories includes applying a geospatial offset for one or more of the trajectories.
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公开(公告)号：US20230280186A1
公开(公告)日：2023-09-07
申请号：US17652953
申请日：2022-03-01
Applicant: HERE GLOBAL B.V.
Inventor： Tero Juhani KESKI-VALKAMA
CPC classification number: G01C21/3867 , G01C21/3815 , G01C21/3841 , G01C21/3878 , B60W60/001 , B60W2556/40
Abstract: A method, apparatus and computer program product are provided for learning to generate maps from raw geospatial observations from sensors traveling within an environment. Methods may include: receiving a plurality of sequences of geospatial observations from discrete trajectories; aligning the discrete trajectories generating aligned geospatial observations; concatenating the aligned geospatial observations; performing attentional clustering on the concatenated, aligned geospatial observations to obtain a set of entities with feature dimensionality; processing the set of entities through an iterative attention model incorporating a Gated Recurrent Unit gating pattern to obtain attentional layer outputs; generating, from one or more Set Transformers, a feature set of map object geometries based, at least in part, on the attentional layer outputs; updating a map geometry based on the feature set from the Set Transformers generating an updated map geometry; and provide for navigational assistance or at least semi-autonomous vehicle control based on the updated map geometry.
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公开(公告)号：US20230280185A1
公开(公告)日：2023-09-07
申请号：US17652952
申请日：2022-03-01
Applicant: HERE GLOBAL B.V.
Inventor： Tero Juhani KESKI-VALKAMA
CPC classification number: G01C21/3848 , G01C21/3819 , G01C21/3822 , B60W60/001 , G01C21/3859 , B60W2552/53 , B60W2556/40
Abstract: A method, apparatus and computer program product are provided for learning to generate maps from raw geospatial observations from sensors traveling within an environment. Methods may include: processing geospatial observations from discrete trajectories through an iterative attention model incorporating a Gated Recurrent Unit gating pattern to obtain a feature summary; determining a drive offset for each of the discrete trajectories based on the feature summary; aligning the discrete trajectories to generate aligned geospatial observations based, at least in part, on the drive offset for a respective discrete trajectory; concatenating the aligned geospatial observations; processing the concatenated, aligned geospatial observations using at least one Set Transformer; generating, from the at least one Set Transformer, map geometries including objects from the geospatial observations; and providing for at least one of navigational assistance or at least semi-autonomous vehicle control based on the map geometries.
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10.发明申请公开(公告)号：US20210279585A1
公开(公告)日：2021-09-09
申请号：US16809243
申请日：2020-03-04
Applicant: HERE Global B.V.
Inventor： Tero Juhani KESKI-VALKAMA
Abstract: An approach is provided for progressive training of long-lived, evolving machine learning architectures. The approach involves, for example, determining alternative paths for the evolution of the machine learning model from a first architecture to a second architecture. The approach also involves determining one or more migration step alternatives in the alternative paths. The migration steps, for instance, include architecture options for the evolution of the machine learning model. The approach further involves processing data using the options to determine respective model performance data. The approach further involves selecting a migration step from the one or more migration step alternatives based on the respective model performance data to control a rate of migration steps over a rate of training in the evolution of the machine learning model. The approach further involves initiating a deployment the selected migration step to the machine learning model.
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