公开(公告)号：US20220292091A1

公开(公告)日：2022-09-15

申请号：US17197974

申请日：2021-03-10

Applicant: HERE Global B.V.

Inventor： Catalin CAPOTA ,  David JONIETZ ,  Ali SOLEYMANI ,  Bo XU ,  Moritz NEUN

IPC: G06F16/2458 ,  G06F16/29

Abstract: An approach is provided for compression of sparse data for machine learning or equivalent tasks. The approach involves, for instance, receiving data that is binned into a plurality of bins. The data, for instance, represents a spatial surface such as a geographic region. The approach also involves processing the data by applying a compression criterion to classify one or more bins of the plurality of bins as either data-containing bins or empty bins. The approach further involves establishing a space filling curve over the plurality of bins, wherein the space filling curve linearizes the plurality of bins according to a placement order. The approach further involves storing the data-containing bins of the plurality of bins in a compressed data structure based on the placement order of the space filling curve.

公开(公告)号：US20230067464A1

公开(公告)日：2023-03-02

申请号：US17410624

申请日：2021-08-24

Applicant: HERE Global B.V.

Inventor： David JONIETZ ,  Michael KOPP ,  Moritz NEUN ,  Bo XU ,  Ali SOLEYMANI

IPC: G08G1/01 ,  G06N20/00 ,  G06K9/62

Abstract: An approach is provided for end-to-end traffic estimation. The approach involves, for instance, retrieving probe data or other sensor data collected from sensors of devices traveling in a geographic area. The approach also involves optionally aggregating the probe or sensor data into a sequence of frames. Each frame comprises a plurality of spatial cells representing the geographic area at a respective time interval. The probe or sensor data is spatially and temporally binned into the spatial cells. The approach further involves initiating an offline pre-processing pipeline to associate the probe or sensor data with road segments of a geographic database and/or otherwise determining a ground-truth traffic state for each frame or sensor data. The approach further involves training a machine learning model using the ground-truth traffic state to determine a predicted traffic state directly from input frames or sensor data.