公开(公告)号：US10060751B1

公开(公告)日：2018-08-28

申请号：US15597999

申请日：2017-05-17

Applicant: HERE GLOBAL B.V.

Inventor： Qin Chen ,  Jaime Ballesteros

IPC: G01C21/00 ,  G01C21/32 ,  G06N99/00 ,  G06N7/00 ,  G06F17/30

CPC classification number: G01C21/32 ,  G06F16/29 ,  G06N5/003 ,  G06N7/005 ,  G06N20/00

Abstract: An approach is provided for point-based map matchers using machine learning. The approach involves retrieving points collected within proximity to a map feature represented by a link of a geographic database. The probe points are collected from sensors of devices traveling near the map feature. The approach also involves determining a probe feature set for each probe point comprising probe attribute values, and determining a link feature set for the link comprising link attribute values. The apparatus further involves classifying, using a machine learning classifier, each probe point to determine a matching probability based on the probe feature set and the link feature to indicate a probability that each probe point is classified as map-matched to the link. The machine learning classifier is trained using ground truth data comprising reference probe points with known map-matches to respective reference links, and comprising known probe attribute values and known link attribute values.

公开(公告)号：US10281285B2

公开(公告)日：2019-05-07

申请号：US16049406

申请日：2018-07-30

Applicant: HERE GLOBAL B.V.

Inventor： Qin Chen ,  Jaime Ballesteros

IPC: G01C21/00 ,  G01C21/32 ,  G06N7/00 ,  G06N20/00 ,  G06F16/29 ,  G06N5/00

Abstract: An approach is provided for point-based map matchers using machine learning. The approach involves retrieving points collected within proximity to a map feature represented by a link of a geographic database. The probe points are collected from sensors of devices traveling near the map feature. The approach also involves determining a probe feature set for each probe point comprising probe attribute values, and determining a link feature set for the link comprising link attribute values. The apparatus further involves classifying, using a machine learning classifier, each probe point to determine a matching probability based on the probe feature set and the link feature to indicate a probability that each probe point is classified as map-matched to the link. The machine learning classifier is trained using ground truth data comprising reference probe points with known map-matches to respective reference links, and comprising known probe attribute values and known link attribute values.