公开(公告)号：US12179757B2

公开(公告)日：2024-12-31

申请号：US17623117

申请日：2020-11-30

Applicant: Korea Electronics Technology Institute

Inventor： Young Bo Shim ,  Kyoung Won Min ,  Haeng Seon Son ,  Seon Young Lee ,  Chang Gue Park

IPC: B60W30/10 ,  B60W60/00 ,  G06V10/30 ,  G06V20/56 ,  G06V20/58

Abstract: Provided is a method for filtering driving paths in order to stably track a path which is generated for various purposes, such as recognizing a lane and keeping or changing the lane, or avoiding an obstacle around a road. A driving path filtering system according to an embodiment of the present disclosure includes: a position recognition unit configured to recognize a position of a vehicle; a lane recognition unit configured to recognize a lane of a road; and a path generator configured to generate a path for the vehicle to travel on, based on a result of recognizing the position and a result of recognizing the lane, and to perform path noise filtering with respect to the generated path. Accordingly, a driving path can be stabilized by removing a path noise (error) by complementally filtering a lane recognition-based path generation method and a map-based path generation method according to a driving environment.

公开(公告)号：US12051230B2

公开(公告)日：2024-07-30

申请号：US17623067

申请日：2020-11-25

Applicant: Korea Electronics Technology Institute

Inventor： Kyoung Won Min ,  Ganzorig Gankhuyag ,  Haeng Seon Son ,  Seon Young Lee ,  Young Bo Shim

IPC: G06V10/26 ,  G06V10/50

CPC classification number: G06V10/267 ,  G06V10/50

Abstract: Provided is a deep learning-based panoptic segmentation accelerated processing technique using a complexity-based RPN skip method. An image segmentation system includes: a first processing unit configured to extract dynamic objects in an instance segmentation method by using an extracted feature; a calculation unit configured to control to skip some areas of the feature extracted at the network by the first processing unit, on the basis of complexity of the input image; a second processing unit configured to extract static objects in a semantic segmentation method by using the feature extracted at the network; and a fusion unit configured to fuse a result of extracting by the first processing unit and a result of extracting by the second processing unit. Accordingly, the panoptic segmentation method can be easily performed even in an embedded environment by reducing complexity for panoptic segmentation processing by reducing a calculation burden.