公开(公告)号：US20240037791A1

公开(公告)日：2024-02-01

申请号：US18355662

申请日：2023-07-20

Applicant: 42DOT INC

Inventor： Seong Gyun JEONG ,  Jung Hee KIM ,  Phuoc Tien Nguyen ,  Jun Hwa HUR

IPC: G06T7/80 ,  G06T3/00 ,  G06T7/50

CPC classification number: G06T7/80 ,  G06T3/0087 ,  G06T7/50 ,  G06T2207/10028

Abstract: Provided are an apparatus and method for generating a depth map by using a volumetric feature. The method may generate a single feature map for a base image included in a surround-view image by performing encoding and postprocessing on the base image, and generate a volumetric feature by encoding the single feature map with depth information and then projecting a result of the encoding into a three-dimensional space. In this method, a depth map of a surround-view image may be generated by using a depth decoder to decode a volumetric feature.

公开(公告)号：US20240078817A1

公开(公告)日：2024-03-07

申请号：US18458163

申请日：2023-08-30

Applicant: 42DOT INC.

Inventor： Seok Woo JUNG ,  Hee Yeon KWON ,  Jung Hee KIM ,  Seong Gyun JEONG

IPC: G06V20/56 ,  B60W30/12 ,  G06V10/82

CPC classification number: G06V20/588 ,  B60W30/12 ,  G06V10/82

Abstract: The present disclosure relates to a method and apparatus for generating a lane polyline by using a neural network model. The method according to an embodiment may extract a multi-scale image feature by using a base image obtained from at least one sensor loaded in a vehicle. According to the method, the multi-scale image feature is input to a first neural network model as input data and a BEV feature may be obtained as output data from the first neural network model. Also, according to the method, the BEV feature may be input to a second neural network model as input data and a polyline image with respect to a certain road may be obtained as output data from the second neural network model. In the present disclosure, a lane polyline obtained from the neural network may be utilized in vehicle control without going through an additional treatment process.

公开(公告)号：US20250022257A1

公开(公告)日：2025-01-16

申请号：US18687024

申请日：2022-09-07

Applicant: 42DOT INC.

Inventor： Seong Gyun JEONG ,  Hee Yeon KWON ,  Seok Woo JUNG

IPC: G06V10/774 ,  B60W40/06 ,  G06V10/762 ,  G06V10/776 ,  G06V10/82 ,  G06V20/56

Abstract: The present disclosure relates to a method and device for generating a lane polyline by using a neural network model.
The method according to an embodiment of the present disclosure may include generating a bird's-eye-view feature based on a base image obtained from at least one sensor mounted on a vehicle, and training a neural network model by using the bird's-eye-view feature as input data for the neural network model and using a lane polyline for a certain road as output data. In the present disclosure, a lane polyline obtained from the above-described neural network model may be used for controlling the vehicle without performing a separate process on the lane polyline.

公开(公告)号：US20240320981A1

公开(公告)日：2024-09-26

申请号：US18044551

申请日：2021-09-08

Applicant: 42DOT INC.

Inventor： Seung Jae LEE ,  Aidyn ZHAKATAYEV ,  Seong Gyun JEONG

IPC: G06V20/56 ,  G06T7/11 ,  G06V10/44

CPC classification number: G06V20/56 ,  G06T7/11 ,  G06V10/44

Abstract: An embodiment provides an apparatus for determining a vehicle boarding possible area for a driving image using a artificial neural network, including: an image segmentation module that obtains a driving image for a driving direction of a vehicle from a camera module and segments the driving image into a plurality of image strips; a pre-trained boarding availability classification artificial neural network module that uses the image strip as input information and boarding availability information for the image strip as output information; a feature extraction module that extracts an activation map including feature information on the image strip from the boarding availability classification artificial neural network module; and an area information generation module that generates boarding possible area information for the image strip based on the feature information included in the activation map.