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Yuliang Guo

from Redwood City, CA
Age ~38
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Connected to:
Alyssa D Guo, 28Annabella P Guo, 54Audrey M Guo, 34Bev Guo, 46Billy Guo, 34Biwei Guo, 42Charlene S Guo, 67Christopher L Guo, 43Chunyuan Guo, 44Deborah Guo, 33

Yuliang Guo Phones & Addresses

  • Redwood City, CA
  • Providence, RI

Work

Company: Brown university Sep 2011 Address: 190 Angell St. Providence, RI 02912 Position: Phd student & research assistant

Education

Degree: M.S. School / High School: Brown University 2009 to 2011 Specialities: Computer Engineering

Skills

Computer Vision • Matlab • Machine Learning • C++ • C • Latex • Engineering • Applied Mathematics • Mathematics • Java

Interests

Accordion • Video Game • Basketball • Tennis • Travel

Industries

Computer Software

Resumes

Resumes

Yuliang Guo Photo 1

Senior Research Scientist

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Location:
San Francisco, CA
Industry:
Computer Software
Work:
Brown University - 190 Angell St. Providence, RI 02912 since Sep 2011
PhD Student & Research Assistant
Vector Software Inc - East Greenwich, RI Jun 2011 - Aug 2011
Software Developer
Shenyang Institute of Metal Research Aug 2008 - Sep 2008
Research Assistant
CNR Corporation Limited Jun 2008 - Aug 2008
Mechanical Design Project
Education:
Brown University 2009 - 2011
M.S., Computer Engineering Shanghai Jiao Tong University 2005 - 2009
B.S, Materials Science and Engineering; Electrical Materials
Skills:
Computer Vision
Matlab
Machine Learning
C++
C
Latex
Engineering
Applied Mathematics
Mathematics
Java
Interests:
Accordion
Video Game
Basketball
Tennis
Travel

Publications

Us Patents

Method For Autonomously Driving A Vehicle Based On Moving Trails Of Obstacles Surrounding The Vehicle

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US Patent:
20200406893, Dec 31, 2020
Filed:
Jun 28, 2019
Appl. No.:
16/457847
Inventors:
- Sunnyvale CA, US
Guang CHEN - Sunnyvale CA, US
Weide ZHANG - Sunnyvale CA, US
Yuliang GUO - Sunnyvale CA, US
Ka Wai TSOI - Sunnyvale CA, US
International Classification:
B60W 30/095
B60W 30/09
G06K 9/00
G05D 1/00
Abstract:
During the autonomous driving, the movement trails or moving history of obstacles, as well as, an autonomous driving vehicle (ADV) may be maintained in a corresponding buffer. For each of the obstacles or objects and the ADV, the vehicle states at different points in time are maintained and stored in one or more buffers. The vehicle states representing the moving trails or moving history of the obstacles and the ADV may be utilized to reconstruct a history trajectory of the obstacles and the ADV, which may be used for a variety of purposes. For example, the moving trails or history of obstacles may be utilized to determine lane configuration of one or more lanes of a road, particularly, in a rural area where the lane markings are unclear. The moving history of the obstacles may also be utilized predict the future movement of the obstacles, tailgate an obstacle, and infer a lane line.

Method For Determining Anchor Boxes For Training Neural Network Object Detection Models For Autonomous Driving

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US Patent:
20200410252, Dec 31, 2020
Filed:
Jun 28, 2019
Appl. No.:
16/457820
Inventors:
- Sunnyvale CA, US
Tae Eun CHOE - Sunnyvale CA, US
Yuliang GUO - Sunnyvale CA, US
Guang CHEN - Sunnyvale CA, US
Weide ZHANG - Sunnyvale CA, US
International Classification:
G06K 9/00
G06K 9/62
Abstract:
In one embodiment, a set of bounding box candidates are plotted onto a 2D space based on their respective dimension (e.g., widths and heights). The bounding box candidates are clustered on the 2D space based on the distribution density of the bounding box candidates. For each of the clusters of the bounding box candidates, an anchor box is determined to represent the corresponding cluster. A neural network model is trained based on the anchor boxes representing the clusters. The neural network model is utilized to detect or recognize objects based on images and/or point clouds captured by a sensor (e.g., camera, LIDAR, and/or RADAR) of an autonomous driving vehicle.

Method For Detecting Closest In-Path Object (Cipo) For Autonomous Driving

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US Patent:
20200410260, Dec 31, 2020
Filed:
Jun 28, 2019
Appl. No.:
16/457719
Inventors:
- Sunnyvale CA, US
Yuliang GUO - Sunnyvale CA, US
Guang CHEN - Sunnyvale CA, US
Weide ZHANG - Sunnyvale CA, US
Ka Wai TSOI - Sunnyvale CA, US
International Classification:
G06K 9/00
G06T 11/20
G05D 1/00
G05D 1/02
B60W 30/09
Abstract:
In one embodiment, in addition to detecting or recognizing an actual lane, a virtual lane is determined based on the current state or motion prediction of an ADV. A virtual lane may or may not be identical or similar to the actual lane. A virtual lane may represent the likely movement of the ADV in a next time period given the current speed and heading direction of the vehicle. If an object is detected that may cross a lane line of the virtual lane and is a closest object to the ADV, the object is considered as a CIPO, and an emergency operation may be activated. That is, even though an object may not be in the path of an actual lane, if the object is in the path of a virtual lane of an ADV, the object may be considered as a CIPO and subject to a special operation.

Sensor Calibration System For Autonomous Driving Vehicles

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US Patent:
20200410704, Dec 31, 2020
Filed:
Jun 28, 2019
Appl. No.:
16/457775
Inventors:
- Sunnyvale CA, US
Yuliang GUO - Sunnyvale CA, US
Guang CHEN - Sunnyvale CA, US
Ka Wai TSOI - Sunnyvale CA, US
Weide ZHANG - Sunnyvale CA, US
International Classification:
G06T 7/536
G06K 9/00
G06T 7/80
G06T 7/55
G06T 11/00
G05D 1/02
Abstract:
In response to a first image captured by a camera of an ADV, a horizon line is determined based on the camera's hardware settings, representing a vanishing point based on an initial or default pitch angle of the camera. One or more lane lines are determined based on the first image via a perception process performed on the first image. In response to a first input signal received from an input device, a position of the horizon line is updated based on the first input signal and a position of at least one of the lane lines is updated based on the updated horizon line. The input signal may represent an incremental adjustment for adjusting the position of the horizon line. A first calibration factor or first correction value is determined for calibrating a pitch angle of the camera based on a difference between the initial horizon line and the updated horizon line.
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Yuliang Guo from Redwood City, CA, age ~38 Get Report