Siyuan LiuSan Diego, CA

2021039, Dec 23, 2021

Jun 23, 2020

16/909987

- San Diego CA, US
Siyuan LIU - San Diego CA, US
Zehua HUANG - San Diego CA, US
Yijie WANG - San Diego CA, US

G06T 7/70
G06K 9/00
G06T 7/11
B60W 60/00
G01C 21/30

Image processing techniques are described to receive bounding box information that describes a bounding box located around a detected obj ect in an image, determine one or more positions of one or more reference points on the bounding box, determine, for each reference point, 3D world coordinates of a point of intersection of the reference point and the road surface, and assign the 3D world coordinates of the one or more reference points to a location of the detected object.

2021037, Dec 2, 2021

Aug 13, 2021

17/401781

- San Diego CA, US
Mingdong WANG - San Diego CA, US
Siyuan LIU - San Diego CA, US
Xiaodi HOU - San Diego CA, US

B60W 30/095
G06K 9/00
B60W 30/09
G05D 1/02
G06N 7/00
G06N 5/04
G06K 9/62

A system and method for proximate vehicle intention prediction for autonomous vehicles are disclosed. A particular embodiment is configured to: receive perception data associated with a host vehicle; extract features from the perception data to detect a proximate vehicle in the vicinity of the host vehicle; generate a trajectory of the detected proximate vehicle based on the perception data; use a trained intention prediction model to generate a predicted intention of the detected proximate vehicle based on the perception data and the trajectory of the detected proximate vehicle; use the predicted intention of the detected proximate vehicle to generate a predicted trajectory of the detected proximate vehicle; and output the predicted intention and predicted trajectory for the detected proximate vehicle to another subsystem.

2020039, Dec 17, 2020

Jun 14, 2019

16/442182

- San Diego CA, US
Siyuan Liu - San Diego CA, US
Lingting Ge - San Diego CA, US
Zehua Huang - San Diego CA, US

G05D 1/02
B60R 1/00
G06T 7/30
G06K 9/00

Devices, systems and methods for fusing scenes from real-time image feeds from on-vehicle cameras in autonomous vehicles to reduce redundancy of the information processed to enable real-time autonomous operation are described. One example of a method for improving perception in an autonomous vehicle includes receiving a plurality of cropped images, wherein each of the plurality of cropped images comprises one or more bounding boxes that correspond to one or more objects in a corresponding cropped image; identifying, based on the metadata in the plurality of cropped images, a first bounding box in a first cropped image and a second bounding box in a second cropped image, wherein the first and second bounding boxes correspond to a common object; and fusing the metadata corresponding to the common object from the first cropped image and the second cropped image to generate an output result for the common object.

2020016, May 21, 2020

Jan 25, 2020

16/752637

- San Diego CA, US
Xiaodi HOU - San Diego CA, US
Siyuan LIU - San Diego CA, US

G06K 9/00
G01S 17/88
G01S 17/08
G01S 17/66
G01S 17/58
G01S 7/48
G01S 17/931
G01S 17/86

A vehicle position and velocity estimation based on camera and LIDAR data are disclosed. A particular embodiment includes: receiving input object data from a subsystem of an autonomous vehicle, the input object data including image data from an image generating device and distance data from a distance measuring device; determining a two-dimensional (2D) position of a proximate object near the autonomous vehicle using the image data received from the image generating device; tracking a three-dimensional (3D) position of the proximate object using the distance data received from the distance measuring device over a plurality of cycles and generating tracking data; determining a 3D position of the proximate object using the 2D position, the distance data received from the distance measuring device, and the tracking data; determining a velocity of the proximate object using the 3D position and the tracking data; and outputting the 3D position and velocity of the proximate object relative to the autonomous vehicle.

2019036, Dec 5, 2019

May 31, 2018

15/994103

- San Diego CA, US
Mingdong WANG - San Diego CA, US
Siyuan LIU - San Diego CA, US
Xiaodi HOU - San Diego CA, US

B60W 30/095
G06K 9/00
B60W 30/09
G05D 1/02
G06N 7/00
G06K 9/62

A system and method for proximate vehicle intention prediction for autonomous vehicles are disclosed. A particular embodiment is configured to: receive perception data associated with a host vehicle; extract features from the perception data to detect a proximate vehicle in the vicinity of the host vehicle; generate a trajectory of the detected proximate vehicle based on the perception data; use a trained intention prediction model to generate a predicted intention of the detected proximate vehicle based on the perception data and the trajectory of the detected proximate vehicle; use the predicted intention of the detected proximate vehicle to generate a predicted trajectory of the detected proximate vehicle; and output the predicted intention and predicted trajectory for the detected proximate vehicle to another subsystem.

2019036, Dec 5, 2019

May 31, 2018

15/994138

- San Diego CA, US
Mingdong WANG - San Diego CA, US
Siyuan LIU - San Diego CA, US
Xiaodi HOU - San Diego CA, US

B60W 30/095
G06K 9/00
B60W 30/09
G05D 1/02
G06N 7/00
G06K 9/62
G06N 5/04

A system and method for proximate vehicle intention prediction for autonomous vehicles are disclosed. A particular embodiment is configured to: receive perception data associated with a host vehicle; extract features from the perception data to detect a proximate vehicle in the vicinity of the host vehicle; generate a trajectory of the detected proximate vehicle based on the perception data; use a trained intention prediction model to generate a predicted intention of the detected proximate vehicle based on the perception data and the trajectory of the detected proximate vehicle; use the predicted intention of the detected proximate vehicle to generate a predicted trajectory of the detected proximate vehicle; and output the predicted intention and predicted trajectory for the detected proximate vehicle to another subsystem.

2019005, Feb 14, 2019

Aug 8, 2017

15/672217

- San Diego CA, US
Siyuan LIU - San Diego CA, US
Kai ZHOU - San Diego CA, US

G06F 17/30
G06F 17/24

A system and method for implementing an image annotation platform are disclosed. A particular embodiment includes: registering a plurality of labelers to which annotation tasks are assigned; assigning annotation tasks to the plurality of labelers; determining if the annotation tasks can be closed or re-assigned to the plurality of labelers; aggregating annotations provided by the plurality of labelers as a result of the closed annotation tasks; evaluating a level of performance of the plurality of labelers in providing the annotations; and calculating payments for the plurality of labelers based on the quantity and quality of the annotations provided by the plurality of labelers.

2019000, Jan 3, 2019

Jul 1, 2017

15/640510

- San Diego CA, US
Siyuan LIU - San Diego CA, US

G06F 9/50
G06F 17/30
G06T 1/20

A system and method for distributed graphics processing unit (GPU) computation are disclosed. A particular embodiment includes: receiving a user task service request from a user node; querying resource availability from a plurality of slave nodes having a plurality of graphics processing units (GPUs) thereon; assigning the user task service request to a plurality of available GPUs based on the resource availability and resource requirements of the user task service request, the assigning including starting a service on a GPU using a distributed processing container and creating a corresponding uniform resource locator (URL); and retaining a list of URLs corresponding to the resources assigned to the user task service request.