Shawn E Hunt

US Patent:

20220306109, Sep 29, 2022

Filed:

Mar 24, 2021

Appl. No.:

17/210780

Inventors:

- Southfield MI, US
- Kariya, JP
Shawn Hunt - Bethel Park PA, US

International Classification:

B60W 30/18
H04L 12/40
H04W 84/18
G06K 9/00

Abstract:

System, methods, and other embodiments described herein relate to improving the synchronizing of systems in a vehicle without relying on external controls or hardware. In one embodiment, a method includes detecting a stop of a vehicle according to data acquired from two or more sensors. The method also includes identifying motion from the stop according to the data and corresponding timestamps from the two or more sensors, wherein the data and the corresponding timestamps are internal to the two or more sensors. The method also includes synchronizing the two or more sensors according to the corresponding timestamps. The method also includes controlling the motion of the vehicle using the two or more sensors.

US Patent:

20220311932, Sep 29, 2022

Filed:

Oct 13, 2021

Appl. No.:

17/500059

Inventors:

- Southfield MI, US
- Kariya-city, JP
Shawn Hunt - Bethel Park PA, US

International Classification:

H04N 5/232
G06K 9/62
G06K 9/00

Abstract:

A camera control system is provided for controlling operation of a camera mounted on a vehicle. The system includes a processor and a memory communicably coupled to the processor. The memory stores a camera control module configured to compare a recognition confidence level associated with a feature to a predetermined threshold and, responsive to the recognition confidence level being below the predetermined threshold and using location information associated with the feature, control operation of the camera to capture at least one image of the feature during movement of the vehicle.

US Patent:

20220237401, Jul 28, 2022

Filed:

Jan 28, 2021

Appl. No.:

17/161110

Inventors:

- Southfield MI, US
Shawn Hunt - Bethel Park PA, US

International Classification:

G06K 9/00
G06K 9/62
G08G 1/13

Abstract:

A system for utilizing object tracking algorithms for tracking objects external to a vehicle includes a processor and a memory in communication with the processor. The object tracking algorithms include a lower computationally demanding object tracking algorithm and a higher computationally demanding object tracking algorithm. The memory has one or more modules that have instructions that cause the processor to determine if the vehicle is located in an urban setting or a non-urban setting and utilize the lower computationally demanding object tracking algorithm by an object tracking system when the vehicle is located in the non-urban setting. If it is determined that the vehicle is in an urban setting, the instructions cause the processor to determine available computational resources of the object tracking system and utilizing either the lower computationally demanding object tracking algorithm or the higher computationally demanding object tracking algorithm based on the available computational resources.

US Patent:

20230112664, Apr 13, 2023

Filed:

Mar 30, 2022

Appl. No.:

17/708745

Inventors:

- Aichi, JP
Kris KITANI - Pittsburgh PA, US
Matthew Patrick O'TOOLE - Pittsburgh PA, US
Xinshuo WENG - Pittsburgh PA, US
Shawn HUNT - Bethel Park PA, US

Assignee:

DENSO CORPORATION - Aichi

International Classification:

G06V 20/58
G06V 10/44
G06V 10/764
G01S 17/894

Abstract:

A method includes generating a plurality of lidar inputs based on the lidar data, where each lidar input from among the plurality of lidar inputs comprises an image-based portion and a geometric-based portion, and where each lidar input from among the plurality of lidar inputs defines a position coordinate of the one or more objects. The method includes performing, for each lidar input from among the plurality of lidar inputs, a convolutional neural network (CNN) routine based on the image-based portion to generate one or more image-based outputs and assigning the plurality of lidar inputs to a plurality of echo groups based on the geometric-based portion. The method includes concatenating the one or more image-based outputs and the plurality of echo groups to generate a plurality of fused outputs and identifying the one or more objects based on the plurality of fused outputs.

US Patent:

20230115660, Apr 13, 2023

Filed:

Mar 30, 2022

Appl. No.:

17/708741

Inventors:

- Aichi, JP
Shawn HUNT - Bethel Park PA, US

Assignee:

DENSO CORPORATION - Aichi

International Classification:

G06T 7/80
G06V 10/40
G06V 10/74
H04N 5/232
H04N 17/00

Abstract:

A method of calibrating a camera sensor and a SPAD LiDAR includes extracting identified features in each of a selected camera image and an ambient-intensity (A-I) image, generating a set of keypoints based on the identified features extracted for each of the images to provide a set of 2D camera keypoint locations and a set of 2D A-I keypoint locations, determining matched keypoints based on the set of 2D A-I keypoint locations and the set of 2D camera keypoint locations to provide a set of 2D A-I matched pixel locations and a set of 2D camera matched pixel locations, interpolating a 3D point cloud data with the set of 2D A-I matched pixel locations to obtain a set of 3D LiDAR matched pixel locations, and determining and storing extrinsic parameters to transform the set of 3D LiDAR matched pixel locations with the set of 2D camera matched pixel locations.

US Patent:

20210406597, Dec 30, 2021

Filed:

Jun 24, 2020

Appl. No.:

16/910718

Inventors:

- Southfield MI, US
- Aichi, JP
Shawn Hunt - Bethel Park PA, US

International Classification:

G06K 9/62
G06N 20/00
G06K 9/00
G06T 7/73

Abstract:

A system for generating synthetic training data may include one or processors and a memory in communication with the one or more processors and having a receiving module, a duplication module, and an insertion module. The modules have instructions that when executed by the one or more processors cause the one or more processors to receive original training data being in the form of a three-dimensional point cloud and having one or more original objects formed by at least a portion of the three-dimensional point cloud and annotated with original annotation data, duplicate one of the one or more original objects to generate a synthetic object, and insert the synthetic object within the original training data to generate the synthetic training data.