Stephen Omar Horton

US Patent:

20220258750, Aug 18, 2022

Filed:

Feb 12, 2021

Appl. No.:

17/174777

Inventors:

Mohammed S. Huq - Hamtramck MI, US
Stephen Horton - Rochester MI, US

International Classification:

B60W 50/02
B60W 50/04
B60W 60/00
B60R 16/023
H04L 12/40

Abstract:

A vehicle includes a controller area network (CAN) and a plurality of a controllers in communication with each other via the CAN, wherein each controller of the plurality of controllers is configured to time-stamp messages transmitted via the CAN using a vehicle-wide synchronized clock, determine a worst-case transmission delay via the CAN based on the time-stamps for messages received from other controllers of the plurality of controllers, and based on the worse-case transmission delay, set a dynamic recovery timer for a malfunctioning controller of the plurality of controllers to recover after its malfunction, wherein the dynamic recovery timer prevents a particular controller that was malfunctioning but has since recovered from being incorrectly designated as a malfunctioning controller in need of service.

US Patent:

20210133947, May 6, 2021

Filed:

Oct 31, 2019

Appl. No.:

16/670575

Inventors:

Dalong Li - Troy MI, US
Stephen Horton - Rochester MI, US
Neil R Garbacik - Lake Orion MI, US

International Classification:

G06T 7/00
G06K 9/00
G05D 1/00
G01S 17/89
G01S 17/87
G01S 17/93
G05D 1/02

Abstract:

An autonomous driving technique comprises determining an image quality metric for each image frame of a series of image frames of a scene outside of a vehicle captured by a camera system and determining an image quality threshold based on the image quality metrics for the series of image frames. The technique then determines whether the image quality metric for a current image frame satisfies the image quality threshold. When the image quality metric for the current image frame satisfies the image quality threshold, object detection is performed by at least utilizing a first deep neural network (DNN) with the current image frame. When the image quality metric for the current image frame fails to satisfy the image quality threshold, object detection is performed by utilizing a second, different DNN with the information captured by another sensor system and without utilizing the first DNN or the current image frame.

US Patent:

20200349363, Nov 5, 2020

Filed:

May 2, 2019

Appl. No.:

16/401794

Inventors:

Miguel Hurtado - Sterling Heights MI, US
Stephen Horton - Rochester MI, US
Eliseo Miranda - Detroit MI, US

International Classification:

G06K 9/00
G06T 7/70
B60R 1/00
G08G 1/16

Abstract:

An advanced driver assistance system (ADAS) of a vehicle and associated method is disclosed. A first set of sensed lane measurements from a first imaging device and a second set of sensed lane measurements from a second imaging device are obtained. Each of the first and second sets of sensed lane measurements includes a lane estimate for the lane lines on a roadway. Each lane estimate is associated with one lane line. For each lane line, the associated lane estimates from the first and second sets of sensed lane measurements are fused to obtain a fused lane estimate, from which a representative model lane estimate is determined. For each of the plurality of lane lines, the associated lane estimates from the first and second sets of sensed lane measurements and the representative model lane estimate are fused to obtain a corrected fused lane estimate, which is output.

US Patent:

20200200906, Jun 25, 2020

Filed:

Dec 19, 2018

Appl. No.:

16/224978

Inventors:

Dalong Li - Troy MI, US
Alex M Smith - LaSalle, CA
Stephen Horton - Rochester MI, US

International Classification:

G01S 17/66
G01S 17/93
G01S 7/48
B60W 30/09
B60W 30/14

Abstract:

An advanced driver assistance system (ADAS) and method for a vehicle utilize a light detection and ranging (LIDAR) system configured to emit laser light pulses and capture reflected laser light pulses collectively forming three-dimensional (3D) LIDAR point cloud data and a controller configured to receive the 3D LIDAR point cloud data, convert the 3D LIDAR point cloud data to a two-dimensional (2D) birdview projection, obtain a template image for object detection, the template image being representative of a specific object, blur the 2D birdview projection and the template image to obtain a blurred 2D birdview projection and a blurred template image, and detect the specific object by matching a portion of the blurred 2D birdview projection to the blurred template image.

US Patent:

20200200907, Jun 25, 2020

Filed:

Dec 19, 2018

Appl. No.:

16/224973

Inventors:

Dalong Li - Troy MI, US
Alex Smith - LaSalle, CA
Stephen Horton - Rochester MI, US

International Classification:

G01S 17/89
G01S 17/93
G06K 9/00
G06T 7/70
G06T 17/05
G05D 1/02
G05D 1/00

Abstract:

A semi-automatic three-dimensional light detection and ranging (LIDAR) point cloud data annotation system and method for autonomous driving of a vehicle involve filtering 3D LIDAR point cloud and normalizing the filtered 3D LIDAR point cloud data relative to the vehicle to obtain normalized 3D LIDAR point cloud data, quantizing the normalized 3D LIDAR point cloud data by dividing it into a set of 3D voxels, projecting the set of 3D voxels to a 2D birdview, identifying a possible object by applying clustering to the 2D birdview projection, obtaining an annotated 2D birdview projection including annotations by a human annotator via the annotation system regarding whether the bounding box corresponds to a confirmed object and a type of the confirmed object, and converting the annotated 2D birdview projection to back into annotated 3D LIDAR point cloud data.

US Patent:

20200202548, Jun 25, 2020

Filed:

Dec 19, 2018

Appl. No.:

16/224930

Inventors:

Zijian Wang - Troy MI, US
Stephen Horton - Rochester MI, US

International Classification:

G06T 7/70
G06T 7/50
G06K 9/62
G06T 5/00
G05D 1/02

Abstract:

Advanced driver assistance (ADAS) systems and methods for a vehicle comprise capturing an image using a monocular camera system of the vehicle and detecting a landmark in the image using a deep neural network (DNN) trained with labeled training data including generating a bounding box for the detected landmark, predicting a depth of pixels in the image using a convolutional neural network (CNN) trained with unlabeled training data captured by a stereo camera system, filtering noise by averaging predicted pixel depths for pixels in the region of the bounding box to obtain an average depth value for the detected landmark, determining a coordinate position of the detected landmark using its average depth value, and performing at least one ADAS feature using the determined coordinate position of the detected landmark.