Jinesh J Jain

US Patent:

20220405573, Dec 22, 2022

Filed:

Jun 18, 2021

Appl. No.:

17/351404

Inventors:

- Dearborn MI, US
Nikita Jaipuria - Union City CA, US
Jinesh Jain - Pacifica CA, US

Assignee:

Ford Global Technologies, LLC - Dearborn MI

International Classification:

G06N 3/08
G06N 3/04
H04L 29/08

Abstract:

A first computer can operate a first instance of a neural network, receive a first data set input to the first instance of the neural network, determine a first calibration parameter for the neural network in the first instance of the neural network based on the first data set, and send the first calibration parameter to a server computer. A second computer can operate a second instance of the neural network, receive a second data set input to the second instance of the neural network, determine a second calibration parameter for the neural network in the second instance of the neural network based on the second data set, and send the second calibration parameter to the server computer. A server computer can aggregate the first and second calibration parameters to update a model of the neural network and update the neural network model for the first and second instances of the neural network at the first and second computers based on the aggregated first and second calibration parameters.

US Patent:

20220234617, Jul 28, 2022

Filed:

Jan 26, 2021

Appl. No.:

17/158088

Inventors:

- Dearborn MI, US
Sowndarya Sundar - Mountain View CA, US
Jinesh Jain - Pacifica CA, US

Assignee:

Ford Global Technologies, LLC - Dearborn MI

International Classification:

B60W 60/00
G06F 16/28
G06N 3/04
G06N 3/08
B60W 50/00
B60W 30/095
B60W 30/09
B60W 40/06

Abstract:

A system comprising a computer including a processor and a memory, the memory including instructions such that the processor is programmed to: process vehicle sensor data with a deep neural network to generate a prediction indicative of one or more objects based on the data and determine an object uncertainty corresponding to the prediction and when the object uncertainty is greater than an uncertainty threshold, segment the vehicle sensor data into a foreground portion and a background portion. Classify the foreground portion as including an unseen object class when a foreground uncertainty is greater than a foreground uncertainty threshold; classify the background portion as including unseen background when a background uncertainty is greater than a background uncertainty threshold; and transmit the data and a data classification to a server.

US Patent:

20220207348, Jun 30, 2022

Filed:

Dec 29, 2020

Appl. No.:

17/136565

Inventors:

- Dearborn MI, US
Jinesh Jain - Pacifica CA, US

Assignee:

Ford Global Technologies, LLC - Dearborn MI

International Classification:

G06N 3/08
G06N 3/04
G06N 20/10
B60W 50/00
B60W 10/04
B60W 10/18
B60W 10/20

Abstract:

A system comprising a computer including a processor and a memory, the memory including instructions such that the processor is programmed to: determine whether a difference between a friction coefficient label and a determined friction coefficient corresponding to an image depicting a surface is greater than a label threshold; modify the determined friction coefficient to equal the friction coefficient label when the difference is greater than the label threshold; and retrain a neural network using the image and the friction coefficient label.

US Patent:

20230128947, Apr 27, 2023

Filed:

Oct 25, 2021

Appl. No.:

17/509227

Inventors:

- Dearborn MI, US
Nikita Jaipuria - Union City CA, US
Jinesh Jain - Pacifica CA, US
Vidya Nariyambut Murali - Sunnyvale CA, US

Assignee:

Ford Global Technologies, LLC - Dearborn MI

International Classification:

B60W 40/04
B60W 30/09
G06T 7/50
G06T 7/20

Abstract:

Image data are input to a machine learning program. The machine learning program is trained with a virtual boundary model based on a distance between a host vehicle and a target object and a loss function based on a real-world physical model. An identification of a threat object is output from the machine learning program. A subsystem of the host vehicle is actuated based on the identification of the threat object.

US Patent:

20210256321, Aug 19, 2021

Filed:

Feb 14, 2020

Appl. No.:

16/791084

Inventors:

- Dearborn MI, US
Jinhyoung Oh - Union City CA, US
Jinesh Jain - Palo Alto CA, US

Assignee:

Ford Global Technologies, LLC - Dearborn MI

International Classification:

G06K 9/62
G06K 9/00
B60W 50/00
G05D 1/02

Abstract:

A computer includes a processor and a memory storing instructions executable by the processor to collect a plurality of data sets, each data set from a respective sensor in a plurality of sensors, and each data set including a range, an azimuth angle, and a range rate for a detection point of the respective one of the sensors on an object to determine, for each detection point, a radial component of a ground speed of the detection point based on the data set associated with the detection point and a speed of a vehicle, and to generate a plurality of clusters, each cluster including selected detection points within a distance threshold from each other and having respective radial components of ground speeds that are (1) above a first threshold and (2) within a second threshold of each other.

US Patent:

20200348687, Nov 5, 2020

Filed:

May 3, 2019

Appl. No.:

16/402367

Inventors:

- Dearborn MI, US
Jinesh Jain - Palo Alto CA, US
Gaurav Pandey - Foster City CA, US

Assignee:

Ford Global Technologies, LLC - Dearborn MI

International Classification:

G05D 1/02
B60W 30/09
B60W 30/095

Abstract:

A vehicle system includes a processor and a memory. The memory stores instructions executable by the processor to identify an area of interest from a plurality of areas on a map, to determine that a detected sound is received in a vehicle audio sensor upon determining that a source of the sound is within the area of interest and not another area in the plurality of areas, and to operate the vehicle based at least in part on the detected sound.

US Patent:

20200160070, May 21, 2020

Filed:

Nov 21, 2018

Appl. No.:

16/198334

Inventors:

- Dearborn MI, US
Jinesh J. Jain - Palo Alto CA, US
Gintaras Vincent Puskorius - Novi MI, US
Leda Daehler - Dearborn MI, US

International Classification:

G06K 9/00
G06N 3/04
G06T 7/73
G06K 9/62
G05D 1/02

Abstract:

A computing system can crop an image based on a width, height and location of a first vehicle in the image. The computing system can estimate a pose of the first vehicle based on inputting the cropped image and the width, height and location of the first vehicle into a deep neural network. The computing system can then operate a second vehicle based on the estimated pose. The computing system may train a model to identify a type and a location of a hazard according to the estimated pose, the hazard being such things as ice, mud, pothole, or other hazard. The model may be used by an autonomous vehicle to identify and avoid hazards or to provide drive assistance alerts.

US Patent:

20200082034, Mar 12, 2020

Filed:

Sep 12, 2018

Appl. No.:

16/129145

Inventors:

- Dearborn MI, US
Sravani Yajamanam Kidambi - Sunnyvale CA, US
Vidya Nariyambut Murali - Sunnyvale CA, US
Jinesh Jain - Palo Alto CA, US

International Classification:

G06F 17/50
G06N 99/00

Abstract:

The present invention extends to methods, systems, and computer program products for evaluating autonomous vehicle algorithms. Aspects use (e.g., supervised) machine learning techniques to analyze performance of autonomous vehicle algorithms on real world and simulated data. Machine learning techniques can be used to identify scenario features that are more likely to influence algorithm performance. Machine learning techniques can also be used to consolidate insights and automate the generation of relevant test cases over multiple iterations to identify error-prone scenarios.