Aashish R Patel

US Patent:

20210004085, Jan 7, 2021

Filed:

Jun 2, 2020

Appl. No.:

16/890406

Inventors:

- Malibu CA, US
Aashish Patel - Malibu CA, US
Michael D. Howard - Westlake Village CA, US
Praveen K. Pilly - Tarzana CA, US
Jaehoon Choe - Agoura Hills CA, US

International Classification:

G06F 3/01
G06N 3/08
A61B 5/0482

Abstract:

A brain-machine interface system configured to decode neural signals to control a target device includes a sensor to sample the neural signals, and a computer-readable storage medium having software instructions, which, when executed by a processor, cause the processor to transform the neural signals into a common representational space stored in the system, provide the common representational space as a state representation to inform an Actor recurrent neural network policy of the system, generate and evaluate, utilizing a deep recurrent neural network of the system having a generative sequence decoder, predictive sequences of control signals, supply a control signal to the target device to achieve an output of the target device, determine an intrinsic biometric-based reward signal, from the common representational space, based on an expectation of the output of the target device, and supply the intrinsic biometric-based reward signal to a Critic model of the system.

US Patent:

20200307564, Oct 1, 2020

Filed:

Mar 26, 2019

Appl. No.:

16/365078

Inventors:

- Detroit MI, US
Aashish N. Patel - Los Angeles CA, US
Rajan Bhattacharyya - Sherman Oaks CA, US
Srinivas Nedunuri - Santa Monica CA, US

International Classification:

B60W 30/09
B60W 30/095
G06N 3/04
G06N 3/08
B60W 30/14
B60W 30/18
G08G 1/16

Abstract:

Embodiments include methods, systems and computer readable storage medium for a method for collision avoidance by a vehicle is disclosed. The method includes installing a vehicle system into a vehicle, wherein the vehicle system provides collision avoidance guidance based on training data using movement information from one or more agents and behaviors associated with one or more individuals associated with the one or more agents or the vehicle. The method further includes detecting, by a processor, a collision course between the vehicle and the one or more mobile agents and/or one or more stationary agents. The method further includes calculating, by the processor, one or more decisions that avoid a collision in response to detecting a collision course. The method further includes selecting, by the processor, a decision from the one or more decisions and controlling, by the processor, operation of the vehicle based on the selected decision.

US Patent:

20200307574, Oct 1, 2020

Filed:

Mar 26, 2019

Appl. No.:

16/365059

Inventors:

- Detroit MI, US
Aashish N. Patel - Los Angeles CA, US
Rajan Bhattacharyya - Sherman Oaks CA, US

International Classification:

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

Abstract:

Embodiments include methods, systems, and computer readable storage medium for a method for providing path-planning guidance by resolving multiple behavioral predictions associated with operating a vehicle is disclosed. The method includes installing a vehicle system into a vehicle, wherein the vehicle system provides path planning guidance based on training data using and fused hypotheses and/or decisions associated with the training data. The method further includes determining, by a processor, a location of the vehicle on a map containing a road network, and determining, by the processor, whether one or more agents exist within a predetermined range of the vehicle. The method further includes selecting, by the processor, an output trajectory to traverse the road network based on the location of the vehicle on the map and the existence of one or more agents. The method further includes controlling, by the processor, operation of the vehicle using the output trajectory.

US Patent:

20200310421, Oct 1, 2020

Filed:

Mar 26, 2019

Appl. No.:

16/365490

Inventors:

- Detroit MI, US
Aashish N. Patel - Los Angeles CA, US
Michael J. Daily - Thousand Oaks CA, US

International Classification:

G05D 1/00
G05D 1/02

Abstract:

An autonomous vehicle, system and method of operating the autonomous vehicle. The system includes a performance evaluator, a decision module and a navigation system. The performance evaluator determines a performance grade for each of a plurality of decisions for operating the autonomous vehicle. The decision module selects a decision have a greatest performance grade. The navigation system operates the autonomous vehicle using the selected decision.