Samuel R Anthony

US Patent:

20210356968, Nov 18, 2021

Filed:

May 14, 2021

Appl. No.:

17/321309

Inventors:

- Boston MA, US
Till S. Hartmann - Brookline MA, US
Samuel English Anthony - Somerville MA, US

International Classification:

G05D 1/02
G06K 9/00
G06K 9/62
G06K 9/46
B60W 60/00
G06N 20/00
G06N 5/04

Abstract:

A system uses a machine learning based model to determine attributes describing states of mind and behavior of traffic entities in video frames captured by an autonomous vehicle. The system classifies video frames according to traffic scenarios depicted, where each scenario is associated with a filter based on vehicle attributes, traffic attributes, and road attributes. The system identifies a set of video frames associated with ground truth scenarios for validating the accuracy of the machine learning based model and predicts attributes of traffic entities in the video frames. The system analyzes video frames captured after the set of video frames to determine actual attributes of the traffic entities. Based on a comparison of the predicted attributes and actual attributes, the system determines a likelihood of the machine learning based model making accurate predictions and uses the likelihood to generate a navigation action table for controlling the autonomous vehicle.

US Patent:

20210357662, Nov 18, 2021

Filed:

May 14, 2021

Appl. No.:

17/321297

Inventors:

- Boston MA, US
Jeffrey D. Zaremba - Cambridge MA, US
Samuel English Anthony - Somerville MA, US

International Classification:

G06K 9/00
B60W 60/00
G06N 3/08

Abstract:

A system uses a machine learning based model to determine attributes describing states of mind and behavior of traffic entities in video frames captured by an autonomous vehicle. The system classifies video frames according to traffic scenarios depicted, where each scenario is associated with a filter based on vehicle attributes, traffic attributes, and road attributes. The system identifies a set of video frames associated with ground truth scenarios for validating the accuracy of the machine learning based model and predicts attributes of traffic entities in the video frames. The system analyzes video frames captured after the set of video frames to determine actual attributes of the traffic entities. Based on a comparison of the predicted attributes and actual attributes, the system determines a likelihood of the machine learning based model making accurate predictions and uses the likelihood to generate a navigation action table for controlling the autonomous vehicle.

US Patent:

20200247432, Aug 6, 2020

Filed:

Dec 10, 2019

Appl. No.:

16/709788

Inventors:

- Somerville MA, US
Samuel English Anthony - Cambridge MA, US

International Classification:

B60W 60/00
G06K 9/00
G08G 1/01
G06N 3/08

Abstract:

A system performs modeling and simulation of non-stationary traffic entities for testing and development of modules used in an autonomous vehicle system. The system uses a machine learning based model that predicts hidden context attributes for traffic entities that may be encountered by a vehicle in traffic. The system generates simulation data for testing and development of modules that help navigate autonomous vehicles. The generated simulation data may be image or video data including representations of traffic entities, for example, pedestrians, bicyclists, and other vehicles. The system may generate simulation data using generative adversarial neural networks.

US Patent:

20200249677, Aug 6, 2020

Filed:

Feb 6, 2020

Appl. No.:

16/783845

Inventors:

- Boston MA, US
Samuel English Anthony - Somerville MA, US

International Classification:

G05D 1/00
G06N 7/00
G06N 3/08
G05D 1/02
B60W 60/00

Abstract:

An autonomous vehicle uses probabilistic neural networks to predict hidden context attributes associated with traffic entities. The hidden context represents behavior of the traffic entities in the traffic. The probabilistic neural network is configured to receive an image of traffic as input and generate output representing hidden context for a traffic entity displayed in the image. The system executes the probabilistic neural network to generate output representing hidden context for traffic entities encountered while navigating through traffic. The system determines a measure of uncertainty for the output values. The autonomous vehicle uses the measure of uncertainty generated by the probabilistic neural network during navigation.

US Patent:

20200239026, Jul 30, 2020

Filed:

Jan 30, 2020

Appl. No.:

16/777673

Inventors:

- Boston MA, US
Samuel English Anthony - Somerville MA, US

International Classification:

B60W 60/00
G05D 1/02

Abstract:

An autonomous vehicle uses machine learning based models to predict hidden context attributes associated with traffic entities. The system uses the hidden context to predict behavior of people near a vehicle in a way that more closely resembles how human drivers would judge the behavior. The system determines an activation threshold value for a braking system of the autonomous vehicle based on the hidden context. The system modifies a world model based on the hidden context predicted by the machine learning based model. The autonomous vehicle is safely navigated, such that the vehicle stays at least a threshold distance away from traffic entities.

US Patent:

20200241545, Jul 30, 2020

Filed:

Jan 30, 2020

Appl. No.:

16/777386

Inventors:

- Boston MA, US
Samuel English Anthony - Somerville MA, US

International Classification:

G05D 1/02
B60W 30/095
B60W 30/09
G05D 1/00
B60W 40/09
G06N 20/00

Abstract:

An autonomous vehicle uses machine learning based models to predict hidden context attributes associated with traffic entities. The system uses the hidden context to predict behavior of people near a vehicle in a way that more closely resembles how human drivers would judge the behavior. The system determines an activation threshold value for a braking system of the autonomous vehicle based on the hidden context. The system modifies a world model based on the hidden context predicted by the machine learning based model. The autonomous vehicle is safely navigated, such that the vehicle stays at least a threshold distance away from traffic entities.

US Patent:

20200242416, Jul 30, 2020

Filed:

Apr 7, 2020

Appl. No.:

16/842101

Inventors:

David COX - Somerville MA, US
Walter SCHEIRER - Somerville MA, US
Samuel ANTHONY - Cambridge MA, US
Ken NAKAYAMA - Cambridge MA, US

International Classification:

G06K 9/62
G06K 9/00

Abstract:

In various embodiments, training objects are classified by human annotators, psychometric data characterizing the annotation of the training objects is acquired, a human-weighted loss function based at least in part on the classification data and the psychometric data is computationally derived, and one or more features of a query object are computationally classifies based at least in part on the human-weighted loss function.

US Patent:

20200180647, Jun 11, 2020

Filed:

Dec 10, 2019

Appl. No.:

16/709790

Inventors:

- Somerville MA, US
Samuel English Anthony - Cambridge MA, US

International Classification:

B60W 50/00
G05D 1/00
B60W 40/04

Abstract:

A system performs modeling and simulation of non-stationary traffic entities for testing and development of modules used in an autonomous vehicle system. The system uses a machine learning based model that predicts hidden context attributes for traffic entities that may be encountered by a vehicle in traffic. The system generates simulation data for testing and development of modules that help navigate autonomous vehicles. The generated simulation data may be image or video data including representations of traffic entities, for example, pedestrians, bicyclists, and other vehicles. The system may generate simulation data using generative adversarial neural networks.