David A Silver

US Patent:

20220198199, Jun 23, 2022

Filed:

Dec 22, 2020

Appl. No.:

17/131232

Inventors:

- Mountain View CA, US
David Harrison Silver - San Carlos CA, US

International Classification:

G06K 9/00
B60W 30/18
G06K 9/32
G06K 9/62

Abstract:

The technology relates to approaches for determining appropriate stopping locations at intersections for vehicles operating in a self-driving mode. While many intersections have stop lines painted on the roadway, many others have no such lines. Even if a stop line is present, the physical location may not match what is in store map data, which may be out of date due to construction or line repainting. Aspects of the technology employ a neural network that utilizes input training data and detected sensor data to perform classification, localization and uncertain estimation processes. Based on these processes, the system is able to evaluate distribution information for possible stop locations. The vehicle uses such information to determine an optimal stop point, which may or may not correspond to a stop line in the map data. This information is also used to update the existing map data, which can be shared with other vehicles.

US Patent:

20230089689, Mar 23, 2023

Filed:

Aug 31, 2022

Appl. No.:

17/900734

Inventors:

- Cupertino CA, US
Gianpaolo FASOLI - Burlingame CA, US
Tyler GENTLES - San Francisco CA, US
Bruno KINDARJI - Paris, FR
Petr KOSTKA - Praha 9, CZ
Rohit NARULA - Mountain View CA, US
David W. SILVER - Los Altos CA, US
Libor SYKORA - P10 Hostivar, CZ
Ka YANG - San Jose CA, US

International Classification:

G06F 21/32

Abstract:

The present disclosure generally relates to digital identification user interfaces.

US Patent:

20230023913, Jan 26, 2023

Filed:

Oct 3, 2022

Appl. No.:

17/958711

Inventors:

- Mountain View CA, US
David Harrison Silver - San Carlos CA, US

International Classification:

G06V 20/56
B60W 30/18
G06K 9/62
G06V 10/25

Abstract:

The technology relates to approaches for determining appropriate stopping locations at intersections for vehicles operating in a self-driving mode. While many intersections have stop lines painted on the roadway, many others have no such lines. Even if a stop line is present, the physical location may not match what is in store map data, which may be out of date due to construction or line repainting. Aspects of the technology employ a neural network that utilizes input training data and detected sensor data to perform classification, localization and uncertain estimation processes. Based on these processes, the system is able to evaluate distribution information for possible stop locations. The vehicle uses such information to determine an optimal stop point, which may or may not correspond to a stop line in the map data. This information is also used to update the existing map data, which can be shared with other vehicles.

US Patent:

20210107494, Apr 15, 2021

Filed:

Oct 8, 2020

Appl. No.:

17/065722

Inventors:

- Mountain View CA, US
David Harrison Silver - San Carlos CA, US

Assignee:

WAYMO LLC - Mountain View CA

International Classification:

B60W 40/09
G01C 21/30
B60W 50/14
G08G 1/16
B60W 60/00
B60W 50/06

Abstract:

The technology relates to identifying and addressing aberrant driver behavior. Various driving operations may be evaluated over different time scales and driving distances. The system can detect driving errors and suboptimal maneuvering, which are evaluated by an onboard driver assistance system and compared against a model of expected driver behavior. The result of this comparison can be used to alert the driver or take immediate corrective driving action. It may also be used for real-time or offline training or sensor calibration purposes. The behavior model may be driver-specific, or may be a nominal driver model based on aggregated information from many drivers. These approaches can be employed with drivers of passenger vehicles, busses, cargo trucks and other vehicles.

US Patent:

20200356794, Nov 12, 2020

Filed:

May 20, 2020

Appl. No.:

16/879299

Inventors:

- Mountain View CA, US
Abhijit S. Ogale - Sunnyvale CA, US
Henrik Kretzschmar - Mountain View CA, US
David Harrison Silver - San Carlos CA, US
Carl Kershaw - San Francisco CA, US
Pankaj Chaudhari - San Francisco CA, US
Chen Wu - Cupertino CA, US
Congcong Li - Cupertino CA, US

International Classification:

G06K 9/00
G05D 1/00
G06N 3/08
G06T 11/20
G06K 9/34
G06F 40/30

Abstract:

Aspects of the disclosure relate to training and using a phrase recognition model to identify phrases in images. As an example, a selected phrase list may include a plurality of phrases is received. Each phrase of the plurality of phrases includes text. An initial plurality of images may be received. A training image set may be selected from the initial plurality of images by identifying the phrase-containing images that include one or more phrases from the selected phrase list. Each given phrase-containing image of the training image set may be labeled with information identifying the one or more phrases from the selected phrase list included in the given phrase-containing images. The model may be trained based on the training image set such that the model is configured to, in response to receiving an input image, output data indicating whether a phrase of the plurality of phrases is included in the input image.

US Patent:

20200189463, Jun 18, 2020

Filed:

Dec 13, 2018

Appl. No.:

16/218926

Inventors:

- Mountain View CA, US
David Harrison Silver - San Carlos CA, US
Christian Lauterbach - Campbell CA, US
Roshni Cooper - Cupertino CA, US

International Classification:

B60R 1/00
G01S 17/89
G01S 17/42
B60W 40/06

Abstract:

The technology relates detecting standing water. In one example, a system comprising one or more processors may be configured to receive sensor data generated by a perception system of a vehicle, wherein the sensor data corresponds to an area surrounding a vehicle. The one or more processors may identify a location in the area where the sensor data is not present and receive map information corresponding to the area, wherein the map information includes road surface locations. The one or more processors may determine that the location corresponds to one or more of the road surface locations in the map information; and output, based upon the determination that the location corresponds to one or more of the road surface locations in the map information, an indication that standing water is at the location.

US Patent:

20190392231, Dec 26, 2019

Filed:

Jun 26, 2018

Appl. No.:

16/018490

Inventors:

- Mountain View CA, US
Abhijit S. Ogale - Sunnyvale CA, US
Henrik Kretzschmar - Mountain View CA, US
David Harrison Silver - San Carlos CA, US
Carl Kershaw - San Francisco CA, US
Pankaj Chaudhari - San Francisco CA, US
Chen Wu - Cupertino CA, US
Congcong Li - Cupertino CA, US

International Classification:

G06K 9/00
G05D 1/00
G06N 3/08
G06F 17/27
G06K 9/34
G06T 11/20

Abstract:

Aspects of the disclosure relate to training and using a phrase recognition model to identify phrases in images. As an example, a selected phrase list may include a plurality of phrases is received. Each phrase of the plurality of phrases includes text. An initial plurality of images may be received. A training image set may be selected from the initial plurality of images by identifying the phrase-containing images that include one or more phrases from the selected phrase list. Each given phrase-containing image of the training image set may be labeled with information identifying the one or more phrases from the selected phrase list included in the given phrase-containing images. The model may be trained based on the training image set such that the model is configured to, in response to receiving an input image, output data indicating whether a phrase of the plurality of phrases is included in the input image.

US Patent:

20180032078, Feb 1, 2018

Filed:

Oct 13, 2016

Appl. No.:

15/292818

Inventors:

- Mountain View CA, US
Andreas Wendel - Mountain View CA, US
Zhinan Xu - Mountain View CA, US
David Harrison Silver - Millbrae CA, US
Brandon Douglas Luders - Sunnyvale CA, US

International Classification:

G05D 1/02
G05D 1/00

Abstract:

Aspects of the disclosure relate to maneuvering a vehicle. As an example, sensor information identifying a set of objects as well as a set of characteristics for each object of the set of objects is received from a perception system of a vehicle. The set of objects is filtered to remove objects corresponding to vehicles, bicycles, and pedestrians. An object within an expected future path of the vehicle is selected from the filtered set of objects. The object is classified as drivable or not drivable based on the set of characteristics. Drivable indicates that the vehicle can drive over the object without causing damage to the vehicle. The vehicle is maneuvered based on the classification such that when the object is classified as drivable, maneuvering the vehicle includes driving the vehicle over the object by not altering the expected future path of the vehicle.