Xiaoyu Y Zhou, 57Cupertino, CA

2023010, Mar 30, 2023

Dec 6, 2022

18/075713

- San Francisco CA, US
Harman Kumar - San Francisco CA, US
Yu-Cheng Lin - San Francisco CA, US
Xiaoyu Zhou - San Francisco CA, US

G01C 21/32
G01C 21/36
G01S 17/88
G05D 1/02

The present technology provides systems, methods, and devices that can update aspects of a map as an autonomous vehicle navigates a route, and therefore avoids the need for dispatching a special purpose mapping vehicle for these updates. As the autonomous vehicle navigates the route, data captured by at least one sensor of an autonomous vehicle can indicate an inconsistency between pre-mapped from a high-resolution sensor system describing a location on a map, and current data describing a new feature of the location. The current data can be clustered together based on a threshold spatial closeness, where the clustering describes the new feature, and semantic labels of the pre-mapped data from the high-resolution sensor system can be updated based on the new feature described by the clustered current data.

2021040, Dec 30, 2021

Jun 29, 2020

16/915468

- San Francisco CA, US
Harman Kumar - San Francisco CA, US
Shreyans Kushwaha - San Francisco CA, US
Xiaoyu Zhou - San Francisco CA, US
Yu-Cheng Lin - San Francisco CA, US

G01C 21/32
G01S 17/89
G05D 1/02

Systems, methods, and computer-readable media are provided for detecting a pavement marking around an autonomous vehicle, comparing the detected pavement marking with a pavement marking present in a semantic data map, determining whether a change has occurred between the detected pavement marking and the pavement marking present in the semantic data map, and updating the semantic data map based on the determining of whether the change has occurred between the detected pavement marking and the pavement marking present in the semantic data map.

2020040, Dec 31, 2020

Jun 28, 2019

16/456385

- San Francisco CA, US
Harman Kumar - San Francisco CA, US
Yu-Cheng Lin - San Francisco CA, US
Xiaoyu Zhou - San Francisco CA, US

G01C 21/32
G01C 21/36
G05D 1/02
G01S 17/88

The present technology provides systems, methods, and devices that can update aspects of a map as an autonomous vehicle navigates a route, and therefore avoids the need for dispatching a special purpose mapping vehicle for these updates. As the autonomous vehicle navigates the route, data captured by at least one sensor of an autonomous vehicle can indicate an inconsistency between pre-mapped from a high-resolution sensor system describing a location on a map, and current data describing a new feature of the location. The current data can be clustered together based on a threshold spatial closeness, where the clustering describes the new feature, and semantic labels of the pre-mapped data from the high-resolution sensor system can be updated based on the new feature described by the clustered current data.

2020040, Dec 31, 2020

Aug 30, 2019

16/557608

- San Francisco CA, US
Harman Kumar - San Francisco CA, US
Yu-Cheng Lin - San Francisco CA, US
Xiaoyu Zhou - San Francisco CA, US

G01C 21/32
G05D 1/02
G01S 17/88
G01C 21/36

The present technology provides systems, methods, and devices that can update aspects of a map as an autonomous vehicle navigates a route, and therefore avoids the need for dispatching a special purpose mapping vehicle for these updates. As the autonomous vehicle navigates the route, data captured by at least one sensor of an autonomous vehicle can indicate an inconsistency between pre-mapped from a high-resolution sensor system describing a location on a map, and current data describing a new feature of the location. A type of the new feature can be classified in accordance with an analysis of a structure of the current data that has been clustered together based on a threshold spatial closeness, and semantic labels of the pre-mapped data from the high-resolution sensor system can be updated based on the new feature described by the clustered current data.