The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Apr. 06, 2021
Filed:
Dec. 30, 2016
Applicant:
Nio Usa, Inc., San Jose, CA (US);
Inventors:
Abhishek Singhal, Santa Clara, CA (US);
Gautam Muralidhar, San Jose, CA (US);
Christopher F. Pouliot, San Mateo, CA (US);
Edward H. Baik, Mountain View, CA (US);
Jonathan A. Cox, San Jose, CA (US);
Assignee:
NIO USA, Inc., San Jose, CA (US);
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 30/02 (2012.01); G06F 16/29 (2019.01); G01S 13/86 (2006.01); G01S 15/02 (2006.01); G08G 1/16 (2006.01); G06F 16/95 (2019.01); B62D 15/02 (2006.01); G01C 21/34 (2006.01); G01S 13/87 (2006.01); B60S 1/56 (2006.01); G01C 21/36 (2006.01); B60R 11/04 (2006.01); B60S 1/62 (2006.01); G01S 7/40 (2006.01); G01S 7/497 (2006.01); G01S 17/89 (2020.01); G02B 27/00 (2006.01); B60W 30/09 (2012.01); B60W 50/00 (2006.01); G05D 1/00 (2006.01); G05D 1/02 (2020.01); A61B 5/01 (2006.01); A61B 5/024 (2006.01); A61B 5/08 (2006.01); A61B 5/16 (2006.01); A61B 5/18 (2006.01); B60W 40/09 (2012.01); B60W 50/08 (2020.01); B60W 10/18 (2012.01); B60W 10/20 (2006.01); B60W 10/04 (2006.01); B60W 40/04 (2006.01); B60W 40/08 (2012.01); B60W 40/105 (2012.01); B62D 15/00 (2006.01); G01S 13/931 (2020.01);
U.S. Cl.
CPC ...
G06Q 30/0266 (2013.01); A61B 5/01 (2013.01); A61B 5/024 (2013.01); A61B 5/08 (2013.01); A61B 5/165 (2013.01); A61B 5/18 (2013.01); B60R 11/04 (2013.01); B60S 1/56 (2013.01); B60S 1/62 (2013.01); B60W 10/04 (2013.01); B60W 10/18 (2013.01); B60W 10/20 (2013.01); B60W 30/09 (2013.01); B60W 40/04 (2013.01); B60W 40/08 (2013.01); B60W 40/09 (2013.01); B60W 40/105 (2013.01); B60W 50/0097 (2013.01); B60W 50/0098 (2013.01); B60W 50/08 (2013.01); B60W 50/082 (2013.01); B62D 15/00 (2013.01); B62D 15/0265 (2013.01); G01C 21/3407 (2013.01); G01C 21/3461 (2013.01); G01C 21/3469 (2013.01); G01C 21/3484 (2013.01); G01C 21/3492 (2013.01); G01C 21/3682 (2013.01); G01C 21/3691 (2013.01); G01C 21/3697 (2013.01); G01S 7/4021 (2013.01); G01S 7/497 (2013.01); G01S 13/862 (2013.01); G01S 13/865 (2013.01); G01S 13/867 (2013.01); G01S 13/87 (2013.01); G01S 15/02 (2013.01); G01S 17/89 (2013.01); G02B 27/0006 (2013.01); G05D 1/0061 (2013.01); G05D 1/0088 (2013.01); G05D 1/0212 (2013.01); G05D 1/0214 (2013.01); G05D 1/0221 (2013.01); G05D 1/0276 (2013.01); G06F 16/29 (2019.01); G06F 16/95 (2019.01); G06Q 30/0269 (2013.01); G08G 1/161 (2013.01); G08G 1/163 (2013.01); G08G 1/164 (2013.01); G08G 1/165 (2013.01); G08G 1/166 (2013.01); B60W 2040/0809 (2013.01); B60W 2050/0004 (2013.01); B60W 2050/0014 (2013.01); B60W 2300/34 (2013.01); B60W 2510/08 (2013.01); B60W 2510/18 (2013.01); B60W 2520/04 (2013.01); B60W 2520/105 (2013.01); B60W 2540/043 (2020.02); B60W 2540/18 (2013.01); B60W 2540/22 (2013.01); B60W 2540/30 (2013.01); B60W 2554/00 (2020.02); B60W 2554/80 (2020.02); B60W 2710/18 (2013.01); B60W 2710/20 (2013.01); B60W 2756/10 (2020.02); B60W 2900/00 (2013.01); G01S 2007/4043 (2013.01); G01S 2007/4977 (2013.01); G01S 2013/932 (2020.01); G01S 2013/9316 (2020.01); G01S 2013/9318 (2020.01); G01S 2013/9319 (2020.01); G01S 2013/9322 (2020.01); G01S 2013/9325 (2013.01); G01S 2013/93185 (2020.01); G01S 2013/93271 (2020.01); G01S 2013/93272 (2020.01); G01S 2013/93273 (2020.01); G01S 2013/93274 (2020.01); G01S 2013/93275 (2020.01); G05D 2201/0212 (2013.01);
Abstract
Embodiments herein can determine an optimal route for an autonomous electric vehicle. The system may score viable routes between the start and end locations of a trip using a numeric or other scale that denotes how viable the route is for autonomy. The score is adjusted using a variety of factors where a learning process leverages both offline and online data. The scored routes are not based simply on the shortest distance between the start and end points but determine the best route based on the driving context for the vehicle and the user.