IP Library Granted Patent US 12,293,668
Granted Patent B2
US 12,293,668 · App. 18/618,476 · Granted May 6, 2025

Platooning method, apparatus and system of autonomous driving platoon

Inventors: Wenrui Li (Beijing, CN); Nan Wu (Beijing, CN); Rui Peng (Beijing, CN); Qingxin Bi (Beijing, CN); Yuhe Jin (Beijing, CN); Yiming Li (Beijing, CN)
Assignee: BEIJING TUSEN ZHITU TECHNOLOGY CO., LTD.
G08G1/22B60K35/00B60W30/165B60W50/00B60W50/14B60W60/0025G07C5/085G08G1/20H04W4/46H04W76/10B60K35/28B60K2360/166B60W2050/146B60W2554/4041B60W2554/802B60W2556/45
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,293,668
App. No.
18/618,476
Granted
May 6, 2025
Kind
B2
Abstract

The present disclosure provides a method and a server for platooning. The method provides: obtaining, based on a platooning request message, a first vehicle type and first kinematic information of a vehicle to join a platoon, a second vehicle type and second kinematic information of a current tail of the platoon, first sensor operating status information of the vehicle to join the platoon, and second sensor operating status information of the current tail vehicle; performing vehicle kinematic determination to obtain a kinematic determination result; performing sensor determination to obtain a sensor determination result; transmitting a confirmation request message to a vehicle of the platoon when the determination results are both successful; and controlling, upon receiving a request approval message, the vehicle to join the platoon to establish a V2V communication connection with each vehicle in the platoon. The method can achieve platoon without any road side unit.

Claims (80)

1. A method for platooning, comprising:

receiving, from a first vehicle, a platooning request message for joining a platoon;

obtaining, based on the platooning request message, a first vehicle type, first kinematic information and first sensor operating status information of the first vehicle, a second vehicle type, second kinematic information and second sensor operating status information of a second vehicle currently at a tail of the platoon, comprising:

retrieving a vehicle registration message of the first vehicle based on a vehicle electronic tag of the first vehicle, to obtain the first vehicle type and the first kinematic information of the first vehicle, the first kinematic information comprising a minimum turning radius and a braking preparation time length of the first vehicle, wherein the platooning request message comprises the vehicle electronic tag of the first vehicle;

obtaining a vehicle electronic tag of the second vehicle currently at the tail; and

retrieving a vehicle registration message of the second vehicle currently at the tail, to obtain the second vehicle type and the second kinematic information of the second vehicle currently at the tail, the second kinematic information comprising a minimum turning radius and a braking preparation time length of the second vehicle currently at the tail;

performing vehicle kinematic determination based on the first vehicle type, the first kinematic information, the second vehicle type, and the second kinematic information, to obtain a kinematic determination result;

performing sensor determination based on the first sensor operating status information and the second sensor operating status information, to obtain a sensor determination result;

transmitting a confirmation request message to a third vehicle in response to the kinematic determination result and the sensor determination result meeting a first predetermined condition, wherein the third vehicle is in the platoon; and

controlling, upon receiving a request approval message from the third vehicle, the first vehicle to establish a Vehicle-to-Vehicle (V2V) communication connection with each vehicle in the platoon.

2. The method of claim 1 , further comprising:

receiving, from a fourth vehicle, a vehicle registration message for joining the platoon, the vehicle registration message containing a vehicle license plate number, a third vehicle type, a vehicle electronic tag, vehicle driver information, vehicle power system information, and vehicle sensor information; and

allowing the fourth vehicle to join the platoon in response to the vehicle registration message meeting a second predetermined condition.

3. The method of claim 2 , further comprising:

receiving position information, destination information, and platoon information from the fourth vehicle; and

determining navigation path information for the fourth vehicle based on the position information and the destination information.

4. The method of claim 3 , further comprising:

transmitting, to the first vehicle, vehicle display information for the fourth vehicle, the vehicle display information containing the vehicle license plate number, the third vehicle type, the position information, the destination information, and the platoon information of the fourth vehicle and a distance for the fourth vehicle to move together with the first vehicle.

5. The method of claim 4 , wherein the vehicle display information comprises a plurality of pieces of vehicle display information, and the platooning request message comprises a selected one of the plurality of pieces of vehicle display information, and receiving, from the first vehicle, the platooning request message comprises:

in response to the platoon information in the selected piece of vehicle display information being empty, determining that the second vehicle currently at the tail of the platoon corresponds to the selected piece of vehicle display information; and

in response to the platoon information in the selected piece of vehicle display information being not empty, determining the platoon based on the platoon information.

6. The method of claim 1 , wherein performing the vehicle kinematic determination based on the first vehicle type, the first kinematic information, the second vehicle type, and the second kinematic information, to obtain the kinematic determination result comprises:

obtaining a first vehicle level to which the first vehicle type corresponds and a second vehicle level to which the second vehicle type corresponds based on the first vehicle type and the second vehicle type, respectively, wherein the first vehicle level is associated with a first vehicle volume and a first vehicle weight, the second vehicle level is associated with a second vehicle volume and a second vehicle weight;

determining whether the first vehicle level is lower than or equal to the second vehicle level;

comparing the minimum turning radius of the first vehicle with the minimum turning radius of the second vehicle currently at the tail;

comparing the braking preparation time length of the first vehicle with the braking preparation time length of the second vehicle currently at the tail; and

determining the kinematic determination result meets the first predetermined condition in response to the first vehicle level being lower than or equal to the second vehicle level, the minimum turning radius of the first vehicle being smaller than or equal to the minimum turning radius of the second vehicle currently at the tail, and the braking preparation time length of the first vehicle being smaller than or equal to the braking preparation time length of the second vehicle currently at the tail.

7. The method of claim 1 , wherein performing sensor determination based on the first sensor operating status information and the second sensor operating status information, to obtain the sensor determination result comprises:

determining whether an operating status of a minimum perception information sensor on the first vehicle and an operating status of a minimum perception information sensor on the second vehicle currently at the tail are operating normally or operating with redundancy, whether an operating status and accuracy of a positioning sensor on the first vehicle and an operating status and accuracy of a positioning sensor on the second vehicle currently at the tail are normal, and whether an operating status and accuracy of a forward distance sensor on the first vehicle and an operating status and accuracy of a forward distance sensor on the second vehicle currently at the tail are normal; and

determining the sensor determination result meets the first predetermined condition in response to the operating status of the minimum perception information sensor on the first vehicle and the operating status of the minimum perception information sensor on the second vehicle currently at the tail being operating normally or operating with redundancy, the operating status and accuracy of the positioning sensor on the first vehicle and the operating status and accuracy of the positioning sensor on the second vehicle currently at the tail being normal, and the operating status and accuracy of the forward distance sensor on the first vehicle and the operating status and accuracy of the forward distance sensor on the second vehicle currently at the tail being normal.

8. A method for platooning, comprising:

receiving, from a first vehicle, a platooning request message for joining a platoon;

obtaining, based on the platooning request message, a first vehicle type, first kinematic information and first sensor operating status information of the first vehicle, and a second vehicle type, second kinematic information and second sensor operating status information of a second vehicle currently at a tail of the platoon, comprising:

retrieving a vehicle registration message of the first vehicle based on a vehicle electronic tag of the first vehicle, to obtain the first vehicle type and the first kinematic information of the first vehicle, the first kinematic information comprising a minimum turning radius and a braking preparation time length of the first vehicle, wherein the platooning request message comprises the vehicle electronic tag of the first vehicle;

obtaining a vehicle electronic tag of the second vehicle currently at the tail; and

retrieving a vehicle registration message of the second vehicle currently at the tail based on the vehicle electronic tag of the second vehicle currently at the tail, to obtain the second vehicle type and the second kinematic information of the second vehicle currently at the tail, the second kinematic information comprising a minimum turning radius and a braking preparation time length of the second vehicle currently at the tail;

performing vehicle kinematic determination, based on the first vehicle type, the first kinematic information, the second vehicle type, and the second kinematic information, to obtain a kinematic determination result;

performing sensor determination, based on the first sensor operating status information and the second sensor operating status information, to obtain a sensor determination result; and

establishing a Vehicle-to-Vehicle (V2V) communication connection with the first vehicle in response to the kinematic determination result and the sensor determination result meeting a first predetermined condition.

9. The method of claim 8 , wherein performing the vehicle kinematic determination, based on the first vehicle type, the first kinematic information, the second vehicle type, and the second kinematic information, to obtain the kinematic determination result comprises:

obtaining a first vehicle level to which the first vehicle type corresponds and a second vehicle level to which the second vehicle type corresponds based on the first vehicle type and the second vehicle type, respectively, wherein the first vehicle level is associated with a first vehicle volume and a first vehicle weight, the second vehicle level is associated with a second vehicle volume and a second vehicle weight;

determining whether the first vehicle level is lower than or equal to the second vehicle level;

comparing the minimum turning radius of the first vehicle with the minimum turning radius of the second vehicle currently at the tail;

comparing the braking preparation time length of the first vehicle with the braking preparation time length of the second vehicle currently at the tail; and

determining the kinematic determination result meets the first predetermined condition in response to the first vehicle level being lower than or equal to the second vehicle level, the minimum turning radius of the first vehicle being smaller than or equal to the minimum turning radius of the second vehicle currently at the tail, and the braking preparation time length of the first vehicle being smaller than or equal to the braking preparation time length of the second vehicle currently at the tail.

10. The method of claim 8 , wherein performing sensor determination, based on the first sensor operating status information and the second sensor operating status information, to obtain the sensor determination result comprises:

determining whether an operating status of a minimum perception information sensor on the first vehicle and an operating status of a minimum perception information sensor on the second vehicle currently at the tail are operating normally or operating with redundancy, whether an operating status and accuracy of a positioning sensor on the first vehicle and an operating status and accuracy of a positioning sensor on the second vehicle currently at the tail are normal, and whether an operating status and accuracy of a forward distance sensor on the first vehicle and an operating status and accuracy of a forward distance sensor on the second vehicle currently at the tail are normal; and

determining the sensor determination result meets the first predetermined condition in response to the operating status of the minimum perception information sensor on the first vehicle and the operating status of the minimum perception information sensor on the second vehicle currently at the tail being operating normally or operating with redundancy, the operating status and accuracy of the positioning sensor on the first vehicle and the operating status and accuracy of the positioning sensor on the second vehicle currently at the tail being normal, and the operating status and accuracy of the forward distance sensor on the first vehicle and the operating status and accuracy of the forward distance sensor on the second vehicle currently at the tail being normal.

11. A computing apparatus, comprising:

a processor; and

a memory storing program codes that, when executed by the processor, causes the apparatus to implement a method comprising:

receiving, from a first vehicle, a platooning request message for joining a platoon;

obtaining, based on the platooning request message, a first vehicle type, first kinematic information and first sensor operating status information of the first vehicle, a second vehicle type, second kinematic information and second sensor operating status information of a second vehicle currently at a tail of the platoon, comprising:

retrieving a vehicle registration message of the first vehicle based on a vehicle electronic tag of the first vehicle, to obtain the first vehicle type and the first kinematic information of the first vehicle, the first kinematic information comprising a minimum turning radius and a braking preparation time length of the first vehicle, wherein the platooning request message comprises the vehicle electronic tag of the first vehicle;

obtaining a vehicle electronic tag of the second vehicle currently at the tail; and

retrieving a vehicle registration message of the second vehicle currently at the tail, to obtain the second vehicle type and the second kinematic information of the second vehicle currently at the tail, the second kinematic information comprising a minimum turning radius and a braking preparation time length of the second vehicle currently at the tail;

performing vehicle kinematic determination based on the first vehicle type, the first kinematic information, the second vehicle type, and the second kinematic information, to obtain a kinematic determination result;

performing sensor determination based on the first sensor operating status information and the second sensor operating status information, to obtain a sensor determination result;

transmitting a confirmation request message to a third vehicle in response to the kinematic determination result and the sensor determination result meeting a first predetermined condition, wherein the third vehicle is in the platoon; and

controlling, upon receiving a request approval message from the third vehicle, the first vehicle to establish a Vehicle-to-Vehicle (V2V) communication connection with each vehicle in the platoon.

12. The computing apparatus of claim 11 , wherein the program codes, when executed by the processor, causes the apparatus to implement the method further comprising:

receiving, from a fourth vehicle, a vehicle registration message for joining the platoon, the vehicle registration message containing a vehicle license plate number, a third vehicle type, a vehicle electronic tag, vehicle driver information, vehicle power system information, and vehicle sensor information; and

allowing the fourth vehicle to join the platoon in response to the vehicle registration message meeting a second predetermined condition.

13. The computing apparatus of claim 12 , wherein the program codes, when executed by the processor, causes the apparatus to implement the method further comprising:

receiving position information, destination information, and platoon information from the fourth vehicle;

determining navigation path information for the fourth vehicle based on the position information and the destination information.

14. The computing apparatus of claim 13 , wherein the program codes, when executed by the processor, causes the apparatus to implement the method further comprising:

transmitting, to the first vehicle, vehicle display information for the fourth vehicle, the vehicle display information containing the vehicle license plate number, the third vehicle type, the position information, the destination information, and the platoon information of the fourth vehicle and a distance for the fourth vehicle to move together with the first vehicle.

15. The computing apparatus of claim 14 , wherein the vehicle display information comprises a plurality of pieces of vehicle display information, and the platooning request message comprises a selected one of the plurality of pieces of vehicle display information, and receiving, from the first vehicle, the platooning request message comprises:

in response to the platoon information in the selected piece of vehicle display information being empty, determining that the second vehicle currently at the tail of the platoon corresponds to the selected piece of vehicle display information; and

in response to the platoon information in the selected piece of vehicle display information being not empty, determining the platoon based on the platoon information.

16. The computing apparatus of claim 11 , wherein performing the vehicle kinematic determination based on the first vehicle type, the first kinematic information, the second vehicle type, and the second kinematic information, to obtain the kinematic determination result comprises:

obtaining a first vehicle level to which the first vehicle type corresponds and a second vehicle level to which the second vehicle type corresponds based on the first vehicle type and the second vehicle type, respectively, wherein the first vehicle level is associated with a first vehicle volume and a first vehicle weight, the second vehicle level is associated with a second vehicle volume and a second vehicle weight;

determining whether the first vehicle level is lower than or equal to the second vehicle level;

comparing the minimum turning radius of the first vehicle with the minimum turning radius of the second vehicle currently at the tail;

comparing the braking preparation time length of the first vehicle with the braking preparation time length of the second vehicle currently at the tail; and

determining the kinematic determination result meets the first predetermined condition in response to the first vehicle level being lower than or equal to the second vehicle level, the minimum turning radius of the first vehicle being smaller than or equal to the minimum turning radius of the second vehicle currently at the tail, and the braking preparation time length of the first vehicle being smaller than or equal to the braking preparation time length of the second vehicle currently at the tail.

17. The computing apparatus of claim 11 , wherein performing sensor determination based on the first sensor operating status information and the second sensor operating status information, to obtain the sensor determination result comprises:

determining whether an operating status of a minimum perception information sensor on the first vehicle and an operating status of a minimum perception information sensor on the second vehicle currently at the tail are operating normally or operating with redundancy, whether an operating status and accuracy of a positioning sensor on the first vehicle and an operating status and accuracy of a positioning sensor on the second vehicle currently at the tail are normal, and whether an operating status and accuracy of a forward distance sensor on the first vehicle and an operating status and accuracy of a forward distance sensor on the second vehicle currently at the tail are normal; and

determining the sensor determination result meets the first predetermined condition in response to the operating status of the minimum perception information sensor on the first vehicle and the operating status of the minimum perception information sensor on the second vehicle currently at the tail being operating normally or operating with redundancy, the operating status and accuracy of the positioning sensor on the first vehicle and the operating status and accuracy of the positioning sensor on the second vehicle currently at the tail being normal, and the operating status and accuracy of the forward distance sensor on the first vehicle and the operating status and accuracy of the forward distance sensor on the second vehicle currently at the tail being normal.

Assignments (2)
CHANGE OF NAME Recorded Dec 9, 2025
From: BEIJING TUSEN ZHITU TECHNOLOGY CO., LTD.
To: BEIJING OCGEN TECHNOLOGY CO., LTD.
Reel/Frame 073916/0416 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 27, 2024
From: LI, WENRUI; WU, NAN; PENG, RUI; BI, QINGXIN; JIN, YUHE; LI, YIMING
To: BEIJING TUSEN ZHITU TECHNOLOGY CO., LTD.
Reel/Frame 066925/0700 →
Priority Claims (1)
CN 201811530975.X · Dec 14, 2018 · national
Continuity (3)
Continuation 17347099 · Jun 14, 2021
Continuation PCTCN2019077076 · Mar 6, 2019
Related Publication 20240242614A1 · Jul 18, 2024
References Cited (383)
US 6084870A · Wooten et al. · 2000 [cited by applicant]
US 6263088B1 · Crabtree · 2001 [cited by applicant]
US 6594821B1 · Banning et al. · 2003 [cited by applicant]
US 6777904B1 · Degner · 2004 [cited by applicant]
US 6975923B2 · Spriggs · 2005 [cited by applicant]
US 7103460B1 · Breed · 2006 [cited by applicant]
US 7689559B2 · Canright · 2010 [cited by applicant]
US 7742841B2 · Sakai et al. · 2010 [cited by applicant]
US 7783403B2 · Breed · 2010 [cited by applicant]
US 7844595B2 · Canright · 2010 [cited by applicant]
US 8041111B1 · Wilensky · 2011 [cited by applicant]
US 8064643B2 · Stein · 2011 [cited by applicant]
US 8082101B2 · Stein · 2011 [cited by applicant]
US 8164628B2 · Stein · 2012 [cited by applicant]
US 8175376B2 · Marchesotti · 2012 [cited by applicant]
US 8271871B2 · Marchesotti · 2012 [cited by applicant]
US 8346480B2 · Trepagnier et al. · 2013 [cited by applicant]
US 8378851B2 · Stein · 2013 [cited by applicant]
US 8392117B2 · Dolgov · 2013 [cited by applicant]
US 8401292B2 · Park · 2013 [cited by applicant]
US 8412449B2 · Trepagnier · 2013 [cited by applicant]
US 8478072B2 · Aisaka · 2013 [cited by applicant]
US 8532870B2 · Hoetzer et al. · 2013 [cited by applicant]
US 8553088B2 · Stein · 2013 [cited by applicant]
US 8706394B2 · Trepagnier et al. · 2014 [cited by applicant]
US 8718861B1 · Montemerlo et al. · 2014 [cited by applicant]
US 8788134B1 · Litkouhi · 2014 [cited by applicant]
US 8908041B2 · Stein · 2014 [cited by applicant]
US 8917169B2 · Schofield · 2014 [cited by applicant]
US 8963913B2 · Baek · 2015 [cited by applicant]
US 8965621B1 · Urmson · 2015 [cited by applicant]
US 8981966B2 · Stein · 2015 [cited by applicant]
US 8983708B2 · Choe et al. · 2015 [cited by applicant]
US 8993951B2 · Schofield · 2015 [cited by applicant]
US 9002632B1 · Emigh · 2015 [cited by applicant]
US 9008369B2 · Schofield · 2015 [cited by applicant]
US 9025880B2 · Perazzi · 2015 [cited by applicant]
US 9042648B2 · Wang · 2015 [cited by applicant]
US 9081385B1 · Ferguson et al. · 2015 [cited by applicant]
US 9088744B2 · Grauer et al. · 2015 [cited by applicant]
US 9111444B2 · Kaganovich · 2015 [cited by applicant]
US 9117133B2 · Barnes · 2015 [cited by applicant]
US 9118816B2 · Stein · 2015 [cited by applicant]
US 9120485B1 · Dolgov · 2015 [cited by applicant]
US 9122954B2 · Srebnik · 2015 [cited by applicant]
US 9134402B2 · Sebastian · 2015 [cited by applicant]
US 9145116B2 · Clarke · 2015 [cited by applicant]
US 9147255B1 · Zhang · 2015 [cited by applicant]
US 9156473B2 · Clarke · 2015 [cited by applicant]
US 9176006B2 · Stein · 2015 [cited by applicant]
US 9179072B2 · Stein · 2015 [cited by applicant]
US 9183447B1 · Gdalyahu · 2015 [cited by applicant]
US 9185360B2 · Stein · 2015 [cited by applicant]
US 9191634B2 · Schofield · 2015 [cited by applicant]
US 9214084B2 · Grauer et al. · 2015 [cited by applicant]
US 9219873B2 · Grauer et al. · 2015 [cited by applicant]
US 9233659B2 · Rosenbaum · 2016 [cited by applicant]
US 9233688B2 · Clarke · 2016 [cited by applicant]
US 9248832B2 · Huberman · 2016 [cited by applicant]
US 9248835B2 · Tanzmeister · 2016 [cited by applicant]
US 9251708B2 · Rosenbaum · 2016 [cited by applicant]
US 9277132B2 · Berberian · 2016 [cited by applicant]
US 9280711B2 · Stein · 2016 [cited by applicant]
US 9282144B2 · Tebay et al. · 2016 [cited by applicant]
US 9286522B2 · Stein · 2016 [cited by applicant]
US 9297641B2 · Stein · 2016 [cited by applicant]
US 9299004B2 · Lin · 2016 [cited by applicant]
US 9315192B1 · Zhu · 2016 [cited by applicant]
US 9317033B2 · Ibanez-Guzman et al. · 2016 [cited by applicant]
US 9317776B1 · Honda · 2016 [cited by applicant]
US 9330334B2 · Lin · 2016 [cited by applicant]
US 9342074B2 · Dolgov · 2016 [cited by applicant]
US 9347779B1 · Lynch · 2016 [cited by applicant]
US 9355635B2 · Gao · 2016 [cited by applicant]
US 9365214B2 · Ben Shalom · 2016 [cited by applicant]
US 9399397B2 · Mizutani · 2016 [cited by applicant]
US 9418549B2 · Kang et al. · 2016 [cited by applicant]
US 9428192B2 · Schofield · 2016 [cited by applicant]
US 9436880B2 · Bos · 2016 [cited by applicant]
US 9438878B2 · Niebla · 2016 [cited by applicant]
US 9443163B2 · Springer · 2016 [cited by applicant]
US 9446765B2 · Ben Shalom · 2016 [cited by applicant]
US 9459515B2 · Stein · 2016 [cited by applicant]
US 9466006B2 · Duan · 2016 [cited by applicant]
US 9476970B1 · Fairfield · 2016 [cited by applicant]
US 9483839B1 · Kwon · 2016 [cited by applicant]
US 9490064B2 · Hirosawa · 2016 [cited by applicant]
US 9494935B2 · Okumura et al. · 2016 [cited by applicant]
US 9507346B1 · Levinson et al. · 2016 [cited by applicant]
US 9513634B2 · Pack et al. · 2016 [cited by applicant]
US 9531966B2 · Stein · 2016 [cited by applicant]
US 9535423B1 · Debreczeni · 2017 [cited by applicant]
US 9538113B2 · Grauer et al. · 2017 [cited by applicant]
US 9547985B2 · Tuukkanen · 2017 [cited by applicant]
US 9549158B2 · Grauer et al. · 2017 [cited by applicant]
US 9555803B2 · Pawlicki · 2017 [cited by applicant]
US 9568915B1 · Berntorp · 2017 [cited by applicant]
US 9587952B1 · Slusar · 2017 [cited by applicant]
US 9599712B2 · Van Der Tempel et al. · 2017 [cited by applicant]
US 9600889B2 · Boisson et al. · 2017 [cited by applicant]
US 9602807B2 · Crane et al. · 2017 [cited by applicant]
US 9612123B1 · Levinson et al. · 2017 [cited by applicant]
US 9620010B2 · Grauer et al. · 2017 [cited by applicant]
US 9625569B2 · Lange · 2017 [cited by applicant]
US 9628565B2 · Stenneth et al. · 2017 [cited by applicant]
US 9649999B1 · Amireddy et al. · 2017 [cited by applicant]
US 9652860B1 · Maali · 2017 [cited by applicant]
US 9669827B1 · Ferguson et al. · 2017 [cited by applicant]
US 9672446B1 · Vallesi-Gonzalez · 2017 [cited by applicant]
US 9690290B2 · Prokhorov · 2017 [cited by applicant]
US 9701023B2 · Zhang et al. · 2017 [cited by applicant]
US 9712754B2 · Grauer et al. · 2017 [cited by applicant]
US 9720418B2 · Stenneth · 2017 [cited by applicant]
US 9723097B2 · Harris · 2017 [cited by applicant]
US 9723099B2 · Chen · 2017 [cited by applicant]
US 9723233B2 · Grauer et al. · 2017 [cited by applicant]
US 9726754B2 · Massanell et al. · 2017 [cited by applicant]
US 9729860B2 · Cohen et al. · 2017 [cited by applicant]
US 9738280B2 · Rayes · 2017 [cited by applicant]
US 9739609B1 · Lewis · 2017 [cited by applicant]
US 9746550B2 · Nath · 2017 [cited by applicant]
US 9753128B2 · Schweizer et al. · 2017 [cited by applicant]
US 9753141B2 · Grauer et al. · 2017 [cited by applicant]
US 9754490B2 · Kentley et al. · 2017 [cited by applicant]
US 9760837B1 · Nowozin et al. · 2017 [cited by applicant]
US 9766625B2 · Boroditsky et al. · 2017 [cited by applicant]
US 9769456B2 · You et al. · 2017 [cited by applicant]
US 9773155B2 · Shotton et al. · 2017 [cited by applicant]
US 9779276B2 · Todeschini et al. · 2017 [cited by applicant]
US 9785149B2 · Wang et al. · 2017 [cited by applicant]
US 9805294B2 · Liu et al. · 2017 [cited by applicant]
US 9810785B2 · Grauer et al. · 2017 [cited by applicant]
US 9823339B2 · Cohen · 2017 [cited by applicant]
US 9953236B1 · Huang · 2018 [cited by applicant]
US 10147193B2 · Huang · 2018 [cited by applicant]
US 10223806B1 · Yi et al. · 2019 [cited by applicant]
US 10223807B1 · Yi et al. · 2019 [cited by applicant]
US 10410055B2 · Wang et al. · 2019 [cited by applicant]
US 20030114980A1 · Klausner et al. · 2003 [cited by applicant]
US 20030174773A1 · Comaniciu · 2003 [cited by applicant]
US 20040264763A1 · Mas et al. · 2004 [cited by applicant]
US 20070067077A1 · Liu et al. · 2007 [cited by applicant]
US 20070183661A1 · El-Maleh · 2007 [cited by applicant]
US 20070183662A1 · Wang · 2007 [cited by applicant]
US 20070230792A1 · Shashua · 2007 [cited by applicant]
US 20070286526A1 · Abousleman · 2007 [cited by applicant]
US 20080249667A1 · Horvitz · 2008 [cited by applicant]
US 20090040054A1 · Wang · 2009 [cited by applicant]
US 20090087029A1 · Coleman · 2009 [cited by applicant]
US 20100049397A1 · Lin · 2010 [cited by applicant]
US 20100111417A1 · Ward · 2010 [cited by applicant]
US 20100226564A1 · Marchesotti · 2010 [cited by applicant]
US 20100281361A1 · Marchesotti · 2010 [cited by applicant]
US 20110022282A1 · Wu et al. · 2011 [cited by applicant]
US 20110142283A1 · Huang · 2011 [cited by applicant]
US 20110206282A1 · Aisaka · 2011 [cited by applicant]
US 20110247031A1 · Jacoby · 2011 [cited by applicant]
US 20110257860A1 · Getman et al. · 2011 [cited by applicant]
US 20120041636A1 · Johnson et al. · 2012 [cited by applicant]
US 20120105639A1 · Stein · 2012 [cited by applicant]
US 20120114181A1 · Borthwick et al. · 2012 [cited by applicant]
US 20120140076A1 · Rosenbaum · 2012 [cited by applicant]
US 20120274629A1 · Baek · 2012 [cited by applicant]
US 20120314070A1 · Zhang et al. · 2012 [cited by applicant]
US 20130051613A1 · Bobbitt et al. · 2013 [cited by applicant]
US 20130083959A1 · Owechko · 2013 [cited by applicant]
US 20130179038A1 · Goswami et al. · 2013 [cited by applicant]
US 20130182134A1 · Grundmann et al. · 2013 [cited by applicant]
US 20130204465A1 · Phillips et al. · 2013 [cited by applicant]
US 20130266187A1 · Bulan · 2013 [cited by applicant]
US 20130329052A1 · Chew · 2013 [cited by applicant]
US 20140072170A1 · Zhang · 2014 [cited by applicant]
US 20140104051A1 · Breed · 2014 [cited by applicant]
US 20140142799A1 · Ferguson et al. · 2014 [cited by applicant]
US 20140143839A1 · Ricci · 2014 [cited by applicant]
US 20140145516A1 · Hirosawa · 2014 [cited by applicant]
US 20140198184A1 · Stein · 2014 [cited by applicant]
US 20140316865A1 · Okamoto · 2014 [cited by applicant]
US 20140321704A1 · Partis · 2014 [cited by applicant]
US 20140334668A1 · Saund · 2014 [cited by applicant]
US 20150062304A1 · Stein · 2015 [cited by applicant]
US 20150269438A1 · Samarsekera et al. · 2015 [cited by applicant]
US 20150269845A1 · Calmettes et al. · 2015 [cited by applicant]
US 20150310370A1 · Burry · 2015 [cited by applicant]
US 20150353082A1 · Lee et al. · 2015 [cited by applicant]
US 20160008988A1 · Kennedy · 2016 [cited by applicant]
US 20160026787A1 · Nairn et al. · 2016 [cited by applicant]
US 20160037064A1 · Stein · 2016 [cited by applicant]
US 20160094774A1 · Li · 2016 [cited by applicant]
US 20160118080A1 · Chen · 2016 [cited by applicant]
US 20160129907A1 · Kim · 2016 [cited by applicant]
US 20160165157A1 · Stein · 2016 [cited by applicant]
US 20160210528A1 · Duan · 2016 [cited by applicant]
US 20160275766A1 · Venetianer et al. · 2016 [cited by applicant]
US 20160280261A1 · Kyrtsos et al. · 2016 [cited by applicant]
US 20160321381A1 · English · 2016 [cited by applicant]
US 20160334230A1 · Ross et al. · 2016 [cited by applicant]
US 20160342837A1 · Hong et al. · 2016 [cited by applicant]
US 20160347322A1 · Clarke et al. · 2016 [cited by applicant]
US 20160368336A1 · Kahn et al. · 2016 [cited by applicant]
US 20160375907A1 · Erban · 2016 [cited by applicant]
US 20170053169A1 · Cuban et al. · 2017 [cited by applicant]
US 20170061632A1 · Linder et al. · 2017 [cited by applicant]
US 20170080928A1 · Wasiek et al. · 2017 [cited by applicant]
US 20170124476A1 · Levinson et al. · 2017 [cited by applicant]
US 20170134631A1 · Zhao et al. · 2017 [cited by applicant]
US 20170177951A1 · Yang et al. · 2017 [cited by applicant]
US 20170293296A1 · Stenneth · 2017 [cited by examiner]
US 20170301104A1 · Qian · 2017 [cited by applicant]
US 20170305423A1 · Green · 2017 [cited by applicant]
US 20170318407A1 · Meister · 2017 [cited by applicant]
US 20170334484A1 · Koravadi · 2017 [cited by applicant]
US 20180057052A1 · Dodd et al. · 2018 [cited by applicant]
US 20180147900A1 · Shank et al. · 2018 [cited by applicant]
US 20180151063A1 · Pun · 2018 [cited by applicant]
US 20180158197A1 · Dasgupta · 2018 [cited by applicant]
US 20180260956A1 · Huang · 2018 [cited by applicant]
US 20180283892A1 · Behrendt · 2018 [cited by applicant]
US 20180341021A1 · Schmitt et al. · 2018 [cited by applicant]
US 20180373980A1 · Huval · 2018 [cited by applicant]
US 20190025853A1 · Julian · 2019 [cited by applicant]
US 20190065863A1 · Luo et al. · 2019 [cited by applicant]
US 20190066329A1 · Yi et al. · 2019 [cited by applicant]
US 20190066330A1 · Yi et al. · 2019 [cited by applicant]
US 20190066344A1 · Yi et al. · 2019 [cited by applicant]
US 20190084477A1 · Gomez-Mendoza et al. · 2019 [cited by applicant]
US 20190084534A1 · Kasper et al. · 2019 [cited by applicant]
US 20190108384A1 · Wang et al. · 2019 [cited by applicant]
US 20190132391A1 · Thomas · 2019 [cited by applicant]
US 20190132392A1 · Liu · 2019 [cited by applicant]
US 20190170867A1 · Wang et al. · 2019 [cited by applicant]
US 20190210564A1 · Han · 2019 [cited by applicant]
US 20190210613A1 · Sun · 2019 [cited by applicant]
US 20190220037A1 · Vladimerou · 2019 [cited by examiner]
US 20190236950A1 · Li · 2019 [cited by applicant]
US 20190266420A1 · Ge · 2019 [cited by applicant]
US 20210358308A1 · Li et al. · 2021 [cited by applicant]
US 20220270493A1 · Mok · 2022 [cited by applicant]
CN 103604436A · 2014 [cited by applicant]
CN 105547288A · 2016 [cited by applicant]
CN 105809950A · 2016 [cited by examiner]
CN 105915354A · 2016 [cited by applicant]
CN 106340197A · 2017 [cited by applicant]
CN 106781591A · 2017 [cited by applicant]
CN 106853827A · 2017 [cited by applicant]
CN 106853827A1 · 2017 [cited by applicant]
CN 107403547A · 2017 [cited by applicant]
CN 107728156A · 2018 [cited by applicant]
CN 107980102A · 2018 [cited by applicant]
CN 108010360A · 2018 [cited by applicant]
CN 108132471A · 2018 [cited by applicant]
CN 108278981A · 2018 [cited by applicant]
CN 108519604A · 2018 [cited by applicant]
CN 108717182A · 2018 [cited by applicant]
CN 108749923A · 2018 [cited by applicant]
CN 108761479A · 2018 [cited by applicant]
CN 108761481A · 2018 [cited by applicant]
CN 108829107A · 2018 [cited by applicant]
CN 108845570A · 2018 [cited by applicant]
CN 208059845U · 2018 [cited by applicant]
CN 108959173A · 2018 [cited by applicant]
DE 2608513A1 · 1977 [cited by applicant]
DE 102016105259A1 · 2016 [cited by applicant]
DE 102017125662A1 · 2018 [cited by applicant]
EP 890470B1 · 1999 [cited by applicant]
EP 1754179A1 · 2007 [cited by applicant]
EP 2448251A2 · 2012 [cited by applicant]
EP 2463843A2 · 2012 [cited by applicant]
EP 2761249A1 · 2014 [cited by applicant]
EP 2946336A2 · 2015 [cited by applicant]
EP 2993654A1 · 2016 [cited by applicant]
EP 3081419A1 · 2016 [cited by applicant]
EP 3232381A1 · 2017 [cited by applicant]
GB 2513392A · 2014 [cited by applicant]
JP 2001334966A · 2001 [cited by applicant]
JP 2005293350A · 2005 [cited by applicant]
JP 3721911B2 · 2005 [cited by applicant]
JP 2012225806A · 2012 [cited by applicant]
JP 2022515355A · 2022 [cited by applicant]
KR 100802511A1 · 2008 [cited by applicant]
WO 1991009375A1 · 1991 [cited by applicant]
WO 2005098739A1 · 2005 [cited by applicant]
WO 2005098751A1 · 2005 [cited by applicant]
WO 2005098782A1 · 2005 [cited by applicant]
WO 2010109419A · 2010 [cited by applicant]
WO 2013045612A1 · 2013 [cited by applicant]
WO 2014111814A2 · 2014 [cited by applicant]
WO 2014166245A1 · 2014 [cited by applicant]
WO 2014201324A1 · 2014 [cited by applicant]
WO 2015083009A1 · 2015 [cited by applicant]
WO 2015103159A1 · 2015 [cited by applicant]
WO 2015125022A2 · 2015 [cited by applicant]
WO 2015186002A2 · 2015 [cited by applicant]
WO 2016090282A1 · 2016 [cited by applicant]
WO 2016135736A2 · 2016 [cited by applicant]
WO 2017079349A1 · 2017 [cited by applicant]
WO 2017079460A2 · 2017 [cited by applicant]
WO 2018039114A1 · 2018 [cited by applicant]
WO 2017013875A1 · 2018 [cited by applicant]
WO 2019040800A1 · 2019 [cited by applicant]
WO 2019084491A1 · 2019 [cited by applicant]
WO 2019084494A1 · 2019 [cited by applicant]
WO 2019140277A2 · 2019 [cited by applicant]
WO 2019168986A1 · 2019 [cited by applicant]
No Author. Japanese Application No. 2021-533666, Office Action Mailed Dec. 6, 2022, pp. 1-6. [cited by applicant]
European Patent Office, Extended European Search Report for EP 19894619, Mailing Date: Jul. 11, 2022, 8 pages. [cited by applicant]
Chinese Application No. 201811530975.X, First Search Results Mailed Nov. 20, 2020, pp. 1-2. [cited by applicant]
Chinese Application No. 201811530975.X, First Office Action Mailed Dec. 21, 2020, pp. 1-7. [cited by applicant]
International Application No. PCT/CN2019/077076, International Search Report and Written Opinion Mailed Aug. 20, 2019, pp. 1-13. [cited by applicant]
International Application No. PCT/CN2019/077076, International Preliminary Report on Patentability Mailed Jun. 8, 2021, pp. 1-4. [cited by applicant]
Carle, Patrick J.F. et al. “Global Rover Localization by Matching LiDAR and Orbital 3D Maps.” IEEE, Anchorage Convention District, pp. 1-6, May 3-8, 2010. (Anchorage Alaska, US). [cited by applicant]
Caselitz, T. et al., “Monocular camera localization in 3D LiDAR maps,” European Conference on Computer Vision (2014) Computer Vision—ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol. 8690. Springer, Cham. [cited by applicant]
Mur-Artal, R. et al., “ORB-SLAM: A Versatile and Accurate Monocular SLAM System,” IEEE Transaction on Robotics, Oct. 2015, pp. 1147-1163, vol. 31, No. 5, Spain. [cited by applicant]
Sattler, T. et al., “Are Large-Scale 3D Models Really Necessary for Accurate Visual Localization?” CVPR, IEEE, 2017, pp. 1-10. [cited by applicant]
Engel, J. et la. “LSD-SLAM: Large Scare Direct Monocular SLAM,” pp. 1-16, Munich. [cited by applicant]
Levinson, Jesse et al., Experimental Robotics, Unsupervised Calibration for Multi-Beam Lasers, pp. 179-194, 12th Ed., Oussama Khatib, Vijay Kumar, Gaurav Sukhatme (Eds.) Springer-Verlag Berlin Heidelberg 2014. [cited by applicant]
International Application No. PCT/US2019/013322, International Search Report and Written Opinion Mailed Apr. 2, 2019. [cited by applicant]
International Application No. PCT/US19/12934, International Search Report and Written Opinion Mailed Apr. 29, 2019. [cited by applicant]
International Application No. PCT/US18/53795, International Search Report and Written Opinion Mailed Dec. 31, 2018. [cited by applicant]
International Application No. PCT/US18/57848, International Search Report and Written Opinion Mailed Jan. 7, 2019. [cited by applicant]
International Application No. PCT/US2018/057851, International Search Report and Written Opinion Mailed Feb. 1, 2019. [cited by applicant]
International Application No. PCT/US2019/019839, International Search Report and Written Opinion Mailed May 23, 2019. [cited by applicant]
International Application No. PCT/US19/25995, International Search Report and Written Opinion Mailed Jul. 9, 2019. [cited by applicant]
Geiger, Andreas et al., “Automatic Camera and Range Sensor Calibration using a single Shot”, Robotics and Automation (ICRA), pp. 1-8, 2012 IEEE International Conference. [cited by applicant]
Zhang, Z. et al. A Flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence (vol. 22, Issue: 11, Nov. 2000). [cited by applicant]
International Application No. PCT/US2018/047830, International Search Report and Written Opinion Mailed Dec. 28, 2018. [cited by applicant]
Bar-Hillel, Aharon et al. “Recent progress in road and lane detection: a survey.” Machine Vision and Applications 25 (2011): 727-745. [cited by applicant]
Schindler, Andreas et al. “Generation of high precision digital maps using circular arc splines,” 2012 IEEE Intelligent Vehicles Symposium, Alcala de Henares, 2012, pp. 246-251. doi: 10.1109/IVS.2012.6232124. [cited by applicant]
International Application No. PCT/US2018/047608, International Search Report and Written Opinion Mailed Dec. 28, 2018. [cited by applicant]
Hou, Xiaodi and Zhang, Liqing, “Saliency Detection: A Spectral Residual Approach”, Computer Vision and Pattern Recognition, CVPR'07—IEEE Conference, pp. 1-8, 2007. [cited by applicant]
Hou, Xiaodi and Harel, Jonathan and Koch, Christof, “Image Signature: Highlighting Sparse Salient Regions”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, No. 1, pp. 194-201, 2012. [cited by applicant]
Hou, Xiaodi and Zhang, Liqing, “Dynamic Visual Attention: Searching For Coding Length Increments”, Advances in Neural Information Processing Systems, vol. 21, pp. 681-688, 2008. [cited by applicant]
Li, Yin and Hou, Xiaodi and Koch, Christof and Rehg, James M. and Yuille, Alan L., “The Secrets of Salient Object Segmentation”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 280-287… [cited by applicant]
Zhou, Bolei and Hou, Xiaodi and Zhang, Liqing, “A Phase Discrepancy Analysis of Object Motion”, Asian Conference on Computer Vision, pp. 225-238, Springer Berlin Heidelberg, 2010. [cited by applicant]
Hou, Xiaodi and Yuille, Alan and Koch, Christof, “Boundary Detection Benchmarking: Beyond F-Measures”, Computer Vision and Pattern Recognition, CVPR'13, vol. 2013, pp. 1-8, IEEE, 2013. [cited by applicant]
Hou, Xiaodi and Zhang, Liqing, “Color Conceptualization”, Proceedings of the 15th ACM International Conference on Multimedia, pp. 265-268, ACM, 2007. [cited by applicant]
Hou, Xiaodi and Zhang, Liqing, “Thumbnail Generation Based on Global Saliency”, Advances in Cognitive Neurodynamics, ICCN 2007, pp. 999-1003, Springer Netherlands, 2008. [cited by applicant]
Hou, Xiaodi and Yuille, Alan and Koch, Christof, “A Meta-Theory of Boundary Detection Benchmarks”, arXiv preprint arXiv:1302.5985, 2013. [cited by applicant]
Li, Yanghao and Wang, Naiyan and Shi, Jianping and Liu, Jiaying and Hou, Xiaodi, “Revisiting Batch Normalization for Practical Domain Adaptation”, arXiv preprint arXiv:1603.04779, 2016. [cited by applicant]
Li, Yanghao and Wang, Naiyan and Liu, Jiaying and Hou, Xiaodi, “Demystifying Neural Style Transfer”, arXiv preprint arXiv:1701.01036, 2017. [cited by applicant]
Hou, Xiaodi and Zhang, Liqing, “A Time-Dependent Model of Information Capacity of Visual Attention”, International Conference on Neural Information Processing, pp. 127-136, Springer Berlin Heidelberg, 2006. [cited by applicant]
Wang, Panqu and Chen, Pengfei and Yuan, Ye and Liu, Ding and Huang, Zehua and Hou, Xiaodi and Cottrell, Garrison, “Understanding Convolution for Semantic Segmentation”, arXiv preprint arXiv:1702.08502, 2017. [cited by applicant]
Li, Yanghao and Wang, Naiyan and Liu, Jiaying and Hou, Xiaodi, “Factorized Bilinear Models for Image Recognition”, arXiv preprint arXiv:1611.05709, 2016. [cited by applicant]
Hou, Xiaodi, “Computational Modeling and Psychophysics in Low and Mid-Level Vision”, California Institute of Technology, 2014. [cited by applicant]
Spinello, Luciano, Triebel, Rudolph, Siegwart, Roland, “Multiclass Multimodal Detection and Tracking in Urban Environments”, Sage Journals, vol. 29 Issue 12, pp. 1498-1515 Article first published online: Oct. 7, 2010; I… [cited by applicant]
Matthew Barth, Carrie Malcolm, Theodore Younglove, and Nicole Hill, “Recent Validation Efforts for a Comprehensive Modal Emissions Model”, Transportation Research Record 1750, Paper No. 01-0326, College of Engineering, … [cited by applicant]
Kyoungho Ahn, Hesham Rakha, “The Effects of Route Choice Decisions on Vehicle Energy Consumption and Emissions”, Virginia Tech Transportation Institute, Blacksburg, VA 24061, date unknown. [cited by applicant]
Ramos, Sebastian, Gehrig, Stefan, Pinggera, Peter, Franke, Uwe, Rother, Carsten, “Detecting Unexpected Obstacles for Self-Driving Cars: Fusing Deep Learning and Geometric Modeling”, arXiv:1612.06573v1 [cs.CV] Dec. 20, 2… [cited by applicant]
Schroff, Florian, Dmitry Kalenichenko, James Philbin, (Google), “FaceNet: A Unified Embedding for Face Recognition and Clustering”, CVPR 2015. [cited by applicant]
Dai, Jifeng, Kaiming He, Jian Sun, (Microsoft Research), “Instance-aware Semantic Segmentation via Multi-task Network Cascades”, CVPR 2016. [cited by applicant]
Huval, Brody, Tao Wang, Sameep Tandon, Jeff Kiske, Will Song, Joel Pazhayampallil, Mykhaylo Andriluka, Pranav Rajpurkar, Toki Migimatsu, Royce Cheng-Yue, Fernando Mujica, Adam Coates, Andrew Y. Ng, “An Empirical Evaluat… [cited by applicant]
Tian Li, “Proposal Free Instance Segmentation Based on Instance-aware Metric”, Department of Computer Science, Cranberry-Lemon University, Pittsburgh, PA., date unknown. [cited by applicant]
Mohammad Norouzi, David J. Fleet, Ruslan Salakhutdinov, “Hamming Distance Metric Learning”, Departments of Computer Science and Statistics, University of Toronto, date unknown. [cited by applicant]
Jain, Suyong Dutt, Grauman, Kristen, “Active Image Segmentation Propagation”, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, Jun. 2016. [cited by applicant]
MacAodha, Oisin, Campbell, Neill D.F., Kautz, Jan, Brostow, Gabriel J., “Hierarchical Subquery Evaluation for Active Learning on a Graph”, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition… [cited by applicant]
Kendall, Alex, Gal, Yarin, “What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision”, arXiv:1703.04977v1 [cs.CV] Mar. 15, 2017. [cited by applicant]
Wei, Junqing, John M. Dolan, Bakhtiar Litkhouhi, “A Prediction- and Cost Function-Based Algorithm for Robust Autonomous Freeway Driving”, 2010 IEEE Intelligent Vehicles Symposium, University of California, San Diego, CA… [cited by applicant]
Peter Welinder, Steve Branson, Serge Belongie, Pietro Perona, “The Multidimensional Wisdom of Crowds”; http://www.vision.caltech.edu/visipedia/papers/WelinderEtaINIPS10.pdf, 2010. [cited by applicant]
Kai Yu, Yang Zhou, Da Li, Zhang Zhang, Kaiqi Huang, “Large-scale Distributed Video Parsing and Evaluation Platform”, Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy … [cited by applicant]
P. Guarneri, G. Rocca and M. Gobbi, “A Neural-Network-Based Model for the Dynamic Simulation of the Tire/Suspension System While Traversing Road Irregularities,” in IEEE Transactions on Neural Networks, vol. 19, No. 9, … [cited by applicant]
C. Yang, Z. Li, R. Cui and B. Xu, “Neural Network-Based Motion Control of an Underactuated Wheeled Inverted Pendulum Model,” in IEEE Transactions on Neural Networks and Learning Systems, vol. 25, No. 11, pp. 2004-2016, … [cited by applicant]
Stephan R. Richter, Vibhav Vineet, Stefan Roth, Vladlen Koltun, “Playing for Data: Ground Truth from Computer Games”, Intel Labs, European Conference on Computer Vision (ECCV), Amsterdam, the Netherlands, 2016. [cited by applicant]
Thanos Athanasiadis, Phivos Mylonas, Yannis Avrithis, and Stefanos Kollias, “Semantic Image Segmentation and Object Labeling”, IEEE Transactions on Circuits and Systems for Video Technology, vol. 17, No. 3, Mar. 2007. [cited by applicant]
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele, “The Cityscapes Dataset for Semantic Urban Scene Understanding”, Proceedings of… [cited by applicant]
Adhiraj Somani, Nan Ye, David Hsu, and Wee Sun Lee, “DESPOT: Online POMDP Planning with Regularization”, Department of Computer Science, National University of Singapore, date unknown. [cited by applicant]
Adam Paszke, Abhishek Chaurasia, Sangpil Kim, and Eugenio Culurciello. Enet: A deep neural network architecture for real-time semantic segmentation. CoRR, abs/1606.02147, 2016. [cited by applicant]
Szeliski, Richard, “Computer Vision: Algorithms and Applications” http://szeliski.org/Book/, 2010. [cited by applicant]
Office Action Mailed in Chinese Application No. 201810025516.X, Mailed Sep. 3, 2019. [cited by applicant]
Luo, Yi et al. U.S. Appl. No. 15/684,389 Notice of Allowance Mailed Oct. 9, 2019. [cited by applicant]
International Application No. PCT/US19/58863, International Search Report and Written Opinion mailed Feb. 14, 2020, pp. 1-11. [cited by applicant]
US Patent & Trademark Office, Non-Final Office Action mailed Apr. 29, 2020 in U.S. Appl. No. 16/174,980, 6 pages. [cited by applicant]
US Patent & Trademark Office, Final Office Action mailed Sep. 8, 2020 in U.S. Appl. No. 16/174,980, 8 pages. [cited by applicant]
International Application No. PCT/CN2019/077075, International Search Report and Written Opinion Mailed Sep. 10, 2019, pp. 1-12. [cited by applicant]
International Application No. PCT/CN2019/077075, International Preliminary Report on Patentability Mailed Jun. 8, 2021, pp. 1-4. [cited by applicant]
Office Action in Chinese Patent Application No. 201811505593.1 dated Sep. 29, 2021. [cited by applicant]
Nyberg, Patrik, “Stabilization, Sensor Fusion and Path Following for Autonomous Reversing of a Full-Scale Truck and Trailer System,” Master of Science in Electrical Engineering, Department of Electrical Engineering, Lin… [cited by applicant]
European Patent Office, Extended European search report for EP 19896541, Mailing Date: Jul. 8, 2022, 10 pages. [cited by applicant]
Chinese Patent Office, Second Office Action for CN 201811505593.1, Mailing Date: Jul. 5, 2022, 33 pages with English translation. [cited by applicant]
Chinese Patent Office, Third Office Action for CN 201811505593.1, Mailing Date: Nov. 3, 2022, 10 pages with English translation. [cited by applicant]
Japanese Patent Office, 1st Examination Report for JP 2021-533593, Mailing Date: Feb. 22, 2023, 6 pages with English translation. [cited by applicant]
European Patent Office, Communication pursuant to Article 94(3) EPC for EP Appl. No. 19896541.0, mailed on Feb. 19, 2024, 6 pages. [cited by applicant]
Japanese Patent Office, Notice of Refusal for JP 2023-129340, Mailing Date: Apr. 19, 2024, 4 pages with English translation. [cited by applicant]
U.S. Patent & Trademark Office, Non-Final Office Action mailed Apr. 10, 2024, in U.S. Appl. No. 18/360,706, 9 pages. [cited by applicant]